Black Swan

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pages: 651 words: 180,162

Antifragile: Things That Gain From Disorder by Nassim Nicholas Taleb

Air France Flight 447, Andrei Shleifer, banking crisis, Benoit Mandelbrot, Berlin Wall, Black Swan, business cycle, Chuck Templeton: OpenTable:, commoditize, creative destruction, credit crunch, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, discrete time, double entry bookkeeping, Emanuel Derman, epigenetics, financial independence, Flash crash, Gary Taubes, George Santayana, Gini coefficient, Henri Poincaré, high net worth, hygiene hypothesis, Ignaz Semmelweis: hand washing, informal economy, invention of the wheel, invisible hand, Isaac Newton, James Hargreaves, Jane Jacobs, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Arrow, knowledge economy, Lao Tzu, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, Marc Andreessen, meta analysis, meta-analysis, microbiome, money market fund, moral hazard, mouse model, Myron Scholes, Norbert Wiener, pattern recognition, Paul Samuelson, placebo effect, Ponzi scheme, principal–agent problem, purchasing power parity, quantitative trading / quantitative finance, Ralph Nader, random walk, Ray Kurzweil, rent control, Republic of Letters, Ronald Reagan, Rory Sutherland, selection bias, Silicon Valley, six sigma, spinning jenny, statistical model, Steve Jobs, Steven Pinker, Stewart Brand, stochastic process, stochastic volatility, Thales and the olive presses, Thales of Miletus, The Great Moderation, the new new thing, The Wealth of Nations by Adam Smith, Thomas Bayes, Thomas Malthus, too big to fail, transaction costs, urban planning, Vilfredo Pareto, Yogi Berra, Zipf's Law

., 1988, The Collapse of Complex Societies: New Studies in Archaeology. Cambridge: Cambridge University Press. Taleb, N. N., and M. Blyth, 2011, “The Black Swan of Cairo.” Foreign Affairs 90(3). Taleb, N. N., and A. Pilpel, 2007, “Epistemology and Risk Management.” Risk and Regulation 13, Summer. Taleb, N. N., and C. Tapiero, 2010, “The Risk Externalities of Too Big to Fail.” Physica A: Statistical Physics and Applications. Taleb, N. N., D. G. Goldstein, and M. Spitznagel, 2009, “The Six Mistakes Executives Make in Risk Management,” Harvard Business Review (October). Taleb, N. N., 2008, “Infinite Variance and the Problems of Practice.” Complexity 14(2). Taleb, N. N., 2009, “Errors, Robustness, and the Fourth Quadrant.” International Journal of Forecasting 25. Taleb, N. N., 2011, “The Future Has Thicker Tails than the Past: Model Error as Branching Counterfactuals.”

BY NASSIM NICHOLAS TALEB INCERTO, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision making when we don’t understand the world, expressed in the form of a personal essay with autobiographical sections, stories, parables, and philosophical, historical, and scientific discussions in nonoverlapping volumes that can be accessed in any order. ANTIFRAGILE (THIS VOLUME) THE BLACK SWAN (2007, 2010), on how high-impact but rare events dominate history, how we retrospectively give ourselves the illusion of understanding them thanks to narratives, how they are impossible to estimate scientifically, how this makes some areas—but not others—totally unpredictable and unforecastable, how confirmatory methods of knowledge don’t work, and how thanks to Black Swan–blind “faux experts” we are prone to building systems increasingly fragile to extreme events.

THE BED OF PROCRUSTES (PHILOSOPHICAL APHORISMS) (2010) INCERTO’S TECHNICAL COMPANION (freely available electronic volume) consisting of academic-style papers, miscellaneous notes, and (very) technical remarks. ABOUT THE AUTHOR NASSIM NICHOLAS TALEB has devoted his life to problems of uncertainty, probability, and knowledge and has led three careers around this focus, as a businessman-trader, a philosophical essayist, and an academic researcher. Although he now spends most of his time either working in intense seclusion in his study, or as a flâneur meditating in cafés across the planet, he is currently Distinguished Professor of Risk Engineering at New York University’s Polytechnic Institute. His main subject matter is “decision making under opacity,” that is, a map and a protocol on how we should live in a world we don’t understand. His books Fooled by Randomness and The Black Swan have been published in thirty-three languages. Taleb believes that prizes, honorary degrees, awards, and ceremonialism debase knowledge by turning it into a spectator sport.


pages: 306 words: 82,765

Skin in the Game: Hidden Asymmetries in Daily Life by Nassim Nicholas Taleb

availability heuristic, Benoit Mandelbrot, Bernie Madoff, Black Swan, Brownian motion, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cellular automata, Claude Shannon: information theory, cognitive dissonance, complexity theory, David Graeber, disintermediation, Donald Trump, Edward Thorp, equity premium, financial independence, information asymmetry, invisible hand, knowledge economy, loss aversion, mandelbrot fractal, mental accounting, microbiome, moral hazard, Murray Gell-Mann, offshore financial centre, p-value, Paul Samuelson, Ponzi scheme, price mechanism, principal–agent problem, Ralph Nader, random walk, rent-seeking, Richard Feynman, Richard Thaler, Ronald Coase, Ronald Reagan, Rory Sutherland, Silicon Valley, Steven Pinker, stochastic process, survivorship bias, The Nature of the Firm, transaction costs, urban planning, Yogi Berra

The Things We Do and Why We Do Them. Palgrave Macmillan. Sandis, Constantine, and Nassim Nicholas Taleb, 2015. “Leadership Ethics and Asymmetry.” In Leadership and Ethics, ed. Boaks and Levine, 233. London: Bloomsbury. Stiglitz, J. E., 1988. “Principal and Agent.” In The New Palgrave Dictionary of Economics, vol. 3. London: Macmillan. Taleb, N. N., 2007. “Black Swans and the Domains of Statistics.” The American Statistician 61(3): 198–200. Taleb, N. N., and P. Cirillo, 2015. “On the Shadow Moments of Apparently Infinite-Mean Phenomena,” arXiv preprint arXiv:1510.06731. Taleb, N. N., and R. Douady, 2015. “On the Super-Additivity and Estimation Biases of Quantile Contributions.” Physica A: Statistical Mechanics and Its Applications 429: 252–260. Taleb, N. N., and C. Sandis, 2013. “The Skin in the Game Heuristic for Protection Against Tail Events.”

Just as Eve came out of Adam’s ribs, so does each book of the Incerto emerge from the penultimate one’s ribs. The Black Swan was an occasional discussion in Fooled by Randomness; the concept of convexity to random events, the theme of Antifragile, was adumbrated in The Black Swan; and, finally, Skin in the Game was a segment of Antifragile under the banner: Thou shalt not become antifragile at the expense of others. Simply, asymmetry in risk bearing leads to imbalances and, potentially, to systemic ruin. What I call the Bob Rubin trade connects to my business as a trader (as we saw, when these people make money, they keep the profits; when they lose, someone else bears the costs while they do their Black Swan invocation). Its manifestations are so ubiquitous that it has been the backbone of every book of the Incerto.

FOOLED BY RANDOMNESS (2001, 2004), on how we tend to mistake luck for skills, how randomness does not look random, why there is no point talking about performance when it is easier to buy and sell than fry an egg, and the profound difference between dentists and speculators. THE BLACK SWAN (2007, 2010), on how high-impact but rare events dominate history, how we retrospectively give ourselves the illusion of understanding them thanks to narratives, how they are impossible to estimate scientifically, how this makes some areas—but not others—totally unpredictable and unforecastable, how confirmatory methods of knowledge don’t work, and how thanks to Black Swan–blind “faux experts” we are prone to building systems increasingly fragile to extreme events. THE BED OF PROCRUSTES (Philosophical Aphorisms) (2010, 2016) ANTIFRAGILE (2012), on how some things like disorder (hence volatility, time, chaos, variability, and stressors) while others don’t, how we can classify things along the lines fragile-robust-antifragile, how we can identify (anti)fragility based on nonlinear response without having to know much about the history of the process (which solves most of the Black Swan problem), and why you are alive if and only if you love (some) volatility.


pages: 317 words: 100,414

Superforecasting: The Art and Science of Prediction by Philip Tetlock, Dan Gardner

Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, availability heuristic, Black Swan, butterfly effect, buy and hold, cloud computing, cuban missile crisis, Daniel Kahneman / Amos Tversky, desegregation, drone strike, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, index fund, Jane Jacobs, Jeff Bezos, Kenneth Arrow, Laplace demon, longitudinal study, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, Nelson Mandela, obamacare, pattern recognition, performance metric, Pierre-Simon Laplace, place-making, placebo effect, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, scientific worldview, Silicon Valley, Skype, statistical model, stem cell, Steve Ballmer, Steve Jobs, Steven Pinker, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!

Indeed, he suspects this whole research program is misguided. Enter the Black Swan Nassim Taleb is a former Wall Street trader whose thinking about uncertainty and probability has produced three enormously influential books and turned “black swan” into a common English phrase. For those unfamiliar with the concept, imagine you are a European living four centuries ago. You have seen many swans in your life to date. All were white. If you were asked to envision all the possible swans you might ever encounter, you would probably imagine lots and lots of swans that vary in size and shape, but all would be white, because your experience has taught you all swans are white. But then a ship returns from Australia. On board is a swan—a black swan. You are stunned. The “black swan” is therefore a brilliant metaphor for an event so far outside experience we can’t even imagine it until it happens.

The “black swan” is therefore a brilliant metaphor for an event so far outside experience we can’t even imagine it until it happens. But Taleb isn’t interested only in surprise. A black swan must be impactful. Indeed, Taleb insists that black swans, and black swans alone, determine the course of history. “History and societies do not crawl,” he wrote. “They make jumps.”4 The implication for my efforts to improve foresight are devastating: IARPA has commissioned a fool’s errand. What matters can’t be forecast and what can be forecast doesn’t matter. Believing otherwise lulls us into a false sense of security. In this view, I have taken a scientific step backward. In my EPJ research, I got it roughly right with the punch line about experts and the dart-throwing chimp. But my Good Judgment Project is premised on misconceptions, panders to desperation, and fosters foolish complacency. I respect Taleb. He and I have even cowritten a paper on a key area where we agree.

I think his critique makes deep points that future tournaments will have to struggle to address. But I see another false dichotomy rearing its head: “forecasting is feasible if you follow my formula” versus “forecasting is bunk.” Dispelling the dichotomy requires putting the beguiling black swan metaphor under the analytic microscope. What exactly is a black swan? The stringent definition is something literally inconceivable before it happens. Taleb has implied as much on occasion. If so, many events dubbed black swans are actually gray. Consider the 9/11 terrorist attacks, the prototypic black swan in which one dazzling sunny morning in September, a bolt from the blue changed history. But 9/11 was not unimaginable. In 1994 a plot to hijack a jet and crash it into the Eiffel Tower was broken up. In 1998 the US Federal Aviation Administration examined a scenario in which terrorists hijacked FedEx cargo planes and crashed them into the World Trade Center.


pages: 338 words: 106,936

The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall

Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Asian financial crisis, bank run, beat the dealer, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, business cycle, butterfly effect, buy and hold, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, collateralized debt obligation, collective bargaining, dark matter, Edward Lorenz: Chaos theory, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, George Akerlof, Gerolamo Cardano, Henri Poincaré, invisible hand, Isaac Newton, iterative process, John Nash: game theory, Kenneth Rogoff, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, martingale, Myron Scholes, new economy, Paul Lévy, Paul Samuelson, prediction markets, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Coase, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical arbitrage, statistical model, stochastic process, The Chicago School, The Myth of the Rational Market, tulip mania, Vilfredo Pareto, volatility smile

In general, one should expect studies of psychology and human behavior to be symbiotic with mathematical approaches to economics. A second kind of criticism — one that has already come up in the book — has found its biggest champion in Nassim Taleb. Taleb has written an influential book, The Black Swan, which argues that markets are far too wild to be tamed by physicists. A black swan, you’ll recall, is an event that is so unprecedented it is simply impossible to predict. Black swans, Taleb argues, are what really matter — and yet they are precisely what our best mathematical models are unable to anticipate. This is a particular problem for financial modeling, Taleb says. He argues in his book and in many articles that physics lives in a world he calls “Mediocristan,” whereas finance lives in “Extremistan.” The difference is that randomness in Mediocristan is well behaved and can be described by normal distributions.

The distribution of cities in France is fat-tailed, in that the very biggest cities are much bigger than the next biggest cities. But if you plot the size of French cities by their population size, as Zipf’s law would have you do, Paris is still much too big. It breaks the mold. Taleb’s argument trades on the fact that black swans can have enormous consequences. Dragon kings are similar in their influence. They are tyrannical when they appear. But unlike black swans, you can hear them coming. Sornette does not argue that all black swans are really dragon kings in disguise, or even that all market crashes are predictable. But he does argue that many things that might seem like black swans really do issue warnings. In many cases, these warnings take the form of log-periodic precursors, oscillations in some form of data that occur only when the system is in the special state where a massive catastrophe can occur.

The three physicists who first introduced the notion of self-organization, Per Bak, Chao Tang, and Kurt Wiesenfeld, took their discovery as evidence that extreme events are, in principle, indistinguishable from more moderate events. The moral, they thought, was that predicting such events was a hopeless endeavor. This concern is at the heart of hedge fund manager Nassim Taleb’s argument against modeling in finance. In his book The Black Swan, Taleb explains that some events — he calls them “black swans” — are so far from standard, normal distribution expectations that you cannot even make sense of questions about their likelihood. They are essentially unpredictable, and yet when they occur, they change everything. Taleb takes it to be a consequence of Mandelbrot’s arguments that these kinds of extreme events, the events with the most dramatic consequences, occur much more frequently than any model can account for. To trust a mathematical model in a wildly random system like a financial market is foolish, then, because the models exclude the most important phenomena: the catastrophic crashes.


pages: 299 words: 92,782

The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing by Michael J. Mauboussin

Amazon Mechanical Turk, Atul Gawande, Benoit Mandelbrot, Black Swan, Checklist Manifesto, Clayton Christensen, cognitive bias, commoditize, Daniel Kahneman / Amos Tversky, David Brooks, deliberate practice, disruptive innovation, Emanuel Derman, fundamental attribution error, Gini coefficient, hindsight bias, hiring and firing, income inequality, Innovator's Dilemma, Long Term Capital Management, loss aversion, Menlo Park, mental accounting, moral hazard, Network effects, prisoner's dilemma, random walk, Richard Thaler, risk-adjusted returns, shareholder value, Simon Singh, six sigma, Steven Pinker, transaction costs, winner-take-all economy, zero-sum game, Zipf's Law

New York: Free Press, 2005. Syed, Matthew. Bounce: Mozart, Federer, Picasso, Beckham, and the Science of Success. New York: Harper, 2010. Taleb, Nassim Nicholas. Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. 2nd ed. New York: ThomsonTexere, 2004. Taleb, Nassim Nicholas. The Black Swan: The Impact of the Highly Improbable, Second Edition. New York: Random House, 2010. Taleb, Nassim Nicholas. The Bed of Procrustes: Philosophical and Practical Aphorisms. New York: Random House, 2010. Taleb, Nassim Nicholas. “Antifragility, Robustness, and Fragility Inside the ‘Black Swan’ Domain.” SSRN working paper, February 2011. Taleb, Nassim Nicholas, and Mark Blyth. “The Black Swan of Cairo: How Suppressing Volatility Makes the World Less Predictable and More Dangerous.” Foreign Affairs 90, no. 3 (May/June 2011): 33–39.

Gimpel, Donald P. Green, and Daron R. Shaw, “How Large and Long-lasting Are the Persuasive Effects of Televised Campaign Ads? Results from a Randomized Field Experiment,” American Political Science Review 105, no. 1 (February 2011): 135–150. 21. Nassim Nicholas Taleb, The Bed of Procrustes: Philosophical and Practical Aphorisms (New York: Random House, 2010). 22. Nassim Nicholas Taleb, “Antifragility, Robustness, and Fragility Inside the ‘Black Swan’ Domain,” SSRN working paper, February 2011. 23. Nassim Nicholas Taleb and Mark Blyth, “The Black Swan of Cairo: How Suppressing Volatility Makes the World Less Predictable and More Dangerous,” Foreign Affairs 90, no. 3 (May/June 2011): 33–39; Emanuel Derman distinguishes between models and theories, “Models are analogies; they always describe one thing relative to something else.

The columns of the matrix are the payoffs, and distinguish between the simple and the complex. Binary payoffs are simple: the team wins or loses; the coin comes up heads or tails. Again, modeling these payoffs mathematically is relatively straightforward. Complex payoffs would include the casualties from a war. You may be able to predict a war, but there's no reliable way to measure its effect. Figure 1-2 summarizes the matrix. FIGURE 1-2 Taleb's four quadrants Source: Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2010), 365. Statistical methods tend to work well in quadrants one through three, and most of what we will be dealing with falls into one of those quadrants. Dealing with quadrant four is far more difficult, and there is a natural and frequently disastrous tendency to apply naively the methods of the first three quadrants to the last.


pages: 57 words: 11,522

The Bed of Procrustes: Philosophical and Practical Aphorisms by Nassim Nicholas Taleb

Benoit Mandelbrot, Black Swan, commoditize, knowledge worker, Republic of Letters

ALSO BY NASSIM NICHOLAS TALEB Fooled by Randomness The Black Swan Copyright © 2010 by Nassim Nicholas Taleb All rights reserved. Published in the United States by Random House, an imprint of The Random House Publishing Group, a division of Random House, Inc., New York. RANDOM HOUSE and colophon are registered trademarks of Random House, Inc. Library of Congress Cataloging-in-Publication Data Taleb, Nassim. The bed of Procrustes: philosophical and practical aphorisms / by Nassim Nicholas Taleb. p. cm. eISBN: 978-0-679-64368-5 1. Aphorisms and apothegms. 2. Human behavior— Quotations, maxims, etc. I. Title. PN6271.T35 2011 818′.602—dc22 2010036866 www.atrandom.com v3.1 To ALEXANDER N. TALEB CONTENTS Cover Other Books by This Author Title Page Copyright Dedication Procrustes PRELUDES COUNTER NARRATIVES MATTERS ONTOLOGICAL THE SACRED AND THE PROFANE CHANCE, SUCCESS, HAPPINESS, AND STOICISM CHARMING AND LESS CHARMING SUCKER PROBLEMS THESEUS, OR LIVING THE PALEO LIFE THE REPUBLIC OF LETTERS THE UNIVERSAL AND THE PARTICULAR FOOLED BY RANDOMNESS AESTHETICS ETHICS ROBUSTNESS AND FRAGILITY THE LUDIC FALLACY AND DOMAIN DEPENDENCE EPISTEMOLOGY AND SUBTRACTIVE KNOWLEDGE THE SCANDAL OF PREDICTION BEING A PHILOSOPHER AND MANAGING TO REMAIN ONE ECONOMIC LIFE AND OTHER VERY VULGAR SUBJECTS THE SAGE, THE WEAK, AND THE MAGNIFICENT THE IMPLICIT AND THE EXPLICIT ON THE VARIETIES OF LOVE AND NONLOVE THE END Postface Acknowledgments About the Author PROCRUSTES Procrustes, in Greek mythology, was the cruel owner of a small estate in Corydalus in Attica, on the way between Athens and Eleusis, where the mystery rites were performed.

Dupire, Y. Zilber, S. Roberts, A. Pilpel, W. Goodlad, W. Murphy, M. Brockman, J. Brockman, C. Taleb, C. Sandis, J. Kujat, T. Burnham, R. Dobelli, M. Ghosn (the younger), S. Taleb, D. Riviere, J. Gray, M. Carreira, M.-C. Riachi, P. Bevelin, J. Audi (pontem fecit), S. Roberts, B. Flyvberg, E. Boujaoude, P. Boghossian, S. Riley, G. Origgi, S. Ammons, and many more (I sometimes remember names of critically helpful people when it is too late to show gratitude). ABOUT THE AUTHOR NASSIM NICHOLAS TALEB spends most of his time as a flâneur, meditating in cafés across the planet. A former trader, he is currently Distinguished Professor at New York University. He is the author of Fooled by Randomness and The Black Swan, which has spent more than a year on the New York Times bestseller list and has become an intellectual, social, and cultural touchstone.

– The costs of specialization: architects build to impress other architects; models are thin to impress other models; academics write to impress other academics; filmmakers try to impress other filmmakers; painters impress art dealers; but authors who write to impress book editors tend to fail. – It is a waste of emotions to answer critics; better to stay in print long after they are dead. – I can predict when an author is about to plagiarize me, and poorly so when he writes that Taleb “popularized” the theory of Black Swan events.* – Newspaper readers exposed to real prose are like deaf persons at a Puccini opera: they may like a thing or two while wondering, “what’s the point?” – Some books cannot be summarized (real literature, poetry); some can be compressed to about ten pages; the majority to zero pages. – The exponential information age is like a verbally incontinent person: he talks more and more as fewer and fewer people listen


pages: 374 words: 114,600

The Quants by Scott Patterson

Albert Einstein, asset allocation, automated trading system, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, job automation, John Meriwether, John Nash: game theory, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Sergey Aleynikov, short selling, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

Prices can gyrate wildly over short periods of time—wildly enough to cause massive, potentially crippling losses to investors who’ve made large, leveraged wagers. As Nassim Nicholas Taleb, a critic of quant models, later argued in several books, investors who believe the market moves according to a random walk are “fooled by randomness” (the title of one of his books). Taleb famously dubbed the wild unexpected swings in markets, and in life itself, “black swans,” evoking the belief long held in the West that all swans are white, a notion exploded when sailors discovered black swans in Australia. Taleb argued that there are far more black swans in the world than many people believe, and that models based on historical trends and expectations of a random walk are bound to lead their users to destruction. Mandelbrot’s theories were shelved by the financial engineers who didn’t want to deal with the messy, chaotic world they evoked.

But one evening a colleague: “Going Under, Happily,” by Pete Muller as told to Loch Adamson, New York Times, June 8, 2003. In May 2002, he attended the wedding: The wedding account is based on interviews with Nassim Taleb, John Liew, and Neil Chriss. His peripatetic life had shown him: The brief account of Taleb’s life is based on numerous interviews with Taleb and his longtime trading partner Mark Spitznagel, as well as the articles “Blowing Up: How Nassim Taleb Turned the Inevitability of Disaster into an Investment Strategy,” by Malcolm Gladwell, New Yorker, April 22 and 29, 2002, and “Flight of the Black Swan,” by Stephanie Baker-Said, Bloomberg Markets, May 2008. Once or twice a month: The subjects of this book did not discuss this poker game often. A number of details were learned from people familiar with the game.

In the end, the Truth according to Renaissance wasn’t about whether the market was efficient or in equilibrium. The Truth was very simple, and remorseless as the driving force of any cutthroat Wall Street banker: Did you make money, or not? Nothing else mattered. Meanwhile, a fund with ties to Nassim Taleb, Universa Investments, was also hitting on all cylinders. Funds run by Universa, managed and owned by Taleb’s longtime collaborator Mark Spitznagel, gained as much as 150 percent in 2008 on its bet that the market is far more volatile than most quant models predict. The fund’s Black Swan Protocol Protection plan purchased far-out-of-the-money put options on stocks and stock indexes, which paid off in spades after Lehman collapsed as the market tanked. By mid-2009, Universa had $6 billion under management, up sharply from the $300 million it started out with in January 2007, and was placing a new bet that hyperinflation would take off as a result of all the cash unleashed by the government and Fed flooding into the economy.


pages: 256 words: 60,620

Think Twice: Harnessing the Power of Counterintuition by Michael J. Mauboussin

affirmative action, asset allocation, Atul Gawande, availability heuristic, Benoit Mandelbrot, Bernie Madoff, Black Swan, butter production in bangladesh, Cass Sunstein, choice architecture, Clayton Christensen, cognitive dissonance, collateralized debt obligation, Daniel Kahneman / Amos Tversky, deliberate practice, disruptive innovation, Edward Thorp, experimental economics, financial innovation, framing effect, fundamental attribution error, Geoffrey West, Santa Fe Institute, George Akerlof, hindsight bias, hiring and firing, information asymmetry, libertarian paternalism, Long Term Capital Management, loose coupling, loss aversion, mandelbrot fractal, Menlo Park, meta analysis, meta-analysis, money market fund, Murray Gell-Mann, Netflix Prize, pattern recognition, Philip Mirowski, placebo effect, Ponzi scheme, prediction markets, presumed consent, Richard Thaler, Robert Shiller, Robert Shiller, statistical model, Steven Pinker, The Wisdom of Crowds, ultimatum game

City sizes have a much wider range of outcomes than human heights do.8 Nassim Taleb, an author and former derivatives trader, calls the extreme outcomes within power law distributions black swans. He defines a black swan as an outlier event that has a consequential impact and that humans seek to explain after the fact.9 In large part owing to Taleb’s efforts, more people are aware of black swans and distributions that deviate from the bell curve. What most people still don’t appreciate is the mechanism that propagates black swans. Here’s where critical points and phase transitions come in. Positive feedback leads to outcomes that are outliers. And critical points help explain our perpetual surprise at black swan events because we have a hard time understanding how such small incremental perturbations can lead to such large outcomes.

Each realm has critical points and phase transitions. In cases where cause and effect are not clear, learning from history is a challenge. Here are some tips on how to cope with systems that have phase transitions: 1. Study the distribution of outcomes for the system you are dealing with. Thanks to Taleb’s prodding, many people now associate extreme events with black swans. But Taleb makes a careful, if overlooked, distinction: if we understand what the broader distribution looks like, the outcomes—however extreme—are correctly labeled as gray swans, not black swans. He calls them “modelable extreme events.” In fact, scientists have done a lot of work classifying the distributions of various systems, including the stock market, terrorist acts, and power-grid failures.25 So if you have the background and tools to understand these systems, you can get a general view of how the system behaves, even if you have no reliable means of predicting any specific event.

Coulsen, “Issues of Expert Flexibility in Contexts Characterized by Complexity and Change,” in Expertise in Context: Human and Machine, ed. Paul J. Feltovich, Kenneth M. Ford, and Robert R. Hoffman (Menlo Park, CA, and Cambridge, MA: AAAI Press and MIT Press, 1997), 125–146. Taleb, The Black Swan, discusses a similar concept he calls the “ludic fallacy.” 15. Donald MacKenzie, An Engine, Not a Camera: How Financial Models Shape Markets (Cambridge: MIT Press, 2006). 16. Benoit Mandelbrot, “The Variation of Certain Speculative Prices,” in The Random Character of Stock Market Prices, ed. Paul H. Cootner, (Cambridge: MIT Press, 1964), 369–412. This is also a core theme of Taleb, The Black Swan. See also Benoit Mandelbrot and Richard L. Hudson, The (Mis)Behavior of Markets (New York: Basic Books, 2004). 17. Paul H. Cootner, “Comments on The Variation of Certain Speculative Prices,” in Cootner, The Random Character of Stock Market Prices, 413–418. 18.


pages: 161 words: 51,919

What's Your Future Worth?: Using Present Value to Make Better Decisions by Peter Neuwirth

backtesting, big-box store, Black Swan, collective bargaining, discounted cash flows, en.wikipedia.org, Long Term Capital Management, Rubik’s Cube, Skype, the scientific method

., 103 discounted cash flow method, 117–118 inadequate for organizations, 128 discount rate(s), 11 definition of, 51–57 example of, 26 Don’t Work Forever (Vernon), 158 E econometric model, 89–90, 91 education, investing in, 45–49 expanding funnel of doubt, 80–83, 98 F FCC, 84–85, 91 financial crisis of 2008–9, 155 financial engineers, 93 financial planning, 157–160 5-step process, 7–13 in day-to-day decisions, 24–37 discount rate and, 11 evaluating impacts of what might happen, 9 non-attachment to a specific scenario and, 9 only calculation in, 8 fooled by randomness, 83–96 Fooled by Randomness (Taleb), 87–88 foregone investment, 118, 119 Foundation Trilogy (Asimov), 66 Frohlich, Rob, 130–138 future. See also “Black Swan”; Taleb, Nassim Nicholas and Big Data, 66 considering all possible, 5 doesn’t really exist, 2–3 imagining the, 65–76 impossibility of predicting the, 95–96, 118 nature of the, 66–70 non-attachment to any particular, 69 and objectivity evaluating likelihood of scenarios, 69 predicting the, 3 thinking about the, 68–70 uncertainty about the, 39, 67 unlikely scenarios, 125–126 G General Equilibrium Model, 91 H hedge funds, 94 Heinlein, Robert, 65 hot steaks, 78 I induction, principle of, 90. See also “Black Swan”; fooled by randomness; Taleb, Nassim Nicholas information age, 1 Internet, 92 Ippolitto, Dean, 99 K Kahneman, Daniel, 10, 77 L Leiber, Fritz, 65–66 licensed actuary, 3.

Among other things, David’s most important work on the appropriate way of funding a pension plan (“Objectives and methods for funding defined benefit pension schemes,” Journal of the Institute of Actuaries, September 1987: 155–225) was never, in my opinion given the credit it deserves, and to this day very few actuaries have heard of the DABM (Defined Accrued Benefit Method). 23. See en.wikipedia.org/wiki/Bell_System 24. See en.wikipedia.org/wiki/Econometric_model 25. Nassim N. Taleb, Fooled by Randomness, Random House (Trade Paperback Edition), 2005. 26. Ibid. pp. 113–115. 27. Ibid. pp. 116–131. 28. Ibid. pp. 126–127. 29. See en.wikipedia.org/wiki/General_equilibrium_theory 30. See FCC Record, Volume 07, No. 09, p. 2724, April 20–May 1, 1992. 31. Nassim N. Taleb, The Black Swan (Random House [Trade Paperback edition], 2010). 32. From 1/1/1982 to 1/1/2000 the S&P 500 rose from 122.55 to 1469.25, a return of almost 15%/year 33. Taleb, Fooled by Randomness, 113–115. 34. There were countless postmortems after the LTCM collapse. See for example: Philippe Jorion, “Risk Management Lessons from Long-Term Capital Management,” European Financial Management 6 (September 2000): 277–300.

In fact, managers at many companies—particularly if they have an MBA—would say that almost all important decisions a company makes utilize Present Value, only they call it the “discounted cash flow method.”39 It’s true that, superficially, the mechanics of the discounted cash flow method and Present Value thinking are quite similar, but in fact there are some subtle—but very important—differences. For one thing, the discounted cash flow method generally focuses on just the “high likelihood” scenarios, while in Present Value thinking we try to imagine all the possibilities, recognizing that low likelihood/high impact possibilities can be very important. Nassim Taleb calls these possibilities “Black Swan” events and suggests that such “impossible to predict” scenarios are the ones that ultimately change our lives in the most important ways.40 While I agree with Taleb that these scenarios are impossible to predict, I don’t think they are impossible to imagine. That is why step 2 is so critical to Present Value thinking, a step that is usually given little attention in discounted cash flow analysis. In addition to the above, discounted cash flow models typically only consider financial/measurable factors, while Present Value takes into account non-financial items.


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Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Nicholas Taleb

Antoine Gombaud: Chevalier de Méré, availability heuristic, backtesting, Benoit Mandelbrot, Black Swan, commoditize, complexity theory, corporate governance, corporate raider, currency peg, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, endowment effect, equity premium, fixed income, global village, hedonic treadmill, hindsight bias, Kenneth Arrow, Long Term Capital Management, loss aversion, mandelbrot fractal, mental accounting, meta analysis, meta-analysis, Myron Scholes, Paul Samuelson, quantitative trading / quantitative finance, QWERTY keyboard, random walk, Richard Feynman, road to serfdom, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, Steven Pinker, stochastic process, survivorship bias, too big to fail, Turing test, Yogi Berra

A correspondence with a reader who was hit with a black swan, the unexpected large-impact random event (the loss of a baby) caused me to spend some time dipping into the literature on adaptation after a severe random event (not coincidentally also dominated by Daniel Kahneman, the pioneer of the ideas on irrational behavior under uncertainty). I have to confess that I never felt really particularly directly of service to anyone being a trader (except myself); it felt elevating and useful being an essayist. All or None A few confusions with the message in this book. Just as our brain does not easily make out probabilistic shades (it goes for the oversimplifying “all-or-none”), it was hard to explain that the idea here was that “it is more random than we think” rather than “it is all random.” I had to face the “Taleb, as a skeptic, thinks everything is random and successful people are just lucky.”

In addition to his scientific and literary interests, Taleb’s hobby is to poke fun at those who take themselves and the quality of their knowledge too seriously. His work has been published in twenty-seven languages (even French) and has more than a million readers. He lives mostly in New York. Also by Nassim Nicholas Taleb The Black Swan Footnotes To return to the corresponding text, click on the reference number or "Return to text." Chapter 7 *What I call empiricism does not simply mean “just look at reality”: it implies the rigorous avoidance of hasty generalizations outside what you saw, your “empiri-cism.” This covers the relation between the past and the future (the past might not be a representative sample of the future, but it also concerns other generalizations we take for granted, in medicine, politics, and science). Return to text.

Because the randomness content compounds its effects. Nowhere is the problem of induction more relevant than in the world of trading—and nowhere has it been as ignored! Cygnus Atratus In his Treatise on Human Nature, the Scots philosopher David Hume posed the issue in the following way (as rephrased in the now famous black swan problem by John Stuart Mill): No amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion. Hume had been irked by the fact that science in his day (the eighteenth century) had experienced a swing from scholasticism, entirely based on deductive reasoning (no emphasis on the obsdervation of the real world) to, owing to Francis Bacon, an overreaction into naive and unstructured empiricism.


pages: 310 words: 82,592

Never Split the Difference: Negotiating as if Your Life Depended on It by Chris Voss, Tahl Raz

banking crisis, Black Swan, clean water, cognitive bias, Daniel Kahneman / Amos Tversky, Donald Trump, framing effect, friendly fire, iterative process, loss aversion, market fundamentalism, price anchoring, telemarketer, ultimatum game, uranium enrichment

Griffin crumpled to the floor, dead. Black Swan theory tells us that things happen that were previously thought to be impossible—or never thought of at all. This is not the same as saying that sometimes things happen against one-in-a-million odds, but rather that things never imagined do come to pass. The idea of the Black Swan was popularized by risk analyst Nassim Nicholas Taleb in his bestselling books Fooled by Randomness (2001)1 and The Black Swan (2007),2 but the term goes back much further. Until the seventeenth century, people could only imagine white swans because all swans ever seen had possessed white feathers. In seventeenth-century London it was common to refer to impossible things as “Black Swans.” But then the Dutch explorer Willem de Vlamingh went to western Australia in 1697—and saw a black swan. Suddenly the unthinkable and unthought was real.

Suddenly the unthinkable and unthought was real. People had always predicted that the next swan they saw would be white, but the discovery of black swans shattered this worldview. Black Swans are just a metaphor, of course. Think of Pearl Harbor, the rise of the Internet, 9/11, and the recent banking crisis. None of the events above was predicted—yet on reflection, the markers were all there. It’s just that people weren’t paying attention. As Taleb uses the term, the Black Swan symbolizes the uselessness of predictions based on previous experience. Black Swans are events or pieces of knowledge that sit outside our regular expectations and therefore cannot be predicted. This is a crucial concept in negotiation. In every negotiating session, there are different kinds of information. There are those things we know, like our counterpart’s name and their offer and our experiences from other negotiations.

., 233 Beaudoin, Charlie, 24 Behavioral Change Stairway Model (BCSM), 97 behavioral economics, 11 behavior change BCSM and, 97 health issues and, 97 lessons that lay the foundation for, 112 psychological environment necessary for, 97–98 “that’s right” and, 98, 101–5, 107 “you’re right” as ineffective, 105–7 behind the table or Level II players, 171–72, 186 pronoun usage and, 179, 187 questions to identify, 256 bending reality, 126–35. See also prospect theory key lessons of, 138–39 Bergen, Peter, 232 Black Swan, The (Taleb), 215 Black Swan Group, The, 3, 21, 191, 220 complementary PDF form, bargaining types, 198 website and more information, 258 Black Swans, 19, 21, 213–45 ascertaining counterpart’s unattained goals, 231 asking questions to reveal, 110 “crazy” vs. a clue, 232–33, 245 example, Griffin hostage case, 213–14, 216–17, 235, 244 example, MBA student uncovers seller’s constraints, 238–41 example, Watson standoff, Washington DC, 224–28 getting face time to unearth hidden factors, 236–37 key lessons of, 244–45 knowing a counterpart’s “religion” and, 225, 228–29, 244 as leverage multipliers, 220–24, 244 listening and uncovering, 228, 244–45 mistaking acting on bad information for craziness, 233–34 mistaking constrained for acting crazy, 234–35 mistaking having other interests for acting crazy, 235 observing unguarded moments to unearth hidden factors, 237 Taleb’s use of term, 216 theory of, 215 tips for reading religion correctly, 228 uncovering unknown unknowns, 218–20 what they are, 238 Blum, Gabriella, 2–4, 5 body language.


pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, business cycle, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, coherent worldview, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Laplace demon, Long Term Capital Management, loss aversion, medical malpractice, meta analysis, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, social intelligence, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

If our prediction does not somehow help to bring about larger results, then it is of little interest or value to us. Once again, we care about things that matter, yet it is precisely these larger, more significant predictions about the future that pose the greatest difficulties. BLACK SWANS AND OTHER “EVENTS” Nowhere is this problem of predicting the things that matter more acute than for what former derivatives trader and gadfly of the financial industry Nassim Taleb calls black swans, meaning events that—like the invention of the printing press, the storming of the Bastille, and the attacks on the World Trade Center—happen rarely but carry great import when they do.15 But what makes an event a black swan? This is where matters get confusing. We tend to speak about events as if they are separate and distinct, and can be assigned a level of importance in the way that we describe natural events such as earthquakes, avalanches, and storms by their magnitude or size.

Conversely, the “evidential” view is that a probability should be interpreted only as the odds one ought to accept for a particular gamble, regardless of whether it is repeated or not. 14. See de Mesquita (2009) for details. 15. As Taleb explains, the term “black swan” derives from the European settlement of Australia: Until the settlers witnessed black swans in what is now Western Australia, conventional wisdom held that all swans must be white. 16. For details of the entire sequence of events surrounding the Bastille, see Sewell (1996, pp. 871–78). It is worth noting, moreover, that other historians of the French Revolution draw the boundaries rather differently from Sewell. 17. Taleb makes a similar point—namely that to have predicted the invention of what we now call the Internet, one would have to have known an awful lot about the applications to which the Internet was put after it had been invented. As Taleb puts it, “to understand the future to the point of being able to predict it, you need to incorporate elements from this future itself.

Earthquakes, by contrast, along with avalanches, storms, and forest fires, display “heavy-tailed” distributions, meaning that most are relatively small and draw little attention, whereas a small number can be extremely large. It’s tempting to think that historical events also follow a heavy-tailed distribution, where Taleb’s black swans lie far out in the tail of the distribution. But as the sociologist William Sewell explains, historical events are not merely “bigger” than others in the sense that some hurricanes are bigger than others. Rather, “events” in the historical sense acquire their significance via the transformations they trigger in wider social arrangements. To illustrate, Sewell revisits the storming of the Bastille on July 14, 1789, an event that certainly seems to satisfy Taleb’s definition of a black swan. Yet as Sewell points out, the event was not just the series of actions that happened in Paris on July 14, but rather encompassed the whole period between July 14 and July 23, during which Louis XVI struggled to control the insurrection in Paris, while the National Assembly at Versailles debated whether to condemn the violence or to embrace it as an expression of the people’s will.


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Radical Uncertainty: Decision-Making for an Unknowable Future by Mervyn King, John Kay

"Robert Solow", Airbus A320, Albert Einstein, Albert Michelson, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Arthur Eddington, autonomous vehicles, availability heuristic, banking crisis, Barry Marshall: ulcers, battle of ideas, Benoit Mandelbrot, bitcoin, Black Swan, Bonfire of the Vanities, Brownian motion, business cycle, business process, capital asset pricing model, central bank independence, collapse of Lehman Brothers, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, Donald Trump, easy for humans, difficult for computers, Edmond Halley, Edward Lloyd's coffeehouse, Edward Thorp, Elon Musk, Ethereum, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, fear of failure, feminist movement, financial deregulation, George Akerlof, germ theory of disease, Hans Rosling, Ignaz Semmelweis: hand washing, income per capita, incomplete markets, inflation targeting, information asymmetry, invention of the wheel, invisible hand, Jeff Bezos, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Snow's cholera map, John von Neumann, Kenneth Arrow, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, market bubble, market fundamentalism, Moneyball by Michael Lewis explains big data, Nash equilibrium, Nate Silver, new economy, Nick Leeson, Northern Rock, oil shock, Paul Samuelson, peak oil, Peter Thiel, Philip Mirowski, Pierre-Simon Laplace, popular electronics, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, railway mania, RAND corporation, rent-seeking, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Coase, sealed-bid auction, shareholder value, Silicon Valley, Simon Kuznets, Socratic dialogue, South Sea Bubble, spectrum auction, Steve Ballmer, Steve Jobs, Steve Wozniak, Tacoma Narrows Bridge, Thales and the olive presses, Thales of Miletus, The Chicago School, the map is not the territory, The Market for Lemons, The Nature of the Firm, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Thomas Malthus, Toyota Production System, transaction costs, ultimatum game, urban planning, value at risk, World Values Survey, Yom Kippur War, zero-sum game

., ‘Revealed: Final Cost of Edinburgh Tram Scheme Will be £1 Billion’, The Scotsman (13 Dec 2017) Taft, J. G., ‘Why Knight Capital Was Saved and Lehman Brothers Failed’, Forbes (20 Aug 2012) < https://www.forbes.com/sites/advisor/2012/08/20/why-knight-capital-was-saved-and-lehman-brothers-failed/ > (accessed 14 Jan 2019) Taleb, N. N., Antifragile: Things that Gain from Disorder (London: Penguin, 2013) Taleb, N. N., The Black Swan: The Impact of the Highly Improbable (London: Penguin, 2008) Taleb, N. N., Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets (London: Penguin, 2007) Taleb, N. N., Skin in the Game: Hidden Asymmetries in Daily Life (London: Allen Lane, 2018) Tetlock, P. E., Expert Political Judgement: How Good Is It? How Can We Know? (Princeton: PUP, 2005) Tetlock, P. E. and Gardner, D., Superforecasting: The Art and Science of Prediction (London: Random House, 2016) Thaler, R.

It is not just that we do not know what will happen. We often do not even know the kinds of things that might happen. When we describe radical uncertainty we are not talking about ‘long tails’ – imaginable and well-defined events whose low probability can be estimated, such as a long losing streak at roulette. And we are not only talking about the ‘black swans’ identified by Nassim Nicholas Taleb – surprising events which no one could have anticipated until they happen, although these ‘black swans’ are examples of radical uncertainty. 19 We are emphasising the vast range of possibilities that lie in between the world of unlikely events which can nevertheless be described with the aid of probability distributions, and the world of the unimaginable. This is a world of uncertain futures and unpredictable consequences, about which there is necessary speculation and inevitable disagreement – disagreement which often will never be resolved.

Ultimately, he found easier and more profitable applications of his skills on Wall Street. 6 Regulators of securities markets restrict the activities of traders with superior information for superficially different but substantively similar reasons. Unknown unknowns At the opposite pole of uncertainty from true randomness are the genuinely unknown unknowns. Taleb’s metaphor of the ‘black swan’ describes the unknown unknowns of business and finance, which are no less important than those of aviation. The origin of the metaphor is that Europeans believed all swans to be white – as all European swans are – until the colonists of Australia observed black swans. A century ago, a telephone that would fit in your pocket, take photographs, calculate the square root of a number, navigate to an unknown destination, and on which you could read any of a million novels, was not improbable; it was just not within the scope of imagination or bounds of possibility.


pages: 576 words: 105,655

Austerity: The History of a Dangerous Idea by Mark Blyth

"Robert Solow", accounting loophole / creative accounting, balance sheet recession, bank run, banking crisis, Black Swan, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, collateralized debt obligation, correlation does not imply causation, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, deindustrialization, disintermediation, diversification, en.wikipedia.org, ending welfare as we know it, Eugene Fama: efficient market hypothesis, eurozone crisis, financial repression, fixed income, floating exchange rates, Fractional reserve banking, full employment, German hyperinflation, Gini coefficient, global reserve currency, Growth in a Time of Debt, Hyman Minsky, income inequality, information asymmetry, interest rate swap, invisible hand, Irish property bubble, Joseph Schumpeter, Kenneth Rogoff, liberal capitalism, liquidationism / Banker’s doctrine / the Treasury view, Long Term Capital Management, market bubble, market clearing, Martin Wolf, money market fund, moral hazard, mortgage debt, mortgage tax deduction, Occupy movement, offshore financial centre, paradox of thrift, Philip Mirowski, price stability, quantitative easing, rent-seeking, reserve currency, road to serfdom, savings glut, short selling, structural adjustment programs, The Great Moderation, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, Tobin tax, too big to fail, unorthodox policies, value at risk, Washington Consensus, zero-sum game

Your VaR number, once calculated, would reflect this. Nassim Taleb never bought into this line of thinking. He had been a critic of VaR models as far back as 1997, arguing that they systematically underestimated the probability of high-impact, low-probability events. He argued that the thin tails of the Gaussian worked for height but not for finance, where the tails were “fat.” The probabilities associated with fat tails do not get exponentially smaller, so outlier events are much more frequent than your model allows you to imagine. This is why ten-sigma events actually happen nine years apart. Taleb’s 2006 book The Black Swan, published before the crisis, turned these criticisms of VaR into a full-blown attack on the way banks and governments think about risk. Taleb essentially asked the question, what would happen if you ran into an eight-and-a-half-foot-tall person having sampled 10,000 people who were shorter?

Want to benefit from the market going up? Use options (the right to buy or sell an asset at a predetermined price) to increase leverage (amplify the bet) while taking a short position as cover. But if this is all it takes to be safe, and to perhaps even make money, why did the banks not see the crisis coming? To answer that question, you need to turn to the trader-turned-philosopher Nassim Nicolas Taleb. Taleb’s Black Swans and Fat-Tailed Worlds A common refrain when the crisis first hit was that no one could have seen it coming. It was the financial equivalent of the meteor that wiped out the dinosaurs. All the diversification and hedging strategies that were supposed to keep banks from blowing up were, as David Viniar, the chief financial officer of Goldman Sachs put it, blindsided by “25 standard deviation moves, several days in a row.”24 This is similar to the “ten sigma event” claim reportedly made by John Meriweather when his hedge fund, Long Term Capital Management (LTCM), blew up in 1998.25 What these sigmas refer to is the number of standard deviations from the mean of a probability distribution at which an outcome will, probabilistically speaking, occur, with each higher sigma (number) being increasingly less likely than the last.

Meeting her would be a ten-sigma event. Taleb would bet against you, and you would lose, because in finance there is no way of knowing that you will not run into the equivalent of an eight-and-a-half foot-tall person. Key here is the issue of observational experience. If you haven’t been around for a third of the life of the universe (ten sigma), then how can you know what is possible over that time period? It’s the assumed distribution that tells you what is possible, not your experience. To return to the height example, just because your model estimates that an eight-foot-tall person does not exist, it doesn’t follow that she doesn’t actually exist and that you will not run into her. In Taleb’s example, all swans were white until Europeans went to Australia and found black swans. Their exhaustive, multiyear, multisite sample of all known swans had convinced Europeans that all swans were white—until they were not.


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Strong Towns: A Bottom-Up Revolution to Rebuild American Prosperity by Charles L. Marohn, Jr.

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, A Pattern Language, American Society of Civil Engineers: Report Card, bank run, big-box store, Black Swan, Bretton Woods, British Empire, business cycle, call centre, cognitive dissonance, complexity theory, corporate governance, Detroit bankruptcy, Donald Trump, en.wikipedia.org, facts on the ground, Ferguson, Missouri, global reserve currency, housing crisis, index fund, Jane Jacobs, Jeff Bezos, low skilled workers, mass immigration, mortgage debt, Network effects, new economy, New Urbanism, paradox of thrift, Paul Samuelson, pensions crisis, Ponzi scheme, quantitative easing, reserve currency, the built environment, The Death and Life of Great American Cities, trickle-down economics, Upton Sinclair, urban planning, urban renewal, walkable city, white flight, women in the workforce, yield curve, zero-sum game

Author of The Black Swan, Nassim Taleb, in a 2013 speech at Loyola College titled “How to Live in a World We Don’t Understand,” explained how humans have reacted to abundance – to a lack of constraints – by exercising more control over their environment, repeatedly solving the immediate problem at the expense of our overall stability. With the enlightenment, the industrial revolution, came a greater control over our environment. So what did we get? The smoothness. We remove everything. We smooth out the economic cycles. No more up and down. It’s okay until it blows up. You want to smooth out the forest fires? It’s okay, but without fires, the forest blows up. You want to make people comfortable? Okay, but their bone density goes down.5 This is a central theme for both Taleb and Diamond, the two modern authors who have most influenced my thinking on these subjects.

Wall Street trader turned author and philosopher Nassim Taleb suggests that there are confident ways to live in a world you don’t fully understand. They begin with acknowledging the limits of our capacity to predict the future. From his book The Black Swan: If you know all possible conditions of a physical system you can, in theory, project its behavior into the future. But this only concerns inanimate objects. It is another matter to project a future when humans are involved, if you consider them living beings and endowed with free will. If you believe in free will you can’t truly believe in social science and economic projection. You cannot predict how people will act.7 Prediction is a central feature of modern public policy, particularly for cities. Yet, with complex systems, prediction provides false comfort. Again, from Taleb: Artificial, man-made mechanical and engineering contraptions with simple responses are complicated, but not “complex,” as they don’t have interdependencies.

Notes 1 https://www.peakprosperity.com/ 2 James Howard Kunstler, The Long Emergency (New York: Grove/Atlantic, 2005). 3 Steve Mouzon, The Original Green: Unlocking the Mystery of True Sustainability (New Urban Guild Foundation, 2010). 4 https://www.strongtowns.org/journal/2014/8/25/stroad-nation.html. 5 Alan Ehrenhalt, The Great Inversion and the Future of the American City (New York: Vintage Books, 2012). 6 https://www.brookings.edu/testimonies/the-changing-geography-of-us- poverty/. 7 Nassim Nicholas Taleb, The Black Swan (New York: Random House, 2007). 8 Nassim Nicholas Taleb, Antifragile (New York: Random House, 2012). 9 https://www.nytimes.com/2011/06/05/opinion/05friedman.html. 7 Productive Places A few blocks from my home there is a restaurant with several historic photos hanging on the wall. I’ve seen them many times but had never stopped to look closely at them, until one day I was waiting for a table and my eyes lingered on a photo of a circus parade passing through a historic downtown.


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Investing Demystified: How to Invest Without Speculation and Sleepless Nights by Lars Kroijer

Andrei Shleifer, asset allocation, asset-backed security, Bernie Madoff, bitcoin, Black Swan, BRICs, Carmen Reinhart, cleantech, compound rate of return, credit crunch, diversification, diversified portfolio, equity premium, estate planning, fixed income, high net worth, implied volatility, index fund, intangible asset, invisible hand, Kenneth Rogoff, market bubble, money market fund, passive investing, pattern recognition, prediction markets, risk tolerance, risk/return, Robert Shiller, Robert Shiller, selection bias, sovereign wealth fund, too big to fail, transaction costs, Vanguard fund, yield curve, zero-coupon bond

Make sure it is worth it. 1 www.telegraph.co.uk/finance/personalfinance/pensions/ 9407283/Fees-that-canhalve-the-value-of-your-pension.html 2 For UK specific thoughts on financial planning and pensions I recommend Jonquil Lowe’s Be Your Own Financial Adviser (Pearson Education, 2010). 3 I used a couple including one from University of Pennsylvania: wharton.upenn.edu/mortality/perl/CalcForm chapter 16 * * * Apocalypse investing Not long before the financial crash of 2008 a book called The Black Swan – The Impact of the Highly Improbable (Penguin, 2008) written by Nassim Nicholas Taleb was published. The book caused quite a stir in the financial community. The title of the book refers to the common assumption that swans are white. Swans had always been white and it had almost become part of the definition of being a swan, that it is a beautiful, graceful, white bird. The swan-watching community (if there is such a thing) was aghast and confused when a black swan appeared out of nowhere. All that it took for granted was thrown to the wind if such a fundamental assumption as the swan’s colour could be shattered in an instant. Taleb goes on to make a mockery of common parameters of risk used in finance. He describes how if you assume that the annual standard deviation of the S&P 500 was 15%, a drop of 45% would represent a 3 standard deviation move and without skew or fat tails (i.e. big moves are more likely than suggested by a normal distribution, as discussed earlier) this should happen approximately 0.14% of the time, or about every 700 years, when in reality it seems to happen every couple of decades.

What an investor should have is a thorough understanding of the risks in the market. This should involve very generic things such as ‘how likely are you to lose 25% of your investment?’ with thorough explanations of how this likelihood can change. There should be more in-depth sections with more technical or mathematical calculations for those who want it, along with discussions of the Black Swan theory of Taleb’s books (see Chapter 16). I don’t think there is enough of this kind of information available for investors today. This risk section could be tailored by incorporating information given by the investor. The more information you as the investor are willing to share, the more detailed analysis you would get back. This could include pension age, other assets, potential liabilities, tax, etc. presented in a fairly generic way, but with the possibility of getting very sophisticated and more detailed.

Index accountants active managers compared with index trackers, 2nd performance over time active personal portfolio management adding up the costs of advisory charges age life stages of financial planning and risk profile AIG allocations see investment allocations alternative investments alternative weightings ‘angel’ investing, 2nd annuities IRR (internal rate of return), 2nd apocalypse investing avoiding fraud and financial disasters and gold as security assets asset classes to avoid concentration risk customisation and noninvestment growth of, and overpayment of fees and institutional investors intangible and liabilities and portfolio theory in the rational portfolio, assets split tangible see also minimal risk assets avoiding fraud banks bailouts cash deposits with and financial disaster and gold as security and property investment Barclays US High Yield index Bernstein, William The Intelligent Asset Allocator bid/offer spread black swan events, 2nd, 3rd Bogle, John bonds bond indices, 2nd dollar domination of ETFs, 2nd and financial planning income from coupon payments indices and the rational portfolio adding other bonds to risk preferences, 2nd rebalancing your portfolio ‘risky’ bonds and liquidity shortterm, 2nd see also corporate bonds; government bonds; minimal risk assets Brazil government bonds broad-based portfolios and liquidity world equities, 2nd, 3rd, 4th, 5th Buffett, Warren, 2nd fee structure capital gains tax (CGT), 2nd, 3rd and gifts car insurance Case-Shiller House Price index, 2nd cash deposits deposit insurance government guarantees risk of CGT see capital gains tax (CGT) civil unrest collectibles commercial property market commodities, 2nd returns form company shares comparison sites, 2nd enhanced independent Contagion (film) corporate bonds adding to minimal risk assets, 2nd, 3rd and financial planning and credit quality ETFs, 2nd and financial planning liquidity of and minimal risk assets and portfolio theory, 2nd and the rational portfolio, 2nd, 3rd adjusting allocations, 2nd risk preferences real return expectations world corporate debt yields, 2nd costs see fees and expenses CRB Commodity index CRB Total Return index, 2nd credit ratings government bonds, 2nd, 3rd, 4th adding to minimal risk assets currency and government bonds, 2nd, 3rd, 4th matching and world equities currency-hedged investment products custody charges customisation Cyprus defined benefits pension schemes defined contribution pension schemes diversification and assets benefits of and corporate bonds, 2nd and equity market risk geographical and government bonds, 2nd and the rational portfolio, 2nd and world equities, 2nd Dow Jones index Industrial Average recovery from losses drop dead allocation early savers edge over the markets see investment edge efficiency frontiers EIS (Enterprise Investment Schemes) Elton, Edwin Modern Portfolio Theory and Investment Analysis emerging market companies listed on Western exchanges Enterprise Investment Schemes (EIS) equities and government and corporate bonds performance and portfolio theory and property investment and the rational portfolio allocations risk preferences, 2nd rebalancing your portfolio risk of diversification and false sense of security recovering from large losses standard deviation, 2nd, 3rd view that markets will always bounce back see also world equities equity risk premium and financial planning ETFs (exchange traded funds), 2nd, 3rd, 4th advantages to owning buying bonds, 2nd, 3rd commodity trading customisation fees and expenses in global property and gold trading implementing and index funds leveraged maturities and minimal risk bonds, 2nd physical or synthetic rebalancing your portfolio and taxes total expense ratio (TER) tracking errors European Union bonds, 2nd expenses see fees and expenses fat tails fees and expenses, 2nd adding up costs alternative investments benefits of paying lower fees and comparison websites financial advisers index trackers compared with active managers and investment edge pension plans and performance impact over time mutual funds, 2nd and the rational investor and the rational portfolio, 2nd rebalancing your portfolio Ferri, Richard All About Asset Allocation financial advisers, 2nd and comparison websites financial crisis 2008–09 and commodities trading and currency matching and government bonds yields and high risk preferences and liquidity and longterm financial planning, 2nd and market risk, 2nd, 3rd, 4th financial planning building your savings and the financial crisis 2008–09, 2nd and investment allocations, 2nd, 3rd and life stages and risk, 2nd risk surveys rules of thumb to consider supercautious savers financial software packages France government bonds fraud, avoiding frequent trading FTSE All-Share index FTSE All-Share Tracker fund FTSE NAREIT Global index, 2nd, 3rd fund pickers future performance mutual funds GDP and corporate bonds and world equity market value, 2nd Germany government bonds gifts and capital gains tax gold, 2nd as security Goldman Sachs government bonds adding to minimal risk assets, 2nd and financial planning and bank deposits banks and government defaults buying in base currency, 2nd credit ratings, 2nd, 3rd, 4th and diversification earnings ETFs, 2nd and the financial crisis (2008) and financial planning inflationprotected liquidity of longerterm maturity minimal risk and world equities, 2nd, 3rd and portfolio theory, 2nd, 3rd and the rational portfolio, 2nd, 3rd adjusting, 2nd, 3rd allocations, 2nd risk preferences real return expectations time horizons yields Greece government debt and bond yields hedge funds, 2nd, 3rd, 4th Japanese government bonds and liquidity high risk preferences home markets overinvestment in Icelandic banks income tax index funds, 2nd and ETFs and government bonds implementing maturities and minimal risk bonds, 2nd total expense ratio (TER) tracking errors index-tracking products, 2nd and active managers adding bonds to a portfolio compared with active managers, 2nd comparison sites, 2nd enhanced independent costs of fund changes and taxes future product development implementing license fees for and liquidity and mutual funds and the rational portfolio, 2nd, 3rd different risk preferences total expense ratio (TER), 2nd versus mutual fund returns over time world equities, 2nd see also ETFs (exchange traded funds); index funds India government bonds inflation earnings from minimal risk bonds inflation-adjusted government bonds inflation-protected bonds returns on world equities information/research costs institutional investors insurance buying deposit insurance schemes intangible assets interest rates cash deposits in banks government bonds, 2nd international investment investment allocations adding other government and corporate bonds and financial planning, 2nd, 3rd flexibility of financial goals life stages rebalancing your portfolio, 2nd investment edge, 2nd absence of, 2nd adding up the costs asset classes to avoid and commodities trading, 2nd and the competition different ways of having and expenses and performance picking your moment and private investments and the rational portfolio reconsidering your edge and world equities ‘invisible hand’ of the market IOUs (promissory notes) IRR (internal rate of return) annuities, 2nd iShares, 2nd Ishikawa, Tets How I Caused the Credit Crunch Japan commodities trading government bonds Nikkei index jewellery leverage ETFs and mortgages portfolios liabilities and the rational portfolio life insurance, 2nd life stages and financial planning liquidity equity portfolio and ‘risky’ bonds and ETFs minimal risk and private investments and the rational portfolio, 2nd, 3rd returns on illiquid investments selling your investment, 2nd localised risks avoiding and noninvestment assets Madoff, Bernie market capitalisation and world equities, 2nd market efficiency and inefficiency Mexico government bonds Microsoft investors, 2nd, 3rd and liquidity, 2nd mid-life savers minimal risk assets, 2nd adding other bonds to corporate bonds, 2nd, 3rd government bonds, 2nd adjusting the risk profile asset classes to avoid buying and diversification and equity markets ETFs and financial planning 50/50 split with world equities, 2nd, 3rd allocations government bonds earnings inflation-protected time horizons of inflationprotected bonds and liquidity as optimal portfolio and portfolio theory, 2nd in the rational portfolio, 2nd, 3rd, 4th, 5th, 6th real return expectations and world equities, 2nd Morgan Stanley mortgages and currency matching and leverage MSCI World Index, 2nd, 3rd, 4th mutual funds fees and performance, 2nd and index trackers national economies and equity market risk OEICs (openended investment companies) oil trading, 2nd optimal portfolio theory minimal risk asset past performance and future performance Paulson, John pension funds, 2nd, 3rd benefits and charges defined benefits schemes underfunded performance and fees index trackers versus active managers versus mutual funds portfolio theory and government bonds optimal and the rational investor price impact private equity capital, 2nd private investments, 2nd and liquidity privatisations and world equities professional investment managers property market investments, 2nd avoiding and financial disasters institutional investors and liquidity and the rational portfolio US subprime housing markets, 2nd, 3rd rational investing, 2nd core of ongoing tasks of rational portfolio adding other bonds to adjusting allocations and equity risk return expectations asset classes to avoid assets and liabilities assets split checklist corporate bonds, 2nd diversification financial benefits of and financial disasters geographical diversification government bonds, 2nd, 3rd, 4th, 5th implementation incorporating other assets and investment edge key components of a and liquidity, 2nd, 3rd and pension plans and portfolio theory and risk preferences risk/return profile, 2nd, 3rd, 4th tailoring to specific needs and circumstances tax adjustments tax benefits of holding and tax efficiency, 2nd, 3rd, 4th world equities, 2nd, 3rd, 4th see also minimal risk assets rebalancing your portfolio ticket size REITs (Real Estate Investment Trusts) residential property market retirees investment allocation retirement annuities and financial planning risk cash deposits credit risk and corporate bonds of equity markets equity risk premium high risk preferences and longterm financial planning, 2nd and the optimised market and the rational portfolio, 2nd, 3rd asset split risk preferences risk expertise websites risk surveys risk/return profile equity markets and financial planning, 2nd, 3rd and long-term financial planning minimal risk assets adding government and corporate bonds to pension plans and portfolio theory and the rational portfolio, 2nd, 3rd, 4th, 5th rebalancing your portfolio world equities riskless investment choice, 2nd S&P 500 index and the CRB Commodity index Index Tracker Portfolio standard deviation stocks volatility savings ‘doing nothing’ with and long-term financial planning life stages SD see standard deviation (SD) selling investments and liquidity software packages Spain government bonds standard deviation (SD) building your savings and equity market risk, 2nd, 3rd synthetic ETFs Taleb, Nassim Nicholas The Black Swan, 2nd tangency points tangible assets tax efficient proxies tax wrappers, 2nd taxes, 2nd advisers or accountants questions to ask benefits of the rational portfolio capital gains tax (CGT), 2nd, 3rd, 4th creating trading lots and financial disaster and pension plans, 2nd rational portfolio adjustment realising losses against tax advice websites tax efficiency and the rational portfolio, 2nd, 3rd, 4th tax schemes tax-sheltered or optimised products transaction tax, 2nd technology-focused funds, 2nd TER (total expense ratio), 2nd This Time is Different: Eight Centuries of Financial Folly (Reinhart and Rogoff) total expense ratio (TER), 2nd transaction taxes, 2nd, 3rd transfer charges turnover costs unit trusts, 2nd United Kingdom bank deposits and credit guarantee equities government bonds credit rating earnings from sterling investors United States corporate bonds, 2nd Dow Jones index, 2nd equity market, 2nd risk, 2nd, 3rd and total expense ratio government bonds credit ratings dollar investors earnings from versus property investment sub-prime housing market, 2nd, 3rd Vanguard, 2nd, 3rd, 4th, 5th, 6th FTSE AllShare index venture capital, 2nd Virgin FTSE All-Share Tracker fund volatility and financial planning predicting future Waal, Edmund de The Hare with Amber Eyes world equities adjusting the rational portfolio alternative weightings defining diversification benefits, 2nd, 3rd ETFs expected returns and financial planning 50/50 split with minimal risk assets, 2nd, 3rd investment allocation and high risk preferences indices liquidity of market value and minimal risk assets, 2nd overweighing ‘home’ equities and portfolio theory and the rational portfolio, 2nd, 3rd, 4th allocations risk preferences real return expectations US market, 2nd ‘Investing Demystified delivers, with great clarity and lucidity, the best possible advice you can get when it comes to personal investments and financial planning.’


pages: 165 words: 50,798

Intertwingled: Information Changes Everything by Peter Morville

A Pattern Language, Airbnb, Albert Einstein, Arthur Eddington, augmented reality, Bernie Madoff, Black Swan, business process, Cass Sunstein, cognitive dissonance, collective bargaining, disruptive innovation, index card, information retrieval, Internet of things, Isaac Newton, iterative process, Jane Jacobs, John Markoff, Lean Startup, Lyft, minimum viable product, Mother of all demos, Nelson Mandela, Paul Graham, peer-to-peer, RFID, Richard Thaler, ride hailing / ride sharing, Schrödinger's Cat, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley startup, source of truth, Steve Jobs, Stewart Brand, Ted Nelson, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, theory of mind, uber lyft, urban planning, urban sprawl, Vannevar Bush, zero-sum game

lvii Don’t Just Be the Change: Mass-Produce It by Alex Steffen (2007). lviii Why Dolphins Make Us Nervous by Robert Krulwich (2013). lix Nonhuman Rights Project, http://www.nonhumanrights.org. lx Your Body Is Younger Than You Think by Nicholas Wade. lxi What is the Function of the Claustrum? by Francis Crick, Christof Koch (2005). lxii A “black swan” is a pivotal event that’s hard to predict or imagine in advance. Nassim Taleb popularized the term in his book, The Black Swan (2007). lxiii Soon Love Soon by Vienna Teng. lxiv Cataloging the World by Alex Wright (2014). lxv As We May Think by Vannevar Bush (1945). lxvi Project Xanadu by Ted Nelson, http://www.xanadu.com. lxvii A Research Center for Augmenting Human Intellect by Doug Englebart (1968). lxviii Englebart’s violin was a chorded keyboard designed to be used in concert with a traditional typewriter keyboard and a mouse.

lxxii On the Drucker Legacy by Robert Klitgaard (2006). lxxiii The Black Swan by Nicholas Nassim Taleb (2007), p.40. lxxiv On Intelligence by Jeff Hawkins (2004), p.87. lxxv The Tell-Tale Brain by V. S. Ramachandran (2011), p.55. lxxvi Hawkins (2004), p.89. lxxvii Teaching Smart People How to Learn by Chris Argyris (1991). lxxviii Models of My Life by Herbert Simon (1991), p.xvii. lxxix Making Sense of the Organization by Karl Weick (2001), p.195. lxxx Weick (2001), p.369. lxxxi Stop Worrying About Making the Right Decision by Ed Batista (2013). lxxxii Mistakes Were Made (But Not By Me) by Carol Tavris et al. (2007), p.32. lxxxiii Weick (2001), p.27. lxxxiv Gamestorming by Dave Gray, Sunni Brown, and James Macanufo (2010). lxxxv Antifragile: Things That Gain from Disorder by Nassim Nicholas Taleb (2012). lxxxvi Weick (2001), p.371.

To make sense of an infinite universe, we create categories to reduce complexity. And we use tools and language to spread the load across mind-body-environment. Despite these devices, our search for the truth is limited by a very small flashlight. So we must spin our categories like tetrominos. We must turn our ontologies downside-in and upside-out. We must seek monsters and cyborgs in the borderlands, and be mindful to watch for “black swans.”lxii We can’t make change unless we’re playful, since learning means letting go. E.M. Forster wrote “the song of the future must transcend creed” and asked “how can I know what I think till I see what I say?” There’s wisdom in those words, but to stare at the finger is to miss the moon. In the beginning was yathā bhūta, reality as-it-is, unmediated by concepts or classification or culture. Now, trapped in our own maps, we meditate, in search of the untranslatable, an understanding deeper than words.


pages: 531 words: 125,069

The Coddling of the American Mind: How Good Intentions and Bad Ideas Are Setting Up a Generation for Failure by Greg Lukianoff, Jonathan Haidt

AltaVista, Bernie Sanders, bitcoin, Black Swan, cognitive dissonance, correlation does not imply causation, demographic transition, Donald Trump, Ferguson, Missouri, Filter Bubble, helicopter parent, hygiene hypothesis, income inequality, Internet Archive, Isaac Newton, low skilled workers, Mahatma Gandhi, mass immigration, mass incarceration, means of production, moral panic, Nelson Mandela, Ralph Nader, risk tolerance, Silicon Valley, Snapchat, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, Unsafe at Any Speed

Antifragility No one has done a better job of explaining the harm of avoiding stressors, risks, and small doses of pain than Nassim Nicholas Taleb, the Lebanese-born statistician, stock trader, and polymath who is now a professor of risk engineering at New York University. In his 2007 best seller, The Black Swan, Taleb argued that most of us think about risk in the wrong way. In complex systems, it is virtually inevitable that unforeseen problems will arise, yet we persist in trying to calculate risk based on past experiences. Life has a way of creating completely unexpected events—events Taleb likens to the appearance of a black swan when, based on your past experience, you assumed that all swans were white. (Taleb was one of the few who predicted the global financial crisis of 2008, based on the financial system’s vulnerability to “black swan” events.) In his later book Antifragile, Taleb explains how systems and people can survive the inevitable black swans of life and, like the immune system, grow stronger in response.

In his later book Antifragile, Taleb explains how systems and people can survive the inevitable black swans of life and, like the immune system, grow stronger in response. Taleb asks us to distinguish three kinds of things. Some, like china teacups, are fragile: they break easily and cannot heal themselves, so you must handle them gently and keep them away from toddlers. Other things are resilient: they can withstand shocks. Parents usually give their toddlers plastic cups precisely because plastic can survive repeated falls to the floor, although the cups do not benefit from such falls. But Taleb asks us to look beyond the overused word “resilience” and recognize that some things are antifragile. Many of the important systems in our economic and political life are like our immune systems: they require stressors and challenges in order to learn, adapt, and grow.

Nature Human Behaviour, 1(4), 0082. Sue, D. W., Capodilupo, C. M., Torino, G. C., Bucceri, J. M., Holder, A. M., Nadal, K. L., & Esquilin, M. (2007). Racial microaggressions in everyday life: Implications for clinical practice. American Psychologist, 62(4), 271–286. Tajfel, H. (1970). Experiments in intergroup discrimination. Scientific American, 223(5), 96–102. Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York, NY: Random House. Taleb, N. N. (2012). Antifragile: Things that gain from disorder. New York, NY: Random House. Tetlock, P. E., Kristel, O. V., Elson, B., Green, M., & Lerner, J. (2000). The psychology of the unthinkable: Taboo trade-offs, forbidden base rates, and heretical counterfactuals. Journal of Personality and Social Psychology, 78, 853–870. Thibaut, J.


pages: 741 words: 179,454

Extreme Money: Masters of the Universe and the Cult of Risk by Satyajit Das

affirmative action, Albert Einstein, algorithmic trading, Andy Kessler, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, BRICs, British Empire, business cycle, capital asset pricing model, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Fall of the Berlin Wall, financial independence, financial innovation, financial thriller, fixed income, full employment, global reserve currency, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, happiness index / gross national happiness, haute cuisine, high net worth, Hyman Minsky, index fund, information asymmetry, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, job automation, Johann Wolfgang von Goethe, John Meriwether, joint-stock company, Jones Act, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Martin Wolf, mega-rich, merger arbitrage, Mikhail Gorbachev, Milgram experiment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, Naomi Klein, negative equity, NetJets, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, Paul Samuelson, pets.com, Philip Mirowski, plutocrats, Plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, quantitative easing, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Thaler, Right to Buy, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, Rod Stewart played at Stephen Schwarzman birthday party, rolodex, Ronald Reagan, Ronald Reagan: Tear down this wall, Satyajit Das, savings glut, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, survivorship bias, The Chicago School, The Great Moderation, the market place, the medium is the message, The Myth of the Rational Market, The Nature of the Firm, the new new thing, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond, zero-sum game

Fêted as a rock star at Davos, the black swan thesis of Nassim Taleb attracts attention. The crisis may be a black swan (an unknown unknown), white swan (a known unknown) or gray (all of the above) swan. Taleb combatively attacks the charlatans—anyone who does not agree with him. Economists and statisticians do not react well to being labeled pseudo-scientists of financial risk who disguised their incompetence behind maths. In August 2007, the imbeciles, knaves, and fools (Taleb’s descriptions) devoted an entire issue of The American Statistician to the black swan hypothesis. On the Charlie Rose Show, Taleb dismisses all criticism as ad hominen, a logical fallacy that the validity of a premise is linked to the advocating person. Statisticians do not like the grandiose prose or tangential literary flights that Taleb defends, arguing that his book is a work of literature and philosophy.

The mathematician Benoit Mandelbrot demonstrated that normal distributions do not exist in practice. In Fooled by Randomness and Black Swan, Nicholas Nassim Taleb argued against the application of statistical methods in finance, especially the normal distribution curve to measure risk. Taleb drew on John Stuart Mill, himself rephrasing a problem posed by Scottish philosopher David Hume: “no amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.” Unpredictable extremes of price movement, known as fat tails, were more common than theory implied. Extreme price moves were perfectly intelligible in hindsight but are not predictable—just as the existence of black swans was only discovered in Australia in the eighteenth century. In August 2007, David Viniar, Goldman Sachs’ CFO, commented that: “We were seeing things that were 25-standard-deviation moves, several days in a row.”

We have no time to go below surfaces.16 During the global financial crisis, CNBC’s Power Lunch hosted a segment—“Turning the corner”—with Dr. Doom, Nouriel Roubini, together with the former derivative trader, financial philosopher, and best-selling author known variously as Nassim Taleb, Nicholas Nassim Taleb, NNT, or simply the Black Swan (his best-known work). Bill Griffeth, the interviewer, and his co-host Michelle Caruso-Cabrera, started off upbeat: “What would it take to make you bearish on this economy right now?” Dr. Doom summarized the position: “It’s ugly!” Dr. Doom’s prophecy that the current recession was likely to be three times as long and three times as deep as previous recent recessions did not please Griffeth: “But that’s not the end of the world, is it?” The Black Swan was gloomy: “We have the same people in charge, those who did not see the crisis coming.” The hosts tried in vain to look for positive statements.


pages: 444 words: 151,136

Endless Money: The Moral Hazards of Socialism by William Baker, Addison Wiggin

Andy Kessler, asset allocation, backtesting, bank run, banking crisis, Berlin Wall, Bernie Madoff, Black Swan, Branko Milanovic, break the buck, Bretton Woods, BRICs, business climate, business cycle, capital asset pricing model, commoditize, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crony capitalism, cuban missile crisis, currency manipulation / currency intervention, debt deflation, Elliott wave, en.wikipedia.org, Fall of the Berlin Wall, feminist movement, fiat currency, fixed income, floating exchange rates, Fractional reserve banking, full employment, German hyperinflation, housing crisis, income inequality, index fund, inflation targeting, Joseph Schumpeter, Kickstarter, laissez-faire capitalism, land reform, liquidity trap, Long Term Capital Management, McMansion, mega-rich, money market fund, moral hazard, mortgage tax deduction, naked short selling, negative equity, offshore financial centre, Ponzi scheme, price stability, pushing on a string, quantitative easing, RAND corporation, rent control, reserve currency, riskless arbitrage, Ronald Reagan, school vouchers, seigniorage, short selling, Silicon Valley, six sigma, statistical arbitrage, statistical model, Steve Jobs, stocks for the long run, The Great Moderation, the scientific method, time value of money, too big to fail, upwardly mobile, War on Poverty, Yogi Berra, young professional

That is to say, there are things that we know we now know we don’t know. But there are also unknown unknowns. There are things we do not know we don’t know. Donald Rumsfeld, statement from Defense Department briefing, 200210 In his bestseller, The Black Swan, the financial commentator Nassim Taleb claims that investors and economists err in understanding the direction of markets because they underestimate what they do not know, a concept he names “tunneling.” (Before the sighting of a black swan in Australia, all swans were believed to be white.) Taleb tells the story of visitors to the medievalist and philosopher Umberto Eco’s library of 30,000 books: Most compliment his knowledge by asking in amazement how many has he read; others understand that his wisdom comes from his habit of turning to the large number of unread books at his disposal for reference.

By 1928, Kemmerer had spread the gold-exchange standard across the globe to 50 nations.7 Morgan rose to influence in an era when the accumulation of great wealth began to astound the ordinary man. As Nassim Nicholas Taleb observed in his book, The Black Swan, life is unfair, a theme the economist Sherwin Rosen, author of studies about the economics of superstars, develops as credited by Taleb. Rosen bemoans the high salaries of basketball stars and TV personalities, which he attributes to the “tournament effect,” wherein someone who is marginally better can win the whole pot, leaving others with nothing. Taleb reminds us how this is so: We would rather buy a recording featuring the renowned Vladimir Horowitz for $10.99 than pay $9.99 for one of a struggling pianist. However, ordinary men through democracy can band together to not only ostracize the J.P.

Yalman Onaran, Banks Keep $35 Billion Markdown Off Income Statements (Update1), Bloomberg, May 19, 2008. 12. Harry Marcopolos, The World’s Largest Hedge Fund is a Fraud, November 7, 2005. Copies circulate on various Internet sites, including http://www. berniemadoffsec.com/the-worlds-largest-hedge-fund-is-a-fraud.html. 13. Nassim Nicholas Taleb, The Black Swan:The Impact of the Highly Improbable (New York: Random House, 2007), 229–252. A sprightly discussion of this is in The Black Swan’s Chapter 15: “The Bell Curve, That Great Intellectual Fraud.” Notes 383 14. Zillow, The Majority of U.S. Homeowners Thinks Their Home is Insulated From the Housing Crisis (August 6, 2008), http://zillow.mediaroom.com/index. php?s=159&item=64. Chapter 3: The Rise and Fall of Hard Money 1. Laurence H. Meyer, Remarks by Governor Laurence H.


pages: 453 words: 111,010

Licence to be Bad by Jonathan Aldred

"Robert Solow", Affordable Care Act / Obamacare, Albert Einstein, availability heuristic, Ayatollah Khomeini, Benoit Mandelbrot, Berlin Wall, Black Swan, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Cass Sunstein, clean water, cognitive dissonance, corporate governance, correlation does not imply causation, cuban missile crisis, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Fall of the Berlin Wall, falling living standards, feminist movement, framing effect, Frederick Winslow Taylor, From Mathematics to the Technologies of Life and Death, full employment, George Akerlof, glass ceiling, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Isaac Newton, Jeff Bezos, John Nash: game theory, John von Neumann, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, meta analysis, meta-analysis, Mont Pelerin Society, mutually assured destruction, Myron Scholes, Nash equilibrium, Norbert Wiener, nudge unit, obamacare, offshore financial centre, Pareto efficiency, Paul Samuelson, plutocrats, Plutocrats, positional goods, profit maximization, profit motive, race to the bottom, RAND corporation, rent-seeking, Richard Thaler, ride hailing / ride sharing, risk tolerance, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, Skype, Social Responsibility of Business Is to Increase Its Profits, spectrum auction, The Nature of the Firm, The Wealth of Nations by Adam Smith, transaction costs, trickle-down economics, Vilfredo Pareto, wealth creators, zero-sum game

Standard deviation is a measure of distance from the average, the middle of the bell; 25-standard deviation events are very far away from this, and so should happen less than once in the history of the universe. Events with extreme impacts, which happen completely unexpectedly (although they may seem predictable with hindsight), have been named Black Swans by Nassim Taleb, a Lebanese mathematician and sometime hedge-fund manager. Bell-curve thinking essentially assumes that the possibility of black swans can be ignored, because they will never happen. Goldman Sachs was not alone in using bell-curve thinking. This orthodoxy dominates the financial sector and has repeatedly been approved by regulators as a basis for judging risks. The global financial crisis beginning in 2007 did not cause banks and their regulators to abandon bell-curve thinking, although they could hardly dismiss the events of that period as unprecedented ‘one-offs’.

New Haven, Yale University Press. A broader, deeper analysis from a philosopher: Grant, R. (2012). Strings Attached. Princeton, Princeton University Press. 8. TRUST IN NUMBERS Entertaining and insightful on the ideas behind the global financial crisis: Lanchester, J. (2010). Whoops! London, Penguin. Sprawling but unmissable, and influential, on the flaws in bell-curve thinking: Taleb, N. (2010). The Black Swan. London, Penguin. 9. YOU DESERVE WHAT YOU GET On the rise of the Laffer curve and, more generally, the political/cultural shift towards markets in the US: Rodgers, D. (2011). Age of Fracture. Harvard, Harvard University Press. There are several excellent books on inequality, but few combine accessibility, insight and encyclopaedic knowledge as well as: Atkinson, A. (2015).

Mellor adds that Ramsey’s version of Wittgenstein here is an improvement on the original, because it sums up a major objection to the Tractatus, one which Wittgenstein (probably) came to endorse. 5 Keynes, J. M. (1937), ‘The General Theory’, Quarterly Journal of Economics, 51, 213–14. 6 Friedman, M., and Friedman, R. (1998), Two Lucky People (Chicago: University of Chicago Press), 146. 7 For this example and much more detail on the difference between bell-curve and scale-invariant phenomena see N. Taleb (2010), The Black Swan (London: Penguin), chapter 15. 8 BBC News, 15 September 2008: http://news.bbc.co.uk/1/hi/7616996.stm. Quoted in D. Orrell (2012), Economyths (London: Icon), 90. 9 Freedman D., and Stark, P. (2003), ‘What is the Chance of an Earthquake?’, Technical report 611, Department of Statistics, University of California, Berkeley. For an accessible introduction which has influenced my discussion here see D.


Trend Commandments: Trading for Exceptional Returns by Michael W. Covel

Albert Einstein, Bernie Madoff, Black Swan, business cycle, buy and hold, commodity trading advisor, correlation coefficient, delayed gratification, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, Lao Tzu, Long Term Capital Management, market bubble, market microstructure, Mikhail Gorbachev, moral hazard, Myron Scholes, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative finance, random walk, Sharpe ratio, systematic trading, the scientific method, transaction costs, tulip mania, upwardly mobile, Y2K, zero-sum game

The Snowball: Warren Buffett and the Business of Life. New York: Bantam Books, 2008. Schwager, Jack D. Market Wizards: Interviews with Top Traders. Columbia: Marketplace Books, 2006. Stridsman, Thomas. Trading Systems That Work: Building and Evaluating Effective Trading Systems. New York: McGraw Hill, 2001. Taleb, Nassim Nicholas. Fooled By Randomness: The Hidden Role of Chance in the Markets and in Life. New York: TEXERE, 2001. Taleb, Nassim Nicholas. The Black Swan: The Impact of the Highly Improbable. New York: The Random House Publishing Group, 2007. Taleb, Nassim. The Bed of Procrustes: Philosophical and Practical Aphorisms. New York: Random House Publishing Group, 2010. Tharp, Van. Trade Your Way to Financial Freedom. New York: McGraw Hill, Inc., 2007. Thomas, Conrad W. Risk and Opportunity: A New Approach to Stock Market Profits.

That’s a major winner right there.3 My political science background allowed me to see that; others should be able to see it too. However, many look at events through the wrong lens. Deep down the money elite know that standard finance theory has no explanation for the winners of low-probability, high-impact events.4 The bottom line is that big, unexpected events make trend traders rich. They exploit Black Swan events (see Nassim Taleb’s book of the same name for more). 92 Tre n d C o m m a n d m e n t s What do I mean? When a major event occurs, such as the Russian debt default of August 1998, the terrorist attacks of September 11, 2001, or the 2002 and 2008 equity market crashes, events accelerate existing trends to even greater magnitudes.5 Unexpected events will never stop. People are people and there are always poor market strategies to exploit.

Index A academic degrees, worth of, 123 Aha! moments, 143-144 alpha in trend following, 137 Altis Partners, 5, 19 Apple Computer, 87 approval ratings, based on economy, 181-182 Aspect Capital, 5 average true range, defined, 13 averaging losses, avoiding, 79 benchmark comparisons, 105-106 Bernanke, Ben, 175-176 bet sizing, 61-62 beta, defined, 12 black box, trend following compared to, 187 Black Swan (Taleb), 91 Blankfein, Lloyd, 175 blind risk, 56 BlueCrest, 5 Borish, Peter, 143 B Bratt, Elmer Clark, 230 Bacon, Louis, 5, 15 bubbles Barclays CTA Index, 15 irrational behavior in, 25-26 Barings Bank collapse, 4 predicting, 153 Bartiromo, Maria, 174, 177 Buffett, Warren, 157-158, 189 batting statistics example, 138-139 Burnett, Erin, 42 Becker, Gary, 25 Bush, George W., 182 behaviors, leading to market losses, 117-120 Bush, George H.


pages: 227 words: 62,177

Numbers Rule Your World: The Hidden Influence of Probability and Statistics on Everything You Do by Kaiser Fung

American Society of Civil Engineers: Report Card, Andrew Wiles, Bernie Madoff, Black Swan, business cycle, call centre, correlation does not imply causation, cross-subsidies, Daniel Kahneman / Amos Tversky, edge city, Emanuel Derman, facts on the ground, fixed income, Gary Taubes, John Snow's cholera map, moral hazard, p-value, pattern recognition, profit motive, Report Card for America’s Infrastructure, statistical model, the scientific method, traveling salesman

This classic theory works well for automotive insurance but applies poorly to catastrophe insurance, as Tampa businessman Bill Poe painfully discovered. For auto insurers, the level of total claims is relatively stable from year to year, even though individual claims are dispersed over time. By contrast, catastrophe insurance is a “negative black swan” business, to follow Nassim Taleb’s terminology. In Taleb’s view, business managers can be lulled into ignoring certain extremely unlikely events (“black swans”) just because of the remote chance of occurrence, even though the rare events have the ability to destroy their businesses. Hurricane insurers hum along merrily, racking up healthy profits, until the big one ravages the Atlantic coast, something that has little chance of happening but wreaks extreme damage when it does happen.

By contrast, I bring out the key concepts underlying those techniques, such as variability, correlation, and stratification. With most books focused on exciting new theories, the work of applied scientists has suffered from general neglect. Freakonomics is a notable exception, covering the applied research of the economics professor Steven Levitt. Two books in the finance area also fit the bill: in The Black Swan, Nassim Taleb harangues theoreticians of financial mathematics (and other related fields) on their failure in statistical thinking, while in My Life as a Quant, Emanuel Derman offers many valuable lessons for financial engineers, the most important of which is that modelers in the social sciences—unlike physicists—should not seek the truth. Daniel Kahneman summarized his Nobel-prize-winning research on the psychology of judgment, including the distinction between intuition and reasoning, in “Maps of Bounded Rationality: Psychology for Behavioral Economics,” published in American Economic Review.

Hurricane insurers hum along merrily, racking up healthy profits, until the big one ravages the Atlantic coast, something that has little chance of happening but wreaks extreme damage when it does happen. A mega-hurricane could cause $100 billion in losses—fifty to a hundred times higher than the damage from the normal storm. The classic theory of insurance, which invokes the bell curve, breaks down at this point because of extreme variability and severe spatial concentration of this risk. When the black swan appears, a large portion of customers makes claims simultaneously, overwhelming insurers. These firms might still be solvent on average—meaning that over the long run, their premiums would cover all claims—but the moment cash balances turn negative, they implode. Indeed, catastrophe insurers who fail to plan for the variability of claims invariably find themselves watching in horror as one ill wind razes their entire surplus. Statisticians not only notice variability but also recognize its type.


Bulletproof Problem Solving by Charles Conn, Robert McLean

active transport: walking or cycling, Airbnb, Amazon Mechanical Turk, asset allocation, availability heuristic, Bayesian statistics, Black Swan, blockchain, business process, call centre, carbon footprint, cloud computing, correlation does not imply causation, Credit Default Swap, crowdsourcing, David Brooks, Donald Trump, Elon Musk, endowment effect, future of work, Hyperloop, Innovator's Dilemma, inventory management, iterative process, loss aversion, meta analysis, meta-analysis, Nate Silver, nudge unit, Occam's razor, pattern recognition, pets.com, prediction markets, principal–agent problem, RAND corporation, randomized controlled trial, risk tolerance, Silicon Valley, smart contracts, stem cell, the rule of 72, the scientific method, The Signal and the Noise by Nate Silver, time value of money, transfer pricing, Vilfredo Pareto, walkable city, WikiLeaks

Set up a priorities matrix for the top half dozen priorities in your business plan. Which ones should you ramp up with resources and slow down or terminate based on your assessment? How else might you cleave the nursing supply problem? Would you cleave the airport capacity problem in a more compelling way than outlined in Chapter 1? Notes 1  Margaret Webb Pressler, “The Fall of the House of Hechinger,” Washington Post, July 21, 1997. 2  See Nassim N. Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007), for a deeply insightful discussion of probability and decision‐making errors. 3  Remedy DefinitionsContextualize/Placard: Leaves artifact in place, but tells the story of the actor's role in offensive acts. Balancing: Brings into the physical or digital space the voices/images of others, including those wronged, may include balancing restorative justice actions.

Tetlock and Dan Gardner, Superforecasting: The Art and Science of Prediction (Crown Publishing, 2015). 3  See Rolf Dobeli, The Art of Thinking Clearly (Sceptre, 2013). 4  Daniel Kahneman, Dan Lovallo, and Olivier Sibony, “The Big Idea: Before You Make the Big Decision,” Harvard Business Review, June 2011; Private conversation with Professor Dan Lovallo, University of Sydney. 5  Philip E. Tetlock and Dan Gardner, Superforecasting: The Art and Science of Prediction (Crown Publishing, 2015). 6  Caroline Webb, How to Have a Good Day (Random House, 2016), 167. 7  Caroline Webb, How to Have a Good Day (Random House, 2016), 170–172. 8  Philip E. Tetlock and Dan Gardner, Superforecasting: The Art and Science of Prediction (Crown Publishing, 2015). 9  Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (Random House, 2007). 10  Daniel Kahnemann, Dan Lovallo, and Olivier Sibony, “Before You Make That Big Decision,” Harvard Business Review, June 2011. Chapter Five Conduct Analyses How you go about gathering facts and conducting analysis to test your hypotheses often makes the difference between good and bad problem solving, even when the earlier steps have been followed carefully.

Can you think of an example from your work where you have a problem you can interrogate with 5 Whys? Lay out the tree and sequence of questions. How would you take the root cause case of homeless women to the next stage of inquiry? What are the second‐ and third‐order questions to be asked? What analysis would you want to see undertaken before you felt comfortable with policies to address the issue? Notes 1  Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (Penguin, 2007). 2  Gerd Gigerenzer, Peter M. Todd, and the ABC Research Group, Simple Heuristics That Make Us Smart (Oxford University Press, 2000). 3  Report prepared for the United Kingdom's Department of International Development by The Nature Conservancy, WWF, and the University of Manchester, “Improving Hydropower Outcomes through System Scale Planning, An Example from Myanmar,” 2016. 4  Warren Buffett, “My Philanthropic Pledge,” Fortune, June 16, 2010. 5  Our friend Barry Nalebuff of Yale points out that the actual rule is 69.3, but is usually rounded up to 72 because it is easier to do the division in your head. 6  CB Insights, May 25, 2015, www.cbinsights.com. 7  Nate Silver, The Signal and the Noise (Penguin, 2012). 8  Dan Lovallo, Carmina Clarke, and Colin Camerer, “Robust Analogizing and the Outside View: Two Empirical Tests of Case Based Decision Making,” Strategic Management Journal 33, no. 5 (2012): 496–512. 9  “‘Chainsaw Al’ Axed,” CNN Money, June 15, 1998. 10  This problem was suggested by Barry Nalebuff of Yale University. 11  Nicklas Garemo, Stefan Matzinger, and Robert Palter, “Megaprojects: The Good, the Bad, and the Better,” McKinsey Quarterly, July 2015 (quoting Bent Flyvberg, Oxford Saïd Business School). 12  Daniel Kahneman, Dan Lovallo, and Olivier Sibony, “Before You Make that Big Decision,” Harvard Business Review, June 2011. 13  Gerd Gigerenzer, Peter M.


pages: 345 words: 75,660

Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb

"Robert Solow", Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, artificial general intelligence, autonomous vehicles, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, On the Economy of Machinery and Manufactures, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steven Levy, strong AI, The Future of Employment, The Signal and the Noise by Nate Silver, Tim Cook: Apple, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

If something has never happened before, a machine cannot predict it (at least without a human’s careful judgment to provide a useful analogy that allows the machine to predict using information about something else). Nassim Nicholas Taleb emphasizes unknown unknowns in his book The Black Swan.13 He highlights that we cannot predict truly new events from past data. The book’s title refers to the Europeans’ discovery of a new type of swan in Australia. To eighteenth-century Europeans, swans were white. Upon arrival in Australia, they saw something totally new and unpredictable: black swans. They had never seen black swans and therefore had no information that could predict the existence of such a swan.14 Taleb argues that the appearances of other unknown unknowns have important consequences—unlike the appearance of black swans, which had little meaningful impact on the direction of European or Australian society. For example, the 1990s were a good time to be in the music industry.15 CD sales were growing and revenue climbed steadily.

Even as machines get better at such situations, the laws of probability mean that in small samples, there will always be some uncertainty. Thus, when data is sparse, machine predictions will be imprecise in a known way. The machine can provide a sense of how imprecise its predictions are. As we discuss in chapter 8, this creates a human role for judging how to act on imprecise predictions. 13. Nassim Nicholas Taleb, The Black Swan (New York: Random House, 2007). 14. In Isaac Asimov’s Foundation series, prediction becomes powerful enough that it could foresee the destruction of the Galactic Empire and the various growing pains of the society that is the focus of the story. Important to the plot line, however, is that these predictions could not foresee the rise of “the mutant.” Predictions did not foresee the unexpected event. 15.

See also autonomous vehicles autonomous vehicles, 8, 14–15 decision making by, 111–112 knowledge loss and, 78 legal requirements on, 116 loss of human driving skill and, 193 mail delivery, 103 in mining, 112–114 passenger interests and, 95 preferences and, 88–90 rail systems, 104 reward function engineering in, 92 school bus drivers and, 149–150 tolerance for error in, 185–187 value capture and, 164–165 Autopilot, 8 Babbage, Charles, 12, 65 back propagation, 38 Baidu, 164, 217, 219 bail-granting decisions, 56–58 bank tellers, 171–173 Bayesian estimation, 13 Beane, Billy, 56, 161–162 Beijing Automotive Group, 164 beta testing, 184, 191 Bhalla, Ajay, 25 biases, 19 feedback data and, 204–205 human predictions and, 55–58 in job ads, 195–198 against machine recommendations, 117 regression models and, 34 variance and, 34–35 binding affinity, 135–138 Bing, 50, 204, 216 biopsies, 108–109, 148 BlackBerry, 129 The Black Swan (Taleb), 60–61 Blake, Thomas, 199 blockchain, 220 Bostrom, Nick, 221, 222 boundary shifting, 157–158, 167–178 data ownership and, 174–176 what to leave in/out and, 168–170 breast cancer, 65 Bresnahan, Tim, 12 Bricklin, Dan, 141, 163, 164 A Brief History of Time (Hawking), 210–211 Brynjolfsson, Erik, 91 business models, 156–157 Amazon, 16–17 Camelyon Grand Challenge, 65 capital, 170–171, 213 Capital in the Twenty-First Century (Piketty), 213 capsule networks, 13 Cardiio, 44 Cardiogram, 44–45, 46, 47–49 causality, 63–64 reverse, 62 CDL.


pages: 270 words: 79,180

The Middleman Economy: How Brokers, Agents, Dealers, and Everyday Matchmakers Create Value and Profit by Marina Krakovsky

Affordable Care Act / Obamacare, Airbnb, Al Roth, Ben Horowitz, Black Swan, buy low sell high, Chuck Templeton: OpenTable:, Credit Default Swap, cross-subsidies, crowdsourcing, disintermediation, diversified portfolio, experimental economics, George Akerlof, Goldman Sachs: Vampire Squid, income inequality, index fund, information asymmetry, Jean Tirole, Joan Didion, Kenneth Arrow, Lean Startup, Lyft, Marc Andreessen, Mark Zuckerberg, market microstructure, Martin Wolf, McMansion, Menlo Park, Metcalfe’s law, moral hazard, multi-sided market, Network effects, patent troll, Paul Graham, Peter Thiel, pez dispenser, ride hailing / ride sharing, Robert Metcalfe, Sand Hill Road, sharing economy, Silicon Valley, social graph, supply-chain management, TaskRabbit, The Market for Lemons, too big to fail, trade route, transaction costs, two-sided market, Uber for X, uber lyft, ultimatum game, Y Combinator

v=JYYsXzt1VDc&feature=youtu.be. 32.Nassim Nicholas Taleb, Antifragile: Things That Gain from Disorder (New York: Random House, 2012). 33.Nassim Nicholas Taleb, “Learning to Love Volatility,” Wall Street Journal, November 16, 2012. 34.For an overview of power-law distributions and some of the many phenomena governed by them, see M. E. J. Newman, “Power Laws, Pareto Distributions and Zipf’s Law,” Contemporary Physics 46 (2005): 323–51. 35.Patricia Cohen, “Richest 1% Likely to Control Half of Global by Wealth by 2016, Study Finds,” New York Times, January 19, 2015. 36.Taleb wrote that “In Extremistan, inequalities are such that one single observation can disproportionately impact the aggregate, or the total.” See Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007), 33. 37.Peter Thiel, Zero to One: Notes on Startups, or How to Build the Future (New York: Crown Business, 2014), 86. 38.Julianne Pepitone and Stacy Cowley, “Facebook’s First Big Investor, Peter Thiel, Cashes Out,” CNNMoney, August 20, 2012. 39.In the introduction to their book of interviews with 35 top VCs and angel investors (including Maples), Tarang Shah and Sheetal Shah observe that entrepreneurs who had founded successful companies “had a very strong intuition and access to asymmetric information” that enabled them to tap emerging opportunities.

Benefiting from Power Laws * * * But VCs have something working in their favor to help them make such bold bets: the two extremes don’t mirror each other. Even a monumental failure has a limit to its losses, whether it be $500,000 or $5 million, whereas a spectacular success knows no bounds: the top companies come to be valued at $500 million or more at exit. This is why the risk analyst Nassim Nicholas Taleb, best known as the author of The Black Swan, considers venture capital to be what he calls “antifragile,” something that actually gains from randomness.32 To achieve antifragility, Taleb writes, the potential cost of errors needs to remain small and the potential gain needs to be large. “It is the asymmetry between upside and downside that allows antifragile tinkering to benefit from disorder and uncertainty.”33 When your downside is limited and your upside is potentially infinite, you should embrace risk taking.

That rule fits venture capital perfectly because returns in venture capital follow a power-law distribution, a pattern many of us are familiar with as the 80/20 rule,34 although many power-law distributions are even more extreme. For example, according to a study released today, the 80 wealthiest individuals in the world collectively own $1.9 trillion—a total about equal to the “wealth” of all the people in the poorer half of the world.35 In The Black Swan, Taleb coined a memorable word to refer to such highly skewed distributions: they occur in “Extremistan,” where a single event or data point has a disproportionate impact on the total.36 Venture capital lives in Extremistan in that only about 15 start-ups out of several thousand vying for VC funding each year are responsible for the vast majority of profits: just one of those megahits—the next Google or Facebook or Twitter—will make you a monumental winner even if all your other investments lose money.


pages: 447 words: 104,258

Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues by Alain Ruttiens

algorithmic trading, asset allocation, asset-backed security, backtesting, banking crisis, Black Swan, Black-Scholes formula, Brownian motion, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, discrete time, diversification, fixed income, implied volatility, interest rate derivative, interest rate swap, margin call, market microstructure, martingale, p-value, passive investing, quantitative trading / quantitative finance, random walk, risk/return, Satyajit Das, Sharpe ratio, short selling, statistical model, stochastic process, stochastic volatility, time value of money, transaction costs, value at risk, volatility smile, Wiener process, yield curve, zero-coupon bond

AÏT-SAHALIA, “Telling from discrete data whether the underlying continuous-time model is a diffusion”, Journal of Finance, vol. LVII, no. 5, 2002, pp. 2075–2112. 13 See Y. AÏT-SAHALIA, P.A. MYKLAND, “The effects of random and discrete sampling when estimating continuous-time diffusions”, Econometrica, vol. 71, no. 2, 2003, pp. 483–549. 14 For more about these issues, see E. DERMAN, N. TALEB, “The illusion of dynamic replication”, Quantitative Finance, vol. 5, no. 4, 2005, pp. 323–326. 15 Nassim N. TALEB, The Black Swan, 2nd ed., Random House, 2010, 480 p. 16 See M. HENRARD, Swaptions: 1 price, 10 deltas, and … 6 gammas, Wilmott Magazine, April 2011, pp. 48–57. 17 Robert A. JARROW, Risk management models: construction, testing, usage, Johnson School Research Paper Series no. 38, 2010, March 15, 2011. 18 See Alain RUTTIENS, Pour contribuer à réduire le risque de pertes dans les activités de marché: la gestion d'actifs et le risque de position, AGEFI Luxembourg, October 2008 (in French). 19 Thomas S.

With respect to risk management, the expected credit loss amount should have to be lower than the profits of the bank or fund activities, 15 while the Credit VaR amount should not exceed the net asset value of the bank (or economic capital) or fund. A higher loss than the Credit VaR level may be viewed as likely to threaten the survival of the bank or fund, hence the tentatives to test such possibility by stress tests, although difficult to design properly (cf. the Nassim Taleb's “black swan”). Example. A fund has $100m of various exposures. Adequate Monte Carlo simulations allow to estimate that, over 1 year, the frequency of losses with c = 99% is 15%. For a global recovery rate (cf. Chapter 13, Section 13.1.4) of the exposure estimated at 40%, the 1-year Credit VaR is Given the complexity of the task of modeling credit risk (cf. Chapter 13, Section 13.2.5), financial institutions generally use external systems, like CreditMetrics of J.P.

We can distinguish three main subsets on this graph: Zone “I”: up to the level of expected value of the losses, these must arguably be able to be covered by the expected profits of the market activity of the firm. If not, there is no rationale for maintaining the activity. Zone “II”: there is some higher, unexpected, loss level corresponding to the maximum financial capacity of the firm, before going bankrupt. Zone III: to care for the highly improbable occurrence of losses above this maximum level, one has not found anything but “stress tests”. The problem – explored by Nassim Taleb in his famous book “The Black Swan”15 – is that it is almost impossible to guess what should have to be tested. Indeed, an abnormally huge loss cannot be caused but by a rare, unexpected event, that could have been hardly anticipated (if it was the case, it could even have resulted in a smaller loss) and tested beforehand. This last consideration enlightens the crucial importance of the “fat tails” in the actual distribution of market returns or prices.


pages: 478 words: 126,416

Other People's Money: Masters of the Universe or Servants of the People? by John Kay

Affordable Care Act / Obamacare, asset-backed security, bank run, banking crisis, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, buy and hold, call centre, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, cognitive dissonance, corporate governance, Credit Default Swap, cross-subsidies, dematerialisation, disruptive innovation, diversification, diversified portfolio, Edward Lloyd's coffeehouse, Elon Musk, Eugene Fama: efficient market hypothesis, eurozone crisis, financial innovation, financial intermediation, financial thriller, fixed income, Flash crash, forward guidance, Fractional reserve banking, full employment, George Akerlof, German hyperinflation, Goldman Sachs: Vampire Squid, Growth in a Time of Debt, income inequality, index fund, inflation targeting, information asymmetry, intangible asset, interest rate derivative, interest rate swap, invention of the wheel, Irish property bubble, Isaac Newton, John Meriwether, light touch regulation, London Whale, Long Term Capital Management, loose coupling, low cost airline, low cost carrier, M-Pesa, market design, millennium bug, mittelstand, money market fund, moral hazard, mortgage debt, Myron Scholes, NetJets, new economy, Nick Leeson, Northern Rock, obamacare, Occupy movement, offshore financial centre, oil shock, passive investing, Paul Samuelson, peer-to-peer lending, performance metric, Peter Thiel, Piper Alpha, Ponzi scheme, price mechanism, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, railway mania, Ralph Waldo Emerson, random walk, regulatory arbitrage, Renaissance Technologies, rent control, risk tolerance, road to serfdom, Robert Shiller, Robert Shiller, Ronald Reagan, Schrödinger's Cat, shareholder value, Silicon Valley, Simon Kuznets, South Sea Bubble, sovereign wealth fund, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, Steve Wozniak, The Great Moderation, The Market for Lemons, the market place, The Myth of the Rational Market, the payments system, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tobin tax, too big to fail, transaction costs, tulip mania, Upton Sinclair, Vanguard fund, Washington Consensus, We are the 99%, Yom Kippur War

Sunlight Foundation, 1 December 2009, 25 March 2013, http://sunlightfoundation.com. Surowiecki, J.M., 2005, The Wisdom of Crowds: Why the Many Are Smarter Than the Few, London, Abacus. Taibbi, M., 2009, ‘The Great American Bubble Machine’, Rolling Stone, 9 July. Tainter, J., 1988, The Collapse of Complex Societies, Cambridge, Cambridge University Press. Taleb, N.N., 2001, Fooled by Randomness: The Hidden Role of Chance in the Markets and in Life, London and New York, Texere. Taleb, N.N., 2007, The Black Swan: The Impact of the Highly Improbable, London, Penguin. Taleb, N.N., 2012, Antifragile: Things That Gain from Disorder, New York, Random House. Tarbell, I.M., 1904, The History of the Standard Oil Company, New York, McClure, Phillips & Co. Taylor, M., 2014, ‘Banks Have Failed to Exorcise Their Technical Gremlins’, Financial Times, 30 January. Tett, G., 2010.

., 2014, ‘The Case for Index-Fund Investing’, Vanguard Research, April, https://advisors.vanguard.com/VGApp/iip/site/advisor/researchcommentary/article/IWE_InvComCase4Index. 20. Kahneman himself is not guilty of this: Kahneman, D., 2011, Thinking Fast and Slow, New York, Farrar, Straus and Giroux. 21. Rubin, R., 2004, In an Uncertain World, New York, Random House. 22. The unknown unknown was famously described by Donald Rumsfeld; see Taleb, N.N., 2007, The Black Swan: The Impact of the Highly Improbable, London, Penguin. 23. Greenspan, A., 2008, Statement to the House, Committee on Oversight and Government Reform, Hearing, 23 October (Serial 110–209). 24. Ibid. 25. Tett, G., 2013, ‘An Interview with Alan Greenspan’, FT Magazine, 25 October. 26. Ramsey, F.P., 1926, ‘Truth and Probability’, in Ramsey, F.P., 1931, The Foundations of Mathematics and Other Logical Essays, ed.

Summers to the Securities Industry Association, Office of Public Affairs, 9 November. 12. Transcript of investor conference call, 9 August 2007, reported on Bloomberg.com, 25 November 2008. 13. Congressional Oversight Panel, June Oversight Report: The AIG Rescue, Its Impact on Markets, and the Government’s Exit Strategy, 10 June 2010. 14. Shaxson, N., 2011, Treasure Islands, New York, St Martin’s Press. 15. Taleb, N.N., 2007, The Black Swan: The Impact of the Highly Improbable, London, Penguin, p. 43. 16. Edwards, J.S.S., Kay, J.A., and Mayer, C.P., 1987, The Economic Analysis of Accounting Profitability, Oxford, Oxford University Press. 17. McLean, B., and Elkind, P., 2003, The Smartest Guys in the Room: The Amazing Rise and Scandalous Fall of Enron, New York, Penguin, p. 41. 18. Galbraith, J.K., 1955, The Great Crash, 1929, London, Hamish Hamilton, pp. 137–9. 19.


pages: 381 words: 101,559

Currency Wars: The Making of the Next Gobal Crisis by James Rickards

Asian financial crisis, bank run, Benoit Mandelbrot, Berlin Wall, Big bang: deregulation of the City of London, Black Swan, borderless world, Bretton Woods, BRICs, British Empire, business climate, buy and hold, capital controls, Carmen Reinhart, Cass Sunstein, collateralized debt obligation, complexity theory, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, Deng Xiaoping, diversification, diversified portfolio, Fall of the Berlin Wall, family office, financial innovation, floating exchange rates, full employment, game design, German hyperinflation, Gini coefficient, global rebalancing, global reserve currency, high net worth, income inequality, interest rate derivative, John Meriwether, Kenneth Rogoff, laissez-faire capitalism, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, money market fund, money: store of value / unit of account / medium of exchange, Myron Scholes, Network effects, New Journalism, Nixon shock, offshore financial centre, oil shock, one-China policy, open economy, paradox of thrift, Paul Samuelson, price mechanism, price stability, private sector deleveraging, quantitative easing, race to the bottom, RAND corporation, rent-seeking, reserve currency, Ronald Reagan, sovereign wealth fund, special drawing rights, special economic zone, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, time value of money, too big to fail, value at risk, War on Poverty, Washington Consensus, zero-sum game

Importantly, phase transitions can produce catastrophic effects from small causes—a single snowflake can cause a village to be destroyed by an avalanche. This is one secret behind so-called black swans. Nassim Nicholas Taleb popularized the term “black swan” in his book of the same name. In that book, Taleb rightly demolished the normal distribution—the bell curve—as a way of understanding risk. The problem is that he demolished one paradigm but did not produce another to replace it. Taleb expressed some disdain for mathematical modeling in general, preferring to take on the mantle of a philosopher. He dubbed all improbably catastrophic events “black swans,” as if to say, “Stuff happens,” and he left it at that. The term is widely used by analysts and policy makers who understand the “Stuff happens” part but don’t understand the critical state dynamics and complexity behind it.

Christ, “A Short-Run Aggregate-Demand Model of the Interdependence and Effects of Monetary and Fiscal Policies with Keynesian and Classical Interest Elasticities,” The American Economic Review 57, no. 2, May 1967. 192 The role of VaR in causing the Panic of 2008 is immense . . . The House of Representatives held one hearing on this topic, at which sworn testimony was provided by Black Swan author Nassim Nicholas Taleb, bank analyst Christopher Whalen and myself, among others. This hearing was held by the Subcommittee on Investigations and Oversight of the Committee on Science, Space and Technology on September 10, 2009. The ostensible reason for using the Science Committee was that VaR is a quantitative and therefore scientific discipline; however, I was informed that this was actually done at the request of Financial Services Committee chairman Barney Frank in order to establish a record on VaR while avoiding the lobbyists who typically influence witness selection and questions in the Financial Services Committee.

Financial Statecraft: The Role of Financial Markets in American Policy. New Haven: Yale University Press, 2006. Stewart, Bruce H., and J. M. Thompson. Nonlinear Dynamics and Chaos, 2nd ed. Chichester, UK: Wiley, 2002. Surowiecki, James. The Wisdom of Crowds. New York: Doubleday, 2004. Tainter, Joseph A. The Collapse of Complex Societies. Cambridge: Cambridge University Press, 1988. Taleb, Nassim Nicholas. The Black Swan: The Impact of the Highly Improbable. New York: Random House, 2007. Tarnoff, Ben. Moneymakers: The Wicked Lives and Surprising Adventures of Three Notorious Counterfeiters. New York: Penguin Press, 2011. Taylor, John B. Getting Off Track: How Government Actions and Interventions Caused, Prolonged, and Worsened the Financial Crisis. Stanford: Hoover Institution Press, 2009. ———.


pages: 246 words: 74,341

Financial Fiasco: How America's Infatuation With Homeownership and Easy Money Created the Economic Crisis by Johan Norberg

accounting loophole / creative accounting, bank run, banking crisis, Bernie Madoff, Black Swan, business cycle, capital controls, central bank independence, collateralized debt obligation, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, David Brooks, diversification, financial deregulation, financial innovation, helicopter parent, Home mortgage interest deduction, housing crisis, Howard Zinn, Hyman Minsky, Isaac Newton, Joseph Schumpeter, Long Term Capital Management, market bubble, Martin Wolf, Mexican peso crisis / tequila crisis, millennium bug, money market fund, moral hazard, mortgage tax deduction, Naomi Klein, new economy, Northern Rock, Own Your Own Home, price stability, Ronald Reagan, savings glut, short selling, Silicon Valley, South Sea Bubble, The Wealth of Nations by Adam Smith, too big to fail

At the end of 2007, the sum of the liabilities and mortgagebacked securities that they had guaranteed and issued equaled the U.S. national debt. For every $100 they had guaranteed or lent through securities, they had only $1.20 of equity.4S In August 2008, Fannie and Freddie owned junk loans and securities based on junk loans worth over $1 trillion-more than one-fifth of their entire mortgage portfolio.49 In the words of Nassim Nicholas Taleb, author of the book The Black Swan, about how people underestimate lowprobability risks, they were "sitting on a barrel of dynamite." Their army of analysts, however, claimed that the risks were small. They had sophisticated models to manage risks. That is, all risks but onea fall in home prices.50 As Freddie Mac's former CEO Richard Syron looked back on what went wrong, he blamed the bad mortgages on politicians' pushing through an expansion of homeownership even to households that could not afford to own a home.

Krugman, "Fannie, Freddie and You." 44. Seeking Alpha, "Countrywide Financial Q2 2007." 45. Leonnig, "How HUD Mortgage Policy Fed the Crisis." 46. Wallison and Calomiris, "The Last Trillion-Dollar Commitment." 47. Shenn, "Fannie, Freddie Subprire Spree." 48. Lockhart, "Reforming the Regulation of the Government Sponsored Enterprises." 49. Wallison and Calomiris, "The Last Trillion-Dollar Commitment." 50. Taleb, The Black Swan, pp. 225-26. 51. Duhigg, "Pressured to Take More Risk." Chapter 3 1. Dougherty, "German Bank Becomes First EU Victim"; The Economist, "Sold Down the River Rhine." 2. Muolo and Padilla, Chain of Blame, pp. 209-10. 3. Grant, Mr. Market Miscalculates, pp. 181-82. 4. Pollock, "The Human Foundations of Financial Risk." 5. Kindleberger and Aliber, Manias, Panics and Crashes, p. 13. 6.

Sarkozy, "International Financial Crisis"; International Herald Tribune, "Germany: US Slipping as Financial Superpower"; Prashad, "Wealth's Apostles." 7. de Rugy and Warren, "Regulatory Agency Spending Reaches New Height," pp. 5-6. 8. de Rugy, "Bush's Regulatory Kiss-Off." 9. Smith, Wealth of Nations, V.i.e.18. 10. Jolly, "Ex-Trader Tells How He Lost So Much for One Bank." 11. Younglai, "SEC's Cox Regrets Short-Selling Ban." 12. Heckscher, Gammal och ny ekonomisk liberalism, pp. 96-97 (quotation translated). 13. Taleb, The Black Swan; Buffett on the Charlie Rose Shozo, WNET, October 1, 2008. 14. Lessig, "Why the Banks All Fell Down." 15. Dowd, "Moral Hazard and the Financial Crisis." 16. The Economist, "Negative Outlook." 17. Hayek, Denationalisation of Money. See also Rothbard, What Has Government Done to Our Money? 18. Hortlund, Fribankskolan, p. 77 (quotation translated). 19. Bernholz, "The Importance of Reorganizing Money, Credit, and Banking," p. 104, citing Parkin and Bade, "Central Bank Laws and Monetary Policy," pp. 24-39.


pages: 376 words: 109,092

Paper Promises by Philip Coggan

accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, balance sheet recession, bank run, banking crisis, barriers to entry, Berlin Wall, Bernie Madoff, Black Swan, Bretton Woods, British Empire, business cycle, call centre, capital controls, Carmen Reinhart, carried interest, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, debt deflation, delayed gratification, diversified portfolio, eurozone crisis, Fall of the Berlin Wall, falling living standards, fear of failure, financial innovation, financial repression, fixed income, floating exchange rates, full employment, German hyperinflation, global reserve currency, hiring and firing, Hyman Minsky, income inequality, inflation targeting, Isaac Newton, John Meriwether, joint-stock company, Kenneth Rogoff, Kickstarter, labour market flexibility, light touch regulation, Long Term Capital Management, manufacturing employment, market bubble, market clearing, Martin Wolf, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, Myron Scholes, negative equity, Nick Leeson, Northern Rock, oil shale / tar sands, paradox of thrift, peak oil, pension reform, plutocrats, Plutocrats, Ponzi scheme, price stability, principal–agent problem, purchasing power parity, quantitative easing, QWERTY keyboard, railway mania, regulatory arbitrage, reserve currency, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, short selling, South Sea Bubble, sovereign wealth fund, special drawing rights, The Chicago School, The Great Moderation, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Wealth of Nations by Adam Smith, time value of money, too big to fail, trade route, tulip mania, value at risk, Washington Consensus, women in the workforce, zero-sum game

Second, because in that event the authorities would have to step in anyway to save a bank and others suffering a similar plight. Another issue was that the figures in the VAR models also tended to be heavily influenced by recent observations. So a long period of low volatility tended to reduce the potential loss generated by the model, thereby persuading banks to take more risk (just as Hyman Minsky predicted). As Taleb points out, this creates a very dangerous mindset. His ‘black swan’ example dates back to the philosopher David Hume; just because you see a thousand white swans does not mean there cannot be a black swan (as there are in Australia). But another example of his reasoning is even more illuminating. Turkeys are fed by the farmer for 364 days, and must presume the farmer to be a benign caregiver; they have no way of anticipating that, on the 365th day, the same farmer will slaughter them for our Thanksgiving or Christmas meal.

New Yorker, 29 November 2010. 19 Bob Woodward, Maestro: Greenspan’s Fed and the American Boom, New York, 2001. 20 Admittedly, that would be very difficult in the case of the European Central Bank. Its mandate was set by treaty. 21 Michiyo Nakamoto and David Wighton, ‘Citigroup Chief Stays Bullish on Buy-outs’, Financial Times, 9 July 2007. 22 Quoted in Nick Leeson, Rogue Trader, London, 1996. 23 Pablo Triana, Lecturing Birds on Flying: Can Mathematical Theories Destroy The Financial Markets?, New York, 2009. 24 Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable, London, 2008. 25 Peter Thal Larsen, ‘Goldman Pays the Price for Being Big’, Financial Times, 13 August 2008. 26 Andrew Haldane, ‘Why Banks Failed the Stress Test’, 9 – 10 February 2009. 27 Interview with the author, 25 October 2010. 9. THE CRISIS BEGINS 1 Tim Congdon, The Debt Threat, Oxford, 1989. 2 Peter Warburton, Debt and Delusion, London, 1999. 3 ‘Debt and Deleveraging: The Global Credit Bubble and its Economic Consequences’, McKinsey Global Institute, January 2010. 4 Scott Schuh, Oz Shy and Joanna Stavins, ‘Who Gains and Who Loses from Credit Card Payments?

Jr, Debt’s Dominion: A History of Bankruptcy Law in America, Princeton, 2001. Skidelsky, Robert, John Maynard Keynes: Fighting for Freedom 1937 – 1946, London, 2001. —Keynes: The Return of the Master, London, 2009. Sorkin, Andrew Ross, Too Big to Fail: Inside the Battle to Save Wall Street, London, 2009. Stiglitz, Joseph, Freefall: Free Markets and the Sinking of the Global Economy , rev. edn, London, 2010. Taleb, Nassim Nicholas, The Black Swan: The Impact of the Highly Improbable , London, 2008. Warburton, Peter, Debt and Delusion: Central Bank Follies that Threaten Economic Disaster, London, 1999. Willetts, David, The Pinch: How the Baby Boomers Took Their Children’s Future – and Why They Should Give It Back, London, 2010. Wolf, Martin, Why Globalization Works: The Case for the Global Market Economy, New Haven, Conn., 2005.


pages: 370 words: 107,983

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All by Robert Elliott Smith

Ada Lovelace, affirmative action, AI winter, Alfred Russel Wallace, Amazon Mechanical Turk, animal electricity, autonomous vehicles, Black Swan, British Empire, cellular automata, citizen journalism, Claude Shannon: information theory, combinatorial explosion, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, desegregation, discovery of DNA, Douglas Hofstadter, Elon Musk, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, low skilled workers, Mark Zuckerberg, mass immigration, meta analysis, meta-analysis, mutually assured destruction, natural language processing, new economy, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, women in the workforce

Errors that fall into the latter camp are what are sometimes call black swans, thanks to Nassim Nicholas Taleb’s popular 2007 book The Black Swan: The Impact of the Highly Improbable.7 Taleb illustrated his titular concept with the one-rule model that simply says, ‘All swans are white’. This model is entirely right and useful, up to the point where one sees even a single black swan. At that point, the model’s value completely vanishes, because one has to reconsider the entire model (that is to say, the one parsimonious rule). A black swan is the revelatory sort of event that Box was talking about, one that prompts a total re-evaluation of swans – if there are black swans might there be swans of different hues, and what else don’t we know about swans now that we’ve discovered there are black swans? This leads to a shift in the movable window, leading us towards the gathering of more data, followed by new cycles of induction and deduction, to form more useful models.

The Nautical Almanac, including tables that implemented Maskelyne’s longitude technique, would continue to be widely used for navigation well into the nineteenth century. 5Mary Croarken, 2003, Mary Edwards: Computing for a Living in 18th-Century England. IEEE Annals of the History of Computing, 25:4, https://ieeexplore.ieee.org/document/1253886 6Molly McLay, 2006, From Wollstonecraft to Mill: Varied Positions and Influences of the European and American Women’s Rights Movements. Constructing the Past, 7(1), Article 13, http://digitalcommons.iwu.edu/constructing/vol7/iss1/13 7N.N. Taleb, 2007, The Black Swan: The Impact of the Highly Improbable, New York: Random House. 8Betty Alexandra Toole, 1992, Ada, the Enchantress of Numbers: A Selection from the Letters of Lord Byron’s Daughter and Her Description of the First Computer. Mill Valley, CA: Strawberry Press. 9Saini, A., 2017, Inferior: How Science Got Women Wrong and the New Research that’s Rewriting the Story. London: Fourth Estate. 10Randy Olsen, 2014, Percentage of Bachelor’s Degrees Conferred to Women, by Major (1970–2012), www.randalolson.com/2014/06/14/percentage-of-bachelors-degrees-conferred-to-women-by-major-1970-2012/ 11Bureau of Labor Statistics, 2013, Occupational Employment Projections to 2022, www.bls.gov/opub/mlr/2013/article/occupational-employment-projections-to-2022.htm 12Suzanne Goldenberg, 2005, Why Women Are Poor at Science, by Harvard President.

This leads to a shift in the movable window, leading us towards the gathering of more data, followed by new cycles of induction and deduction, to form more useful models. Unfortunately, steering the window of social models has resisted recognition of black swans, even when they’ve crashed right into the most important developments in the history of science and technology. In 1814, Byron married the wealthy heiress Anne Isabella Milbanke, nicknamed Lady Annabelle. She was a highly educated and religious woman and their marriage lasted barely two years as Byron’s behaviour grew increasingly erratic, drinking heavily, flying into rages and carrying on affairs. But out of their relationship came one of the era’s most extraordinary female black swans, their daughter Ada, Countess of Lovelace, an accomplished mathematician and writer, patron and what many have called the world’s first computer programmer.


pages: 537 words: 144,318

The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money by Steven Drobny

Albert Einstein, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business cycle, business process, buy and hold, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, commoditize, Commodity Super-Cycle, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, diversification, diversified portfolio, equity premium, family office, fiat currency, fixed income, follow your passion, full employment, George Santayana, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, invisible hand, Kickstarter, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, market microstructure, moral hazard, Myron Scholes, North Sea oil, open economy, peak oil, pension reform, Ponzi scheme, prediction markets, price discovery process, price stability, private sector deleveraging, profit motive, purchasing power parity, quantitative easing, random walk, reserve currency, risk tolerance, risk-adjusted returns, risk/return, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, stocks for the long term, survivorship bias, The Great Moderation, Thomas Bayes, time value of money, too big to fail, transaction costs, unbiased observer, value at risk, Vanguard fund, yield curve, zero-sum game

Too often, people use it to avoid taking responsibility for their actions by claiming the events—and their losses—in 2008 were unforeseeable, whereas in fact their hypothesis of how markets worked was just disproved. The other hypotheses always existed. The metaphor of the black swan is of course an old one and was used by Karl Popper in the 1930s to illustrate the fallacy of induction. It is an example of something that can falsify a hypothesis. If you have a hypothesis that all swans are white, a single black swan falsifies that hypothesis. In this usage, the existence of a black swan is of course neither unforeseeable nor even a low probability event, since hypotheses are falsified all the time. It is as though the recent “black swan” is not taken as a falsification but instead as confirmation that swans are generally white and so we should carry on as before, which is a perverse interpretation of either Popper or Taleb. Did 2008 invalidate the endowment model? I do not think 2008 invalidates what I call the beta-plus approach, which will always have an important place in investing.

When you run money conservatively, always thinking that such a severe market environment is possible, you tend to feel a bit vindicated when it occurs. 2008 was a reminder that it really matters to care about liquidity and correlation, that it matters to worry about a large range of risk indicators rather than just one, that counterparty risk is important, that your balance sheet is important. Most of these lessons are as old as the hills, which is why I really cannot understand all this talk about black swans. When the same thing happens over and over again, how can you be surprised? “Black swan” may have become the most confusing phrase in markets. Nassim Taleb’s recent use of the term is commonly understood to denote an unlikely and unforeseeable event, but this is not the main story of 2008. I saw a crisis as highly likely given people’s beliefs and behaviors. Many people seem to use the “black swan” idea to reassure themselves when some bad things happened that they did not expect. They use it to claim that it was not their fault, which I do not think was Taleb’s meaning. Too often, people use it to avoid taking responsibility for their actions by claiming the events—and their losses—in 2008 were unforeseeable, whereas in fact their hypothesis of how markets worked was just disproved.

.,” Swampland: A Blog about Politics, September 15, 2009, http://swampland.blogs.time.com/2009/09/15/warren-buffett-could-have-saved-lehma/. How do you make sure that you are around to keep playing in your hedge fund? We tend to be long volatility and look for trades that make money in difficult markets, during down swings in risky assets and in times of increased volatility. We like to be long the tails, the black swans. In general, however, we run a fairly low-risk fund. We try to avoid being hurt by catastrophic scenarios because the survivors are usually granted a license to print money for a while. Do you spend more time thinking about how it could all go wrong or how you are going to make money? We spend more time on how to make money. We are in the business of making money, so that is where the whole process starts.


pages: 502 words: 107,657

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, lifelogging, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, speech recognition, statistical model, Steven Levy, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

I also often refer in my own writing to a third type of analytics—“prescriptive”—that tells its users what to do through controlled experiments or optimization. Those quantitative methods are much less popular, however, than predictive analytics. This book and the ideas behind it are a good counterpoint to the work of Nassim Nicholas Taleb. His books, including The Black Swan, suggest that many efforts at prediction are doomed to fail because of randomness and the inherent unpredictability of complex events. Taleb is no doubt correct that some events are black swans that are beyond prediction, but the fact is that most human behavior is quite regular and predictable. The many examples that Siegel provides of successful prediction remind us that most swans are white. Siegel also resists the blandishments of the “big data” movement. Certainly some of the examples he mentions fall into this category—data that is too large or unstructured to be easily managed by conventional relational databases.

Leinweber got as far as 99 percent accuracy predicting the S&P 500 by allowing a regression model to work with not only Bangladesh’s butter production, but Bangladesh’s sheep population, U.S. butter production, and U.S. cheese production. As a lactose-intolerant data scientist, I protest! Leinweber attracted the attention he sought, but his lesson didn’t seem to sink in. “I got calls for years asking me what the current butter business in Bangladesh was looking like and I kept saying, ‘Ya know, it was a joke, it was a joke!’ It’s scary how few people actually get that.” As Black Swan author Nassim Taleb put it in his suitably titled book, Fooled by Randomness, “Nowhere is the problem of induction more relevant than in the world of trading—and nowhere has it been as ignored!” Thus the occasional overzealous yet earnest public claim of economic prediction based on factors like women’s hemlines, men’s necktie width, Super Bowl results, and Christmas day snowfall in Boston. The culprit that kills learning is overlearning (aka overfitting).

See also test data Baesens, Ben bagging (bootstrap aggregating) Bangladesh Barbie dolls Bayes, Thomas (Bayes Network) Beane, Billy Beano Beaux, Alex behavioral predictors Bella Pictures BellKor BellKor Netflix Prize teams Ben Gurion University (Israel) Bernstein, Peter Berra, Yogi Big Bang Theory, The Big Bang theory Big Brother BigChaos team “big data” movement billing errors, predicting black box trading Black Swan, The (Taleb) blogs and blogging anxiety, predicting from entries collective intelligence and data glut and content in LiveJournal mood prediction research via nature of Blue Cross Blue Shield of Tennessee BMW BNSF Railway board games, predictive play of Bohr, Niels book titles, testing Bowie, David brain activity, predicting Brandeis, Louis Brasil Telecom (Oi) breast cancer, predicting Brecht, Bertolt Breiman, Leo Brigham Young University British Broadcasting Corporation (BBC) Brobst, Stephen Brooks, Mel Brynjolfsson, Eric buildings, predicting fault in Bullard, Ben burglaries, predicting business rules, decision trees and buying behavior, predicting C Cage, Nicolas Canadian Automobile Association Canadian Tire car crashes and harm, predicting CareerBuilder Carlin, George Carlson, Gretchen Carnegie Mellon University CART decision trees Castagno, Davide causality cell phone industry consumer behavior and dropped calls, predicting GPS data and location predicting Telenor (Norway) CellTel (African telecom) Central Tables.


pages: 348 words: 83,490

More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded) by Michael J. Mauboussin

Albert Einstein, Andrei Shleifer, Atul Gawande, availability heuristic, beat the dealer, Benoit Mandelbrot, Black Swan, Brownian motion, butter production in bangladesh, buy and hold, capital asset pricing model, Clayton Christensen, clockwork universe, complexity theory, corporate governance, creative destruction, Daniel Kahneman / Amos Tversky, deliberate practice, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, dogs of the Dow, Drosophila, Edward Thorp, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, fixed income, framing effect, functional fixedness, hindsight bias, hiring and firing, Howard Rheingold, index fund, information asymmetry, intangible asset, invisible hand, Isaac Newton, Jeff Bezos, Kenneth Arrow, Laplace demon, Long Term Capital Management, loss aversion, mandelbrot fractal, margin call, market bubble, Menlo Park, mental accounting, Milgram experiment, Murray Gell-Mann, Nash equilibrium, new economy, Paul Samuelson, Pierre-Simon Laplace, quantitative trading / quantitative finance, random walk, Richard Florida, Richard Thaler, Robert Shiller, Robert Shiller, shareholder value, statistical model, Steven Pinker, stocks for the long run, survivorship bias, The Wisdom of Crowds, transaction costs, traveling salesman, value at risk, wealth creators, women in the workforce, zero-sum game

Daniel Kahneman, Paul Slovic, and Amos Tversky (Cambridge: Cambridge University Press, 1982), 306-34. 8 Peter Schwartz, Inevitable Surprises: Thinking Ahead in a Time of Turbulence (New York: Gotham Books, 2003). 9 Roger Lowenstein, When Genius Failed: The Rise and Fall of Long-Term Capital Management (New York: Random House, 2000); Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007). 10 Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica 47 (1979): 263-91. 11 Nassim Nicholas Taleb, Fooled By Randomness: The Hidden Role of Chance in Markets and in Life (New York: Texere, 2001), 89-90. Taleb takes to task the well-known investor Jim Rogers for arguing against investing in options because of the frequency of loss. Says Taleb, “Mr. Jim Rogers seems to have gone very far in life for someone who does not distinguish between probability and expectation.” 12 See chapter 3. 13 Russo and Schoemaker, Winning Decisions, 123-24. 14 Rubin, commencement address, University of Pennsylvania, 1999. 2.

“Damn the Slam PAM Plan!” Slate, July 30, 2003. ——. “Decisions, Decisions.” New Yorker, March 28, 2003. http://www.newyorker.com/archive/2003/03/24/030324ta_talk_surowiecki. ——. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. New York: Random House, 2004. Taleb, Nassim Nicholas. Fooled By Randomness: The Hidden Role of Chance in Markets and in Life. New York: Texere, 2001. ——. The Black Swan: The Impact of the Highly Improbable. New York: Random House, 2007. Tallis, Frank. Hidden Minds: A History of the Unconscious. New York: Arcade Publishing, 2002. Taylor, Richard P. “Order in Pollock’s Chaos.” Scientific American (December 2002). http://materialscience.uoregon.edu/taylor/art/scientificamerican.pdf. Taylor, Richard P., B.

A few stocks going up or down dramatically will often have a much greater impact on portfolio performance than the batting average. Bulls, Bears, and Odds In his provocative book Fooled by Randomness, Nassim Taleb relates an anecdote that beautifully drives home the expected value message.3 In a meeting with his fellow traders, a colleague asked Taleb about his view of the market. He responded that he thought there was a high probability that the market would go up slightly over the next week. Pressed further, he assigned a 70 percent probability to the up move. Someone in the meeting then noted that Taleb was short a large quantity of S&P 500 futures—a bet that the market would go down—seemingly in contrast to his “bullish” outlook. Taleb then explained his position in expected-value terms. Exhibit 3.1 clarifies his thought. EXHIBIT 3.1 Frequency Versus Magnitude Source: Author analysis.


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Masters of Management: How the Business Gurus and Their Ideas Have Changed the World—for Better and for Worse by Adrian Wooldridge

affirmative action, barriers to entry, Black Swan, blood diamonds, borderless world, business climate, business cycle, business intelligence, business process, carbon footprint, Cass Sunstein, Clayton Christensen, cloud computing, collaborative consumption, collapse of Lehman Brothers, collateralized debt obligation, commoditize, corporate governance, corporate social responsibility, creative destruction, credit crunch, crowdsourcing, David Brooks, David Ricardo: comparative advantage, disintermediation, disruptive innovation, don't be evil, Donald Trump, Edward Glaeser, Exxon Valdez, financial deregulation, Frederick Winslow Taylor, future of work, George Gilder, global supply chain, industrial cluster, intangible asset, job satisfaction, job-hopping, joint-stock company, Joseph Schumpeter, Just-in-time delivery, Kickstarter, knowledge economy, knowledge worker, lake wobegon effect, Long Term Capital Management, low skilled workers, Mark Zuckerberg, McMansion, means of production, Menlo Park, mobile money, Naomi Klein, Netflix Prize, Network effects, new economy, Nick Leeson, Norman Macrae, patent troll, Ponzi scheme, popular capitalism, post-industrial society, profit motive, purchasing power parity, Ralph Nader, recommendation engine, Richard Florida, Richard Thaler, risk tolerance, Ronald Reagan, science of happiness, shareholder value, Silicon Valley, Silicon Valley startup, Skype, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, Steven Levy, supply-chain management, technoutopianism, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Hsieh, too big to fail, wealth creators, women in the workforce, young professional, Zipcar

The most famous analyst of the growing uncertainty is Nassim Taleb, whose impeccably timed The Black Swan: The Impact of the Highly Improbable (2007) has become a global best-seller, translated into thirty-one languages, turning its author into the prophet-cum-rock star of the global economic meltdown.8 The product of a prominent and polyglot Lebanese family, Taleb began his career in finance, as a Wall Street trader, including a spell at Lehman Brothers, and as a hedge fund manager. But once he had made enough money to be “free from authority,” as he put it, he turned to academia—he is now a professor at both the Polytechnic Institute of New York University and Oxford University—and to the wider world of punditry. The Black Swan was a quirkily learned polemic against humanity’s hubris about its ability to predict the future. Taleb argued that our minds are wired to deceive us.

We like to think that we can predict and control the world. But most of the greatest events, from scientific breakthroughs to global upheavals, are black swans. This tendency is particularly dangerous when it is allied to arrogant bankers and a globalized financial system. Before the global meltdown, the lords of finance liked to boast that the combination of global integration and the development of sophisticated financial models had dramatically reduced volatility. Nonsense, said Taleb: while volatility might have declined in the short term, giving the appearance of stability, the world was on the verge of a “Black Swan” of devastating proportions. No sooner had the book hit the bookstores than the black swan flapped its wings. The Internet and the capital markets have both reinforced the third revolutionary force: globalization.

See Boston Consulting Group Beals, Vaughn, 320 Beck, Glenn, 388 Beinhocker, Eric, 265–266 Bell, Daniel, 345 Ben & Jerry’s, 195, 247 Bennis, Warren, 307, 392, 403, 408 Berglas, Steven, 406 Berlin, Isaiah, 76, 396 Bernhard, Wolfgang, 53 Bernstein, Ann, 39 Best Buy, 66 Better Capital, 395 Bevan, Nye, 315 Bhattacharya, Arindam, 64 Bhide, Amar, 176, 200 Bieber, Justin, 243 Bilderberg Group, 53 Bimbo, 206 Birt, John, 314, 315 Bishop, Matthew, 47 Black, Conrad, 309 Black & Decker, 206 The Black Swan: The Impact of the Highly Improbably (Taleb), 149–150 Blair, Tony, 44, 315, 320, 401 Blink, 118, 120 Bloom, Nick, 12 BMW, 247, 347 Boardroom, 291–311 women and, 304–305 Boeing, 53, 298 Bombay Stock Exchange, 287 Booth School (Chicago Business School), 51, 57, 61 Booz Allen Hamilton, 64, 298, 343, 354, 404 The Borderless World (Ohmae), 277 Boston Consulting Group (BCG), 5, 52, 63, 64, 111, 223, 228, 254, 267, 364 Boston Pizza, 353 Bower, Joseph, 301 Bower, Marvin, 49–50, 63 BP, 17, 43–45, 280, 301, 320, 358 Brainworkers, 363–390 Brands, 215–216, 272 Branson, Richard, 155, 163, 171–172, 195, 197, 309, 320 Bratton, William, 30–31, 321–322 Brave New World (Huxley), 80 Brecht, Bertolt, 80 BrightHouse, 235 Brin, Sergey, 109–110, 163, 164, 195, 196 British Broadcasting Corporation (BBC), 314, 358 British Children’s Society, 360 British Equal Opportunities Commission, 304 Brochet, Francois, 297 Broughton, Philip Delves, 2–3, 14 Browder, Bill, 139 Browder, Earl, 139 Brown, Gordon, 320 Brown, John Seely, 64, 224, 229 Brown, Shona, 144 Browne, John, 44, 320 Bryan, Lowell, 111 BSkyB, 36 Buckingham, Marcus, 65 Buffett, Warren, 309, 388 Built to Last: Successful Habits of Visionary Companies (Collins and Porras), 112, 262 Burns, C.


pages: 147 words: 39,910

The Great Mental Models: General Thinking Concepts by Shane Parrish

Albert Einstein, Atul Gawande, Barry Marshall: ulcers, bitcoin, Black Swan, colonial rule, correlation coefficient, correlation does not imply causation, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, delayed gratification, feminist movement, index fund, Isaac Newton, Jane Jacobs, mandelbrot fractal, Pierre-Simon Laplace, Ponzi scheme, Richard Feynman, statistical model, stem cell, The Death and Life of Great American Cities, the map is not the territory, the scientific method, Thomas Bayes, Torches of Freedom

(For reference, the general stock market has returned no more than 7% to 8% per annum in the United States over a long period, before fees.) Orders of Magnitude Nassim Taleb puts his finger in the right place when he points out our naive use of probabilities. In The Black Swan, he argues that any small error in measuring the risk of an extreme event can mean we’re not just slightly off, but way off—off by orders of magnitude, in fact. In other words, not just 10% wrong but ten times wrong, or 100 times wrong, or 1,000 times wrong. Something we thought could only happen every 1,000 years might be likely to happen in any given year! This is using false prior information and results in us underestimating the probability of the future distribution being different. _ Taleb, Nassim. The Black Swan: The Impact of the Highly Improbable, 2nd edition. New York: Random House, 2010. Anti-fragility How do we benefit from the uncertainty of a world we don’t understand, one dominated by “fat tails”?

This book, and the volumes which will follow, are the books I wish had existed years ago when I started learning about mental models. These are my homage to the idea that we can benefit from understanding how the world works and applying that understanding to keep us out of trouble. The ideas in these volumes are not my own, nor do I deserve any credit for them. They come from the likes of Charlie Munger, Nassim Taleb, Charles Darwin, Peter Kaufman, Peter Bevelin, Richard Feynman, Albert Einstein, and so many others. As the Roman poet Publius Terentius wrote: “Nothing has yet been said that’s not been said before.” I’ve only curated, edited, and shaped the work of others before me. The timeless, broad ideas in these volumes are for my children and their children and their children’s children. In creating them, I hope to allow others to approach problems with clarity and confidence, helping to make their journey through life more successful and rewarding

New York: Random House, 2010. Anti-fragility How do we benefit from the uncertainty of a world we don’t understand, one dominated by “fat tails”? The answer to this was provided by Nassim Taleb in a book curiously titled Antifragile. Here is the core of the idea. We can think about three categories of objects: Ones that are harmed by volatility and unpredictability, ones that are neutral to volatility and unpredictability, and finally, ones that benefit from it. The latter category is antifragile—like a package that wants to be mishandled. Up to a point, certain things benefit from volatility, and that’s how we want to be. Why? Because the world is fundamentally unpredictable and volatile, and large events—panics, crashes, wars, bubbles, and so on—tend to have a disproportionate impact on outcomes. There are two ways to handle such a world: try to predict, or try to prepare.


pages: 312 words: 91,835

Global Inequality: A New Approach for the Age of Globalization by Branko Milanovic

"Robert Solow", Asian financial crisis, assortative mating, Berlin Wall, bitcoin, Black Swan, Branko Milanovic, Capital in the Twenty-First Century by Thomas Piketty, centre right, colonial exploitation, colonial rule, David Ricardo: comparative advantage, deglobalization, demographic transition, Deng Xiaoping, discovery of the americas, European colonialism, Fall of the Berlin Wall, Francis Fukuyama: the end of history, full employment, Gini coefficient, Gunnar Myrdal, income inequality, income per capita, invisible hand, labor-force participation, liberal capitalism, low skilled workers, Martin Wolf, means of production, mittelstand, moral hazard, Nash equilibrium, offshore financial centre, oil shock, open borders, Paul Samuelson, place-making, plutocrats, Plutocrats, post scarcity, post-industrial society, profit motive, purchasing power parity, Ralph Nader, Second Machine Age, seigniorage, Silicon Valley, Simon Kuznets, special economic zone, stakhanovite, trade route, transfer pricing, very high income, Vilfredo Pareto, Washington Consensus, women in the workforce

In perhaps 99 out of 100 cases, we are likely to be wrong. And even in the 1 case out of 100 where we happen to be right, the value of that guess will be considered to result more from pure chance than from any genuine ability to extract from the past and predict the future. These singular events will remain totally outside our predictive ability, just like the appearance of black swans, as popularized in Nassim Taleb’s recent book The Black Swan (2007). And since we cannot believe that they will cease to occur in the future, it simply means that all our predictions will largely be faulty. Although we cannot predict any particular event that might occur in the next century, we can consider some possible scenarios that could change the economic composition of entire continents or even the world: Nuclear war between the United States and Russia or China that could lead to massive destruction and long-lasting radioactive contamination.

For it is only thus that massive income differences between people with approximately the same abilities can be explained. As in tennis, a tiny difference in skill level is sufficient to make one person number one in the world, earning millions, and another person number 150, covering the costs out of his own (or more likely his parents’) pocket in order to participate in tournaments. A useful way to visualize the winner-take-all rule is to think of the scalability of different jobs. As Nassim Taleb writes in Black Swan, scalable jobs are those where a person’s same unit of labor can be sold many times over.5 A typical example is that of a top pianist who in the past could sell her ability only to those who would come to listen to her. Then, with the invention of the record player, she could sell it to all who would buy the recordings; today, via the Internet, YouTube, and webcasting, she can sell it to practically the entire globe.

Thus, the massive wage differences that exist within the same types of jobs are a combination of (1) technological change, which makes jobs in principle scalable (without the ability to record sound, a pianist’s performance would not be scalable) and (2) globalization, that is, the ability to reach every corner of the world. As I discussed in Chapter 2, we see here again that the effects of technological change and globalization cannot be readily separated: the two, while conceptually distinct, go together. Perhaps the most important change will continue to be the increasing number of activities that are scalable. In Black Swan, Taleb gives the examples of a sex-worker and a cook as people whose activities are not scalable. But this is no longer necessarily the case. Entire industries have grown up on the Internet with people advertising their own nudity or teaching cooking, and doing this for thousands of fee-paying viewers simultaneously.6 The point is: technology has tremendously expanded the ability of sex workers, cooks, personal trainers, teachers, and many others to sell their services: a rival good has become nonrival.


pages: 342 words: 94,762

Wait: The Art and Science of Delay by Frank Partnoy

algorithmic trading, Atul Gawande, Bernie Madoff, Black Swan, blood diamonds, Cass Sunstein, Checklist Manifesto, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, Daniel Kahneman / Amos Tversky, delayed gratification, Flash crash, Frederick Winslow Taylor, George Akerlof, Google Earth, Hernando de Soto, High speed trading, impulse control, income inequality, information asymmetry, Isaac Newton, Long Term Capital Management, Menlo Park, mental accounting, meta analysis, meta-analysis, MITM: man-in-the-middle, Nick Leeson, paper trading, Paul Graham, payday loans, Ralph Nader, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, six sigma, Spread Networks laid a new fibre optics cable between New York and Chicago, Stanford marshmallow experiment, statistical model, Steve Jobs, The Market for Lemons, the scientific method, The Wealth of Nations by Adam Smith, upwardly mobile, Walter Mischel

Shafir also has studied, with economist Richard Thaler, some of the puzzles of how and why we delay gratification. Eldar Shafir and Richard Thaler, “Invest Now, Drink Later, Spend Never: On the Mental Accounting of Delayed Consumption,” Journal of Economic Psychology 27(5, 2006): 694–712. 3. Nassim Taleb in particular has demonstrated that human beings make all sorts of cognitive mistakes in assessing risk. See Nassim Nicholas Taleb, Fooled by Randomness: The Hidden Role of Chance in Life and Markets (Random House, 2008), and Taleb, The Black Swan. 4. Men appear to be more overconfident than women about their trading: one study shows that men trade 45 percent more than women, which costs them almost a full percentage point in terms of their net annual returns. See Brad M. Barber and Terrance Odean, “Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment,” Quarterly Journal of Economics (February 2010): 261–292. 5.

The contingency we have not considered seriously looks strange; what looks strange is thought impossible; what is improbable need not be considered seriously.”20 These unconsidered contingencies are what Donald Rumsfeld, the former secretary of defense, referred to as “unknown unknowns.” They are what the economist Frank Knight labeled unquantifiable “uncertainty” (as opposed to risk, which can be measured). Or what Nassim Taleb called “black swans.” Or what the German military theorist Carl von Clausewitz wrote was the inevitability of surprise. Or what Charles Perrow labeled unanticipated “normal accidents.” They are what Socrates meant when he said, “I neither know nor think that I know.”21 For centuries, these leading thinkers and other people we have met in this book have told us not to jump to firm conclusions about the unknown.

Most recently, the financial crisis was caused in part by overreliance on statistical models that didn’t take into account the chances of declines in housing prices. But that was just the most recent iteration: the collapse of Enron, the implosion of the hedge fund Long-Term Capital Management, the billions of dollars lost by rogue traders Kweku Adoboli, Jerome Kerviel, Nick Leeson, and others—all of these fiascos have, at their heart, a mistaken reliance on complex math. Nassim N. Taleb has written widely and wisely about the deception in financial models, most notably in his book The Black Swan: The Impact of the Highly Improbable (Random House, 2007). In retrospect, many economic models look absurd. For my college honors thesis, I wrote computer code, using something called “simplicial algorithms,” that was supposed to accurately depict Mexico’s economy. Though not particularly useful, the model looked cool and I received credit for the thesis.


Global Catastrophic Risks by Nick Bostrom, Milan M. Cirkovic

affirmative action, agricultural Revolution, Albert Einstein, American Society of Civil Engineers: Report Card, anthropic principle, artificial general intelligence, Asilomar, availability heuristic, Bill Joy: nanobots, Black Swan, carbon-based life, cognitive bias, complexity theory, computer age, coronavirus, corporate governance, cosmic microwave background, cosmological constant, cosmological principle, cuban missile crisis, dark matter, death of newspapers, demographic transition, Deng Xiaoping, distributed generation, Doomsday Clock, Drosophila, endogenous growth, Ernest Rutherford, failed state, feminist movement, framing effect, friendly AI, Georg Cantor, global pandemic, global village, Gödel, Escher, Bach, hindsight bias, Intergovernmental Panel on Climate Change (IPCC), invention of agriculture, Kevin Kelly, Kuiper Belt, Law of Accelerating Returns, life extension, means of production, meta analysis, meta-analysis, Mikhail Gorbachev, millennium bug, mutually assured destruction, nuclear winter, P = NP, peak oil, phenotype, planetary scale, Ponzi scheme, prediction markets, RAND corporation, Ray Kurzweil, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, Singularitarianism, social intelligence, South China Sea, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, Tunguska event, twin studies, uranium enrichment, Vernor Vinge, War on Poverty, Westphalian system, Y2K

But preventing the Challenger disaster would have required, not attending to the problem with the 0-rings, but attending to every warning sign which seemed as severe as the 0-ring problem, without benefit of hindsight. 5 .4 Black Swans Taleb (2005) suggests that hindsight bias and availability bias bear primary responsibility for our failure to guard against what he calls Black Swans. Black Swans are an especially difficult version of the problem of the fat tails: sometimes most of the variance in a process comes from exceptionally rare, exceptionally huge events. Consider a financial instrument that earns $10 with 98% probability, but loses $1000 with 2% probability; it is a poor net risk, but it looks like a steady winner. Taleb (2001) gives the example of a trader whose strategy worked for 6 years without a single bad quarter, yielding close to $80 million - then lost $300 million in a single catastrophe.

Then why are we so taken aback when Black Swans occur? Why did LTCM borrow a leverage of $125 billion against $4.72 billion of equity, almost ensuring that any Black Swan would destroy them? Because of hindsight bias, we learn overly specific lessons. After September 1 1 , the U S Federal Aviation Administration prohibited box-cutters on airplanes. The hindsight bias rendered the event too predictable in retrospect, permitting the angry victims to find it the result of 'negligence' such as intelligence agencies' failure to distinguish warnings of AI Qaeda activity amid a thousand other warnings. We learned not to allow hijacked planes to overfly our cities. We did not learn the lesson: 'Black Swans do occur; do what you can to prepare for the unanticipated.' Taleb (2005) writes: It is difficult to motivate people in the prevention of Black Swans . . .

N . 216 Bin Laden, 0. 417-18 biodiversity ix biodiversity cycles 256 biodiversity preservation 20 biological weapons 453-4 difference from other weapons of mass destruction 454-5 use of nanoscale technology 484 Biological Weapons: Limiting the Threat, Lederberg, J. 475 biosecurity threats ix, 22-4 biosphere, destruction of 34, 35 biotechnology 450-2 dual-use challenges 455-8 Luddite apocalypticism 81-2 micro- and molecular biology 458-60 rapidity of progress 454-5 risk management 460, 474-5 DNA synthesis technology 463-4, 465 international regulations 464 multi-stakeholder partnerships 462-3 533 novel pathogens 464-5 oversight of research 460-2 'soft' oversight of research 462 use of nanoscale technology 484 bioterrorism 82, 407, 451-2, 456-7 catastrophic attacks 466-8 infectious disease surveillance 469-70 prevention, inverse cost-benefit analysis 188-9 BioWatch 469 Bird, K. and Sherwin, M . J . 403 Black, F . 301 The Black Book of Communism: Crimes, Terror, Repression, Courtois, S . et a!. 518 Black Death 290, 294, 295 black holes 41 risk from particle accelerators 348-50 The Black Swan: The Impact ofthe Highly Improbable, Taleb, N. 162 Black Swans 94-5, 180 Blair, B. 383-4 blast, nuclear explosions 386 block-assembly operation, nanofactories 497 Blood Music, G. Bear 358 Bomb Scare: The History and Future of Nuclear Weapons, Cirincione, ) . 401 bonobos, evolution 56 Bostrom, N. 103, 121, 1 30, 1 36, 138-9, 318, 512 Anthropic Bias: Observation Selection Effects 141 bottom up climate models 266 botulinum toxin 456 Bovine Spongiform Encephalitis (BSE) 303 brain acceleration of 331 evolution 56-7 gene regulation 58 brain function, gene changes 58, 61 brain scans, value in totalitarianism 5 1 1 brain size, increase 365 Brave New World, Huxley, A. 512 'breakout', technological 360 Brenner, L.A. et a!.


pages: 379 words: 99,340

The Revolt of the Public and the Crisis of Authority in the New Millennium by Martin Gurri

Affordable Care Act / Obamacare, Albert Einstein, anti-communist, Arthur Eddington, Ayatollah Khomeini, bitcoin, Black Swan, Burning Man, business cycle, citizen journalism, Climategate, Climatic Research Unit, collective bargaining, creative destruction, crowdsourcing, currency manipulation / currency intervention, dark matter, David Graeber, death of newspapers, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, Francis Fukuyama: the end of history, Frederick Winslow Taylor, full employment, housing crisis, income inequality, Intergovernmental Panel on Climate Change (IPCC), invention of writing, job-hopping, Mohammed Bouazizi, Nate Silver, Occupy movement, Port of Oakland, Republic of Letters, Ronald Reagan, Skype, Steve Jobs, the scientific method, The Signal and the Noise by Nate Silver, too big to fail, traveling salesman, University of East Anglia, urban renewal, War on Poverty, We are the 99%, WikiLeaks, young professional

The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t. The Penguin Press, 2012. Sorkin, Andrew. Too Big To Fail: The Inside Story of How Wall Street and Washington Fought To Save the Financial System – And Themselves. Penguin Books, 2009. Sreberny, Annabelle, and Khiabany, Gholam. Blogistan: The Internet and Politics in Iran. I.B. Tauris, 2011. Taleb, Nassim Nicholas. Antifragile: Things That Gain From Disorder. Random House, 2012. Taleb, Nassim Nicholas. The Black Swan: The Impact of the Highly Improbable. Random House, 2007. Taleb, Nassim Nicholas. Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Random House Trade Paperbacks, 2004. Taylor, Frederick Winslow. Principles of Scientific Management. A public domain book, 1911. Tetlock, Philip E. Expert Political Judgment: How Good Is It? How Can We Know? Princeton University Press, 2005.

[233] Anthony Olcott, “All That Is Solid Melts Into Air,” Institute For the Study of Diplomacy, May 2010, http://www.academia.edu/662725/All_That_Is_Solid_Melts_Into_Air. [234] Scott, Seeing Like a State, 81. [235] Bureau of Labor Statistic’s “How the Government Measures Unmployment,” http://www.bls.gov/cps/cps_htgm.htm. [236] Charles Dickens, Bleak House, Chapter 5: “Telescopic Philanthropy,” http://www.worldwideschool.org/library/books/lit/charlesdickens/BleakHouse/chap4.html. [237] N. N. Taleb, The Black Swan: The Impact of the Highly Improbable (Random House, 2007), 34-35. [238] The most lucid and readable book on networks and small worlds, in my judgment, is still Albert-Laszlo Barabasi’s Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life (2002). [239] My chart. [240] Ed O’Keefe, “How many .gov sites exist? Thousands.”, Washington Post, December 20, 2011, http://www.washingtonpost.com/blogs/federal-eye/post/how-many-gov-sites-exist-thousands/2011/12/20/gIQAkGAG7O_blog.html

Yet any feature I might depict in my portrait of the public can be falsified by some example, and any attempt I might make to simplify or personalize the subject will result in caricature and error. The most promising way forward, it seems to me, is to follow N. N. Taleb’s “subtractive knowledge” method of analyzing complex questions. Rather than assert what the public is, I explain what the public is not. This resembles the sculptor’s approach of chipping away at the stone until a likeness emerged, or the bond trader’s formula of identifying safe investments by subtracting risk.[29] Since the public is an unstable and undetermined entity – a complex system – this negative mode of characterizing its behavior is least likely to fall into the fallacy of personification, of inventing some new Marxian-style “class” with a single consciousness and will. Taleb’s method is also helpful because the term in question, “the public,” has been made to stand for so many things that it had become obscured under layers of confusion and special pleading.


pages: 483 words: 141,836

Red-Blooded Risk: The Secret History of Wall Street by Aaron Brown, Eric Kim

activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, Asian financial crisis, Atul Gawande, backtesting, Basel III, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Black Swan, business cycle, capital asset pricing model, central bank independence, Checklist Manifesto, corporate governance, creative destruction, credit crunch, Credit Default Swap, disintermediation, distributed generation, diversification, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, experimental subject, financial innovation, illegal immigration, implied volatility, index fund, Long Term Capital Management, loss aversion, margin call, market clearing, market fundamentalism, market microstructure, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, natural language processing, open economy, Pierre-Simon Laplace, pre–internet, quantitative trading / quantitative finance, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, The Myth of the Rational Market, Thomas Bayes, too big to fail, transaction costs, value at risk, yield curve

You must manage them even though you don’t know what might happen, you don’t know how your actions will influence what might happen, and you certainly can’t assign probabilities. This may seem like an obvious insight, and perhaps it is. But many people disagree. A common approach by quantitative professionals is to deal only with the first kind of analysis, based on the assumption that events you know nothing about shouldn’t affect your decisions. At the other extreme, Nassim Taleb in Fooled by Randomness and The Black Swan has argued that only the second kind of analysis matters; the first is fraudulent, because long-term outcomes are dominated by unexpected, high-impact events. A more popular but less intellectual version of Nassim’s argument is to make choices according to whim or tradition or gut instinct because careful analysis and planning seem to fail so often. This idea happens to be the topic of my dissertation written in 1982 at the University of Chicago Graduate School of Business (now the Booth School of Business).

I have picked the book most directly relevant to mine and leave it to you to select among their other books if you are so inclined. By that logic I had to regretfully omit my other books (but they’re great). In addition, I have picked mostly recent books and relatively obscure books. The classics everyone likes are easy to find. I start with the books that take on the subject of Red-Blooded Risk most directly. Nassim Taleb is best known for his Black Swan, but his earlier book, Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets, tackles many of the same issues as my book, with somewhat different results. We more or less agree on the problem, but go in opposite directions to find solutions. A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation by Richard Bookstaber comes at things from a third direction.

See also Probability betting/probability and foundation of frequentism and in history/law “prior beliefs” and risk defining capital technology startups and Beat the Dealer (Thorp) Beat the Market (Thorp and Kassouf) Behavioral Game Theory (Camerer) Bennet, Rick Bernoulli, Jakob Berns, Gregory Bernstein, Peter Betting: Kelly bets probability and public sports Beyond Counting (Grosjean) Beyond Individual Choice (Bacharach) Big Short, The (Lewis) Black, Alethea Black, Fischer Black-Scholes-Merton model Black Swan, The (Taleb) Black Wednesday Bloom, Murray Teigh Bogle, John Bond ratings Bookstaber, Richard Born Losers (Sandage) Bounds of Reason, The (Gintis) Brenner, Reuven and Gabrielle Bringing Down the House (Mezrich) British Treasury Broke, (Adams) Bronze Age Bronze Age Economics (Earle) Bubble investors Bulls, Bears, and Brains (Leitzes) Burton, Robert Alan Business Cycles and Equilibrium (Black) Busting Vegas (Mezrich) Calvet, Laurent E.


pages: 654 words: 191,864

Thinking, Fast and Slow by Daniel Kahneman

Albert Einstein, Atul Gawande, availability heuristic, Bayesian statistics, Black Swan, Cass Sunstein, Checklist Manifesto, choice architecture, cognitive bias, complexity theory, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, delayed gratification, demand response, endowment effect, experimental economics, experimental subject, Exxon Valdez, feminist movement, framing effect, hedonic treadmill, hindsight bias, index card, information asymmetry, job satisfaction, John von Neumann, Kenneth Arrow, libertarian paternalism, loss aversion, medical residency, mental accounting, meta analysis, meta-analysis, nudge unit, pattern recognition, Paul Samuelson, pre–internet, price anchoring, quantitative trading / quantitative finance, random walk, Richard Thaler, risk tolerance, Robert Metcalfe, Ronald Reagan, Shai Danziger, Supply of New York City Cabdrivers, The Chicago School, The Wisdom of Crowds, Thomas Bayes, transaction costs, union organizing, Walter Mischel, Yom Kippur War

Part 3 Overconfidence The Illusion of Understanding The trader-philosopher-statistician Nassim Taleb could also be considered a psychologist. In The Black Swan, Taleb introduced the notion of a narrative fallacy to describe how flawed stories of the past shape our views of the world and our expectations for the future. Narrative fallacies arise inevitably from our continuous attempt to make sense of the world. The explanatory stories that people find compelling are simple; are concrete rather than abstract; assign a larger role to talent, stupidity, and intentions than to luck; and focus on a few striking events that happened rather than on the countless events that failed to happen. Any recent salient event is a candidate to become the kernel of a causal narrative. Taleb suggests that we humans constantly fool ourselves by constructing flimsy accounts of the past and believing they are true.

The difficulties of statistical thinking contribute to the main theme of Part 3, which describes a puzzling limitation of our mind: our excessive confidence in what we believe we know, and our apparent inability to acknowledge the full extent of our ignorance and the uncertainty of the world we live in. We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events. Overconfidence is fed by the illusory certainty of hindsight. My views on this topic have been influenced by Nassim Taleb, the author of The Black Swan. I hope for watercooler conversations that intelligently explore the lessons that can be learned from the past while resisting the lure of hindsight and the illusion of certainty. The focus of part 4 is a conversation with the discipline of economics on the nature of decision making and on the assumption that economic agents are rational. This section of the book provides a current view, informed by the two-system model, of the key concepts of prospect theory, the model of choice that Amos and I published in 1979.

In your network of associationsmals in co, anger and lack of punctuality are linked as an effect and its possible cause, but there is no such link between anger and the idea of expecting caterers. A coherent story was instantly constructed as you read; you immediately knew the cause of Fred’s anger. Finding such causal connections is part of understanding a story and is an automatic operation of System 1. System 2, your conscious self, was offered the causal interpretation and accepted it. A story in Nassim Taleb’s The Black Swan illustrates this automatic search for causality. He reports that bond prices initially rose on the day of Saddam Hussein’s capture in his hiding place in Iraq. Investors were apparently seeking safer assets that morning, and the Bloomberg News service flashed this headline: U.S. TREASURIES RISE; HUSSEIN CAPTURE MAY NOT CURB TERRORISM. Half an hour later, bond prices fell back and the revised headline read: U.S.


Super Thinking: The Big Book of Mental Models by Gabriel Weinberg, Lauren McCann

affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Gödel, Escher, Bach, hindsight bias, housing crisis, Ignaz Semmelweis: hand washing, illegal immigration, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, lateral thinking, loss aversion, Louis Pasteur, Lyft, mail merge, Mark Zuckerberg, meta analysis, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nuclear winter, offshore financial centre, p-value, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, survivorship bias, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, Vilfredo Pareto, wikimedia commons

One thing to watch out for in this type of analysis is the possibility of black swan events, which are extreme, consequential events (that end in things like financial ruin), but which have significantly higher probabilities than you might initially expect. The name is derived from the false belief, held for many centuries in Europe and other places, that black swans did not exist, when in fact they were (and still are) common birds in Australia. As applied to decision tree analysis, a conservative approach would be to increase your probability estimates of low-probability but highly impactful scenarios like the bankruptcy one. This revision would account for the fact that the scenario might represent a black swan event, and that you might therefore be wrong about its probability. One reason that the probability of black swan events may be miscalculated relates to the normal distribution (see Chapter 5), which is the bell-curve-shaped probability distribution that explains the frequency of many natural phenomena (e.g., people’s heights).

Think of the stodgy person at your office or school who is always talking about the “way it’s always been done,” constantly anxious about change and new technology. That person embodies the Shirky principle. You do not want to be that person. Inertia in beliefs and behaviors allows entrenched ideas and organizations to persist for long periods of time. The Lindy effect is the name of this phenomenon. It was popularized by Nassim Taleb in his book Antifragile, which we mentioned in Chapter 1. Taleb explains: If a book has been in print for forty years, I can expect it to be in print for another forty years. But, and that is the main difference, if it survives another decade, then it will be expected to be in print another fifty years. This, simply, as a rule, tells you why things that have been around for a long time are not “aging” like persons, but “aging” in reverse.

One reason that the probability of black swan events may be miscalculated relates to the normal distribution (see Chapter 5), which is the bell-curve-shaped probability distribution that explains the frequency of many natural phenomena (e.g., people’s heights). In a normal distribution, rare events occur on the tails of the distribution (e.g., really tall or short people), far from the middle of the bell curve. Black swan events, though, often come from fat-tailed distributions, which literally have fatter tails, meaning that events way out from the middle have a much higher probability when compared with a normal distribution. Fat-Tailed Distribution There are many naturally occurring fat-tailed distributions as well, and sometimes people just incorrectly assume they are dealing with a normal distribution when in fact they are dealing with a distribution with a fatter tail, and that means that events in the tail occur with higher probability.


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Little Bets: How Breakthrough Ideas Emerge From Small Discoveries by Peter Sims

Amazon Web Services, Black Swan, Clayton Christensen, complexity theory, David Heinemeier Hansson, deliberate practice, discovery of penicillin, endowment effect, fear of failure, Frank Gehry, Guggenheim Bilbao, Jeff Bezos, knowledge economy, lateral thinking, Lean Startup, longitudinal study, loss aversion, meta analysis, meta-analysis, PageRank, Richard Florida, Richard Thaler, Ruby on Rails, Silicon Valley, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, theory of mind, Toyota Production System, urban planning, Wall-E

The Fifth Element. New York: Doubleday, 1990. MIT’s Peter Senge has been a primary contributor to the thinking behind what it takes to build sustainable and learning organizational cultures. This is Senge’s cornerstone book. Taleb, Nassim Nicholas. The Black Swan. New York: Random House, 2007. Academic, researcher, and investor Nassim Taleb’s book is an important reminder of the impact of unlikely events. It’s a thought-provoking intellectual adventure. One of the points that Taleb highlights is that, when operating within a high degree of uncertainty, one should experiment including to find what Taleb calls inadvertent discoveries. Countless innovations have happened this way, including Alexander Fleming’s discovery of penicillin, which he found in a mold that had contaminated another experiment. Thomke, Stefan.

., Handbook of Creativity, 168 Stokes, Patricia, Creativity from Constraint, 170–71 Storyboards, 59–62, 70, 145 Strategy and innovation, further readings and resources on, 173–76 Stress Factory, New Brunswick, New Jersey, 1 Subtle controls, 85–86 Summit Partners, 111 SUN Microsystems, 10, 36 Superelaborate storyboards, 43 Surprises, 11 Sutherland, Jeff, 84, 85 Sutton, Robert, 42 Tait, Karen, 157–58 Tait, Richard, 155–58 Takeuchi, Hirotaka, 85–86 Tal Afar, Iraq, 92–94, 103, 150, 159 Taleb, Nassim Nicholas, The Black Swan, 175 Taliban, 25 Technology, 17, 20, 21, 22, 29–32, 83–91, 107–108, 112, 132, 137, 142–46, 153. See also Computers; specific technologies TEDTalks, 165 Thoen, Chris, 62, 63, 135 Thomke, Stefan, Experimentation Matters, 175 3M, 135–36, 138, 140 Tin Toy (film), 145, 146 Today Show (TV show), 125 Toyota, 44 Toyota, Yasuhisa, 81, 82 Toy Story (film), 30, 32, 146 Toy Story 2 (film), 44–45 Toy Story 3 (film), 70 Tushman (Michael L.) and O’Reilly (Charles), Winning Through Innovation, 175–76 Twitter feeds, suggested, 176–78 Tyco, 109 Uncertainty, 16–17 University of Chicago, 7 Up, 71, 105–106 US News & World Report, 78 Valley Forge Military Academy, 91 Vanier, Andre, 89–91 Vanity Fair, 118 Viacom, 146 Vietnam War, 24–25 VMWare, 107 Von Clausewitz, Carl, On War, 176 Von Hippel, Eric, 131–40, 199 The Sources of Innovation, 168 Von Hippel Strategy, 133–40 Walking around, management by, 120–21 Walkman, 108 WALL-E (film), 52, 59 Wallis, Michael, 106 Wall Street, 6 Washington, D.C., 124, 125, 126 Waterfall method, 86–88 Waterman, Bob, In Search of Excellence, 120 Weick, Karl, 141–42, 147, 148, 149–50 West, Kanye, 134 West Point, 22, 24, 91 Wilde, Oscar, 78 Will.i.am, 134 Wired magazine, 108 Wiseman, Richard, 121–24, 129, 152 The Luck Factor, 121 World War II, 25 Worm’s eye view, 104–105 Wozniak, Steve, 108 Wright, Will, 115 Xerox PARC, 12, 108 Yahoo!


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Everydata: The Misinformation Hidden in the Little Data You Consume Every Day by John H. Johnson

Affordable Care Act / Obamacare, Black Swan, business intelligence, Carmen Reinhart, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, Donald Trump, en.wikipedia.org, Kenneth Rogoff, labor-force participation, lake wobegon effect, Long Term Capital Management, Mercator projection, Mercator projection distort size, especially Greenland and Africa, meta analysis, meta-analysis, Nate Silver, obamacare, p-value, PageRank, pattern recognition, publication bias, QR code, randomized controlled trial, risk-adjusted returns, Ronald Reagan, selection bias, statistical model, The Signal and the Noise by Nate Silver, Thomas Bayes, Tim Cook: Apple, wikimedia commons, Yogi Berra

As the Wall Street Journal noted in an article about coincidences in lottery drawings, “with millions of people choosing numbers in hundreds of lotteries around the world each week, coincidences are bound to happen.”43 Consider the black swan. A few hundred years ago, people assumed that the existence of a black swan was impossible, simply because they hadn’t seen any evidence of one before. But not seeing a black swan doesn’t mean it doesn’t exist, just that we haven’t seen it—yet. Today, a “black swan” event is something that is highly improbable, yet has a massive impact when it occurs; the term was popularized by Nassim Nicholas Taleb, who has written extensively about the topic of uncertainty. Just because it hasn’t happened yet doesn’t mean it can’t—or won’t—happen. Black swans exist.44 ARE YOU SURE? So far, we’ve focused primarily on statistical concepts. But there are certainly many psychological factors that can play a role in forecasting.

James Fallows, “When a 1-in-a-Billion Chance of Accident May Not Seem ‘Safe Enough,’”Atlantic website, March 28, 2014, http://www.theatlantic.com/technology/archive/2014/03/when-a-1-in-a-billion-chance-of-accident-may-not-seem-safe-enough/359780/. 43. Carl Bialik, “Odds Are, Stunning Coincidences Can Be Expected,” Wall Street Journal website, updated September 24, 2009, http://www.wsj.com/articles/SB125366023562432131, accessed August 2, 2015. 44. Taleb cites the rise of the Internet and the events of September 11, 2001, as examples of events with black swan characteristics in his book The Black Swan: The Impact of the Highly Improbable, 2nd ed., with a new section: “On Robustness and Fragility” (Incerto), Random House (2010). 45. Brad M. Barber and Terrance Odean, “Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors,” Journal of Finance LV (April 2, 2000), http://citeseerx.ist.psu.edu/viewdoc/download?

., the total number of votes in a state are aggregated to determine who receives that state’s Electoral College votes) Average—A type of summary statistic (usually the mean, mode, or median) that describes the data in a single metric Big data—Data that’s too big for people to process without the use of sophisticated machinery or computing capacity, given its enormous volume Bivariate relationship—A fancy way of saying that there is a relationship between two (“bi”) variables (“variate”) (e.g., the price of your house is related to the number of bathrooms it has) Black swan event—Something that is highly improbable, yet has a massive impact when it occurs Causation—A relationship where it is determined that one factor causes another factor Cherry-picking—Choosing anecdotal examples from the data to make your point, while ignoring other data points that may contradict it Confidence interval—A way to measure the level of statistical certainty about results; typically expressed as a range of values, the confidence interval tells you the range of values within which you’re likely to see the estimate (assuming, of course, you have a random—and representative—sample) Confidence level—The term we use to determine how confident we are that we’re measuring the data correctly Confirmation bias—The tendency to interpret data in a way that reinforces your preconceptions Correlation—A type of statistical relationship between two variables, usually defined as positive (moving in the same direction) or negative (moving in opposite directions) Data—Information or facts Dependence—When one variable is said to be directly determined by another Deterministic forecast—A forecast for which you determine a precise outcome (e.g., it will rain tomorrow at 9 a.m. at my house) Economic impact—How much something is going to cost in terms of time, money, health, or other resources Estimate—A statistic capturing an inference about a population from a sample of data Everydata—The term we use to describe everyday data External validity—The extent to which the results from your sample can be extended to draw meaningful conclusions about the full population False positive—A situation in which the statistical forecast predicts an untrue outcome (e.g., your credit card company calls you suspecting a recent purchase you actually made was fraudulent) Forecast—A statement about the future; while forecast and prediction may have different meanings to specific groups of people (see chapter 8), we generally use them synonymously unless noted otherwise Forecast bias—The term used to describe when a prediction is consistently high (a positive forecast bias) or low (a negative bias) Inference—The process of making statistical conclusions about the data Magnitude—Essentially, the size of the effect Margin of error—A way to measure statistical uncertainty Mean—What most people think of when you say “average” (to get the mean, you add up all the values, then divide by the number of data points) Median—The middle value in a data set that has been rank ordered Misrepresentation—When data is portrayed in an inaccurate or misleading manner Mode—The data point (or points) most frequently found in your data Observation—Looking at one unit, such as a person, a price, or a day Odds—In statistics, the odds of something happening is the ratio of the probability of an outcome to the probability that it doesn’t occur (e.g., a horse’s statistical odds of winning a race might be ⅓, which means it is probable that the horse will win one out of every three races; in betting jargon, the odds are typically the reverse, so this same horse would have 2–1 odds against, which means it has a ⅔ chance of losing) Omitted variable—A variable that plays a role in a relationship, but may be overlooked or otherwise not included; omitted variables are one of the primary reasons why correlation doesn’t equal causation Outlier—A particular observation that doesn’t fit; it may be much higher (or lower) than all the other data, or perhaps it just doesn’t fall into the pattern of everything else that you’re seeing P-hacking—Named after p-values, p-hacking is a term for the practice of repeatedly analyzing data, trying to find ways to make nonsignificant results significant P-value—A way to measure statistical significance; the lower your p-value is, the less likely it is that the results you’re seeing are due to chance Population—The entire set of data or observations that you want to study and draw inferences about; statisticians rarely have the ability to look at the entire population in a study, although it could be possible with a small, well-defined group (e.g., the voting habits of all 100 U.S. senators) Prediction—See forecast Prediction error—A way to measure uncertainty in the future, essentially by comparing the predicted results to the actual outcomes, once they occur Prediction interval—The range in which we expect to see the next data point Probabilistic forecast—A forecast where you determine the probability of an outcome (e.g., there is a 30 percent chance of thunderstorms tomorrow) Probability—The likelihood (typically expressed as a percentage, fraction, or decimal) that an outcome will occur Proxy—A factor that you believe is closely related (but not identical) to another difficult-to-measure factor (e.g., IQ is a proxy for innate ability) Random—When an observed pattern is due to chance, rather than some observable process or event Risk—A term that can mean different things to different people; in general, risk takes into account not only the probability of an event, but also the consequences Sample—Part of the full population (e.g., the set of Challenger launches with O-ring failures) Sample selection—A potential statistical problem that arises when the way a sample has been chosen is directly related to the outcomes one is studying; also, sometimes used to describe the process of determining a sample from a population Sampling error—The uncertainty of not knowing if a sample represents the true value in the population or not Selection bias—A potential concern when a sample is comprised of those who chose to participate, a factor which may bias the results Spurious correlation—A statistical relationship between two factors that has no practical or economic meaning, or one that is driven by an omitted variable (e.g., the relationship between murder rates and ice cream consumption) Statistic—A numeric measure that describes an aspect of the data (e.g., a mean, a median, a mode) Statistical impact—Having a statistically significant effect of some undetermined size Statistical significance—A probability-based method to determine whether an observed effect is truly present in the data, or just due to random chance Summary statistic—Metric that provides information about one or more aspects of the data; averages and aggregated data are two examples of summary statistics Weighted average—An average calculated by assigning each value a weight (based on the value’s relative importance) NOTES Preface 1.


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The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, zero-sum game

Outside of AI and cognitive science, the most common objections to machine learning are variants of this claim. Nassim Taleb hammered on it forcefully in his book The Black Swan. Some events are simply not predictable. If you’ve only ever seen white swans, you think the probability of ever seeing a black one is zero. The financial meltdown of 2008 was a “black swan.” It’s true that some things are predictable and some aren’t, and the first duty of the machine learner is to distinguish between them. But the goal of the Master Algorithm is to learn everything that can be known, and that’s a vastly wider domain than Taleb and others imagine. The housing bust was far from a black swan; on the contrary, it was widely predicted. Most banks’ models failed to see it coming, but that was due to well-understood limitations of those models, not limitations of machine learning in general.

., 138–139 Baldwin effect, 139, 140, 304 Bandit problems, 129–130 Barto, Andy, 221 Bayes, Thomas, 144–145 Bayesian learning, 166–170, 174–175 Bayesian methods, cell model and, 114 Bayesian model averaging, 166–167 Bayesian models, tweaking probabilities, 170–173 Bayesian networks, 24, 156–161, 305–306 Alchemy and, 250 gene regulation and, 159 inference problem and, 161–166 Master Algorithm and, 240, 245 relational learning and, 231 Bayesians, 51, 52–53, 54, 143–175 Alchemy and, 253 further reading, 304–305 hidden Markov model, 154–155 If . . . then . . . rules and, 155–156 inference problem, 161–166 learning and, 166–170 logic and probability and, 173–175 Markov chain, 153–155 Markov networks, 170–173 Master Algorithm and, 240–241, 242 medical diagnosis and, 149–150 models and, 149–153 nature and, 141 probabilistic inference and, 52, 53 See also Bayesian networks Bayes’ theorem, 31–32, 52–53, 143–149, 253 Beam search, 135 “Beer and diapers” rule, 69–70 Belief, probability and, 149 Belief propagation, 161–164, 242, 253 Bell Labs, 190 Bellman, Richard, 188, 220 Bellman’s equation, 220 Berkeley, George, 58 Berlin, Isaiah, 41 Bias, 78–79 Bias-free learning, futility of, 64 Bias-variance decomposition, 301 The Bible Code (Drosnin), 72 Big data, 21 A/B testing and, 227 algorithms and, 7 clustering and, 206–207 relational learning and, 232–233 science, machine learning, and, 14–16 scientific truth and, 40 Big-data systems, 258 Bing, 12 Biology, learning algorithms and, 15 Black swans, 38–39, 158, 232 The Black Swan (Taleb), 38 Blessing of nonuniformity, 189 Board games, reinforcement learning and, 219 Bohr, Niels, 178, 199 Boltzmann distribution, 103–104 Boltzmann machines, 103–104, 117, 250 Boole, George, 104, 175 Boolean circuits, 123, 136 Boolean variable, 149 Boosting, 238 Borges, Jorge Luis, 71 Box, George, 151 Brahe, Tycho, 14, 131 Brahe phase of science, 39–40 Brain learning algorithms and, 26–28 mapping, 118 number of connections in, 94–95 reverse engineering the, 52, 302 S curves and, 105 simulating with computer, 95 spin glasses and, 102–103 BRAIN initiative, 118 Breiman, Leo, 238 Brin, Sergey, 55, 227, 274 Bryson, Arthur, 113 Bucket brigade algorithm, 127 Building blocks, 128–129, 134 Buntine, Wray, 80 Burglar alarms, Bayesian networks and, 157–158 Burks, Arthur, 123 Burns, Bob, 206 Business, machine learning and, 10–13 C. elegans, 118 Cajal, Santiago Ramón y, 93–94 Caltech, 170 CancerCommons.org, 261 Cancer cure algorithm for, 53–54 Bayesian learning and, 174 inverse deduction and, 83–85 Markov logic network and, 249 program for (CanceRx), 259–261, 310 Cancer diagnosis, 141 Cancer drugs predicting efficacy of, 83–84 relational learning and models for, 233 selection of, 41–42 CanceRx, 259–261, 310 Capital One, 272 Carbonell, Jaime, 69 Carnap, Rudolf, 175 Cars driverless, 113, 166, 172, 306 learning to drive, 113 Case-based reasoning, 198, 307 Catch Me If You Can (film), 177 Cause and effect, Bayes’ theorem and, 145–149 Cell model of, 114–115 relational learning and workings of, 233 Cell assembly, 94 Cell phone, hidden Markov models and, 155 Centaurs, 277 Central Dogma, 83 Cerebellum, 27, 118 Chance, Bayes and, 145 Chaos, study of, 30 Checkers-playing program, 219 Cholera outbreak, London’s, 182–183 Chomsky, Noam, 36–38 Chrome, 266 Chunking, 223–227, 254, 309 Circuit design, genetic programming and, 135–136 Classes, 86–87, 209, 257 Classifiers, 86–87, 127 Master Algorithm and, 240 Naïve Bayes, 151–153 nearest-neighbor algorithm and, 183 Clinton, Bill, 18 Clustering, 205–210, 254, 257 hierarchical, 210 Cluster prototypes, 207–208 Clusters, 205–210 “Cocktail party” problem, 215 Cognition, theory of, 226 Coin toss, 63, 130, 167–168 Collaborative filtering systems, 183–184, 306–307 Columbus test, 113 Combinatorial explosion, 73–74 Commoner, Barry, 158 Commonsense reasoning, 35, 118–119, 145, 276–277, 300 Complexity monster, 5–6, 7, 43, 246 Compositionality, 119 Computational biologists, use of hidden Markov models, 155 Computers decision making and, 282–286 evolution of, 286–289 human interaction with, 264–267 as learners, 45 logic and, 2 S curves and, 105 as sign of Master Algorithm, 34 simulating brain using, 95 as unifier, 236 writing own programs, 6 Computer science, Master Algorithm and, 32–34 Computer vision, Markov networks and, 172 Concepts, 67 conjunctive, 66–68 set of rules and, 68–69 sets of, 86–87 Conceptual model, 44, 152 Conditional independence, 157–158 Conditional probabilities, 245 Conditional random fields, 172, 306 Conference on Neural Information Processing Systems (NIPS), 170, 172 Conjunctive concepts, 65–68, 74 Connectionists/connectionism, 51, 52, 54, 93–119 Alchemy and, 252 autoencoder and, 116–118 backpropagation and, 52, 107–111 Boltzmann machine and, 103–104 cell model, 114–115 connectomics, 118–119 deep learning and, 115 further reading, 302–303 Master Algorithm and, 240–241 nature and, 137–142 neural networks and, 112–114 perceptron, 96–101, 107–108 S curves and, 104–107 spin glasses and, 102–103 symbolist learning vs., 91, 94–95 Connectomics, 118–119 Consciousness, 96 Consilience (Wilson), 31 Constrained optimization, 193–195, 241, 242 Constraints, support vector machines and, 193–195 Convolutional neural networks, 117–119, 303 Cope, David, 199, 307 Cornell University, Creative Machines Lab, 121–122 Cortex, 118, 138 unity of, 26–28, 299–300 Counterexamples, 67 Cover, Tom, 185 Crawlers, 8–9 Creative Machines Lab, 121–122 Credit-assignment problem, 102, 104, 107, 127 Crick, Francis, 122, 236 Crossover, 124–125, 134–136, 241, 243 Curse of dimensionality, 186–190, 196, 201, 307 Cyber Command, 19 Cyberwar, 19–21, 279–282, 299, 310 Cyc project, 35, 300 DARPA, 21, 37, 113, 121, 255 Darwin, Charles, 28, 30, 131, 235 algorithm, 122–128 analogy and, 178 Hume and, 58 on lack of mathematical ability, 127 on selective breeding, 123–124 variation and, 124 Data accuracy of held-out, 75–76 Bayes’ theorem and, 31–32 control of, 45 first principal component of the, 214 human intuition and, 39 learning from finite, 24–25 Master Algorithm and, 25–26 patterns in, 70–75 sciences and complex, 14 as strategic asset for business, 13 theory and, 46 See also Big data; Overfitting; Personal data Database engine, 49–50 Databases, 8, 9 Data mining, 8, 73, 232–233, 298, 306.

It’s the same in any Bayesian network: to obtain the probability of a complete state, just multiply the probabilities from the corresponding lines in the individual variables’ tables. So, provided the conditional independencies hold, no information is lost by switching to the more compact representation. And in this way we can easily compute the probabilities of extremely unusual states, including states that were never observed before. Bayesian networks give the lie to the common misconception that machine learning can’t predict very rare events, or “black swans,” as Nassim Taleb calls them. In retrospect, we can see that Naïve Bayes, Markov chains, and HMMs are all special cases of Bayesian networks. The structure of Naïve Bayes is: Markov chains encode the assumption that the future is conditionally independent of the past given the present. HMMs assume in addition that each observation depends only on the corresponding state. Bayesian networks are for Bayesians what logic is for symbolists: a lingua franca that allows us to elegantly encode a dizzying variety of situations and devise algorithms that work uniformly in all of them.


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The Education of a Value Investor: My Transformative Quest for Wealth, Wisdom, and Enlightenment by Guy Spier

Albert Einstein, Atul Gawande, Benoit Mandelbrot, big-box store, Black Swan, Checklist Manifesto, Clayton Christensen, Daniel Kahneman / Amos Tversky, Exxon Valdez, Gordon Gekko, housing crisis, information asymmetry, Isaac Newton, Kenneth Arrow, Long Term Capital Management, Mahatma Gandhi, mandelbrot fractal, Nelson Mandela, NetJets, pattern recognition, pre–internet, random walk, Ronald Reagan, South Sea Bubble, Steve Jobs, winner-take-all economy, young professional, zero-sum game

But I suspect that it’s harder for people like me who flocked to New York from elsewhere and therefore lack the local roots that give emotional stability to people raised there. For outsiders, it’s too easy to get unbalanced by the unbridled appetites—including greed and envy—that financial centers like New York and London can inflame. To borrow a memorable term from Nassim Nicholas Taleb’s book The Black Swan: The Impact of the Highly Improbable, these big cities are “Extremistan.” As we know from various studies, the disparity between our own wealth and our neighbors’ wealth can play a significant role in determining our happiness. If so, then reading about a New York–based multibillionaire like the Blackstone Group’s Stephen Schwarzman may well trigger a destabilizing reaction in my irrational brain.

See also letters to shareholders Aquamarine Chemicals, 42–3 Aquamarine Fund assets reach $50 million, 49–50 and Bear Stearns, 86–7 creation and naming of, 43 cumulative return, 2 and fee structures, 46–7 and financial crisis of 2008–2009, 86–99 investment of net worth in, 46 and Lehman Brothers, 87–8 Ariely, Dan, 102 attention deficit disorder (ADD), 48, 107–8, 155 authenticity, 33, 42, 63, 66–7, 76, 81, 84, 132 Bak, Per, 105 Bank of America, 126–7 Bear Stearns, 86–7 behavioral finance, 60, 102–9, 115, 117–18, 125, 135–8, 140–1, 143, 147–50, 154, 191 behaviorism, 28 Benello, Allen, 112, 144 Berkshire Hathaway, 46, 59, 82, 142, 192 annual meetings, 41, 61, 71–2, 178 annual reports, 38–9, 63, 142 and Buffett’s salary, 73, 116 as a company rather than a fund, 94–5 decentralized structure of, 81 holdings, 40–1, 78, 125, 137, 153 and the tech bubble, 71 textile operations, 17–18 See also Buffett, Warren Bernanke, Ben, 87 Bettger, Frank, 6 Bill and Melinda Gates Foundation, 78, 185 Black Swan: The Impact of the Highly Improbable, The (Taleb), 109 Blodget, Henry, 17 Bloomberg terminal/monitor, 2, 52, 87, 116–19, 135–6, 146 Blumkin, Rose, 41–2, 177 Bogdanor, Vernon, 25 Bond Markets, Analysis and Strategies (Fabozzi), 18–19 Bosanek, Debbie, 82, 176, 181 Boulder Brands, 167–70 Brandt, Jonathan, 144, 178 Braxton Associates, 28–9 bridge, 122, 124–8, 130 Brookfield Office Properties, 97–8 Buffett, Howard, 116 Buffett, Susan, 75, 79, 174–5 Buffett, Warren, 37, 53, 55, 90 annual Berkshire salary, 73, 116 and bridge, 124 on debt, 93 father of, 116 on fear, 85 and fee structures, 46–7, 67, 74 and financial crisis of 2008–2009, 90 on first rule of investing, 52 generosity of, 115, 175–6, 178–9, 185 guest talk at HBS, 28–30, 42 and the “inner/outer scorecard,” 26, 65, 80–1 integrity of, 36 and investment decisions, 17–18, 24, 53, 90, 127, 136–7, 148, 165 on learning from mistakes, 2 Letters to Shareholders, 39, 63, 93 as life-long learner, 29–30 on love, 175 Lowenstein’s biography of, 19, 30, 63, 116, 142 lunch with, 1–2, 22, 69–84, 98 and management, 110–11, 163 mistakes of, 17–18, 153 office of, 35, 95, 111–17, 143 playfulness of, 116, 121–2, 131 preface to The Intelligent Investor, 19, 30 on reputation, 18 as role model, 39–40, 46, 63, 71, 83, 96, 113, 115–16, 122, 176 Schroeder’s biography of, 83, 142 “The Superinvestors of Graham-and-Doddsville,” 37, 82 “Too Hard” box of, 116, 170, 180 and wealth, 78, 82, 188 See also Berkshire Hathaway Buffett: The Making of an American Capitalist (Lowenstein), 19, 30, 63, 116, 142 Buffett-Pabrai Way of doing business, 171–85 Burlington Coat Factory, 36 Burlington Northern Santa Fe, 137 Burns, C.

For example, the proximity of so much extreme wealth might make it more tempting for me to swing for the fences with my investments instead of focusing calmly on making a decent compounded return without undue risk. For me at least, it seemed wiser to live in a place where the differences are less extreme. Given my particular set of flaws and vulnerabilities, I figured that I would stand a better chance of operating somewhat rationally in the kind of place that Taleb describes as “Mediocristan,” where life is more mundane. So I started actively to consider alternatives to Manhattan. For a while, I thought seriously of moving to Omaha, given how well it had worked for Warren. I also considered Irvine, California, where Mohnish lives. I contemplated other American cities like Boston, Grand Rapids, and Boulder. And I thought about relatively low-key European cities such as Munich, Lyon, Nice, Geneva, and Oxford.


pages: 309 words: 81,975

Brave New Work: Are You Ready to Reinvent Your Organization? by Aaron Dignan

"side hustle", activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, Black Swan, blockchain, Buckminster Fuller, Burning Man, butterfly effect, cashless society, Clayton Christensen, clean water, cognitive bias, cognitive dissonance, corporate governance, corporate social responsibility, correlation does not imply causation, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, David Heinemeier Hansson, deliberate practice, DevOps, disruptive innovation, don't be evil, Elon Musk, endowment effect, Ethereum, ethereum blockchain, Frederick Winslow Taylor, future of work, gender pay gap, Geoffrey West, Santa Fe Institute, gig economy, Google X / Alphabet X, hiring and firing, hive mind, income inequality, information asymmetry, Internet of things, Jeff Bezos, job satisfaction, Kevin Kelly, Kickstarter, Lean Startup, loose coupling, loss aversion, Lyft, Marc Andreessen, Mark Zuckerberg, minimum viable product, new economy, Paul Graham, race to the bottom, remote working, Richard Thaler, shareholder value, Silicon Valley, six sigma, smart contracts, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, source of truth, Stanford marshmallow experiment, Steve Jobs, TaskRabbit, the High Line, too big to fail, Toyota Production System, uber lyft, universal basic income, Y Combinator, zero-sum game

What’s more, many organizations forget how potent the connection between purpose and strategy really is. If you don’t have a compelling vision—a dent in the universe beyond shareholder value—your strategies will fall flat. Because how can we win if we don’t know what winning looks like? Thought Starters Wild Swings and Sure Things. His politics and cantankerous demeanor aside, Nassim Nicholas Taleb introduced an extremely valuable concept in his book The Black Swan called the barbell strategy. This financial strategy is named for how it distributes risk—to two extremes: invest 85–90 percent of your assets in extremely safe instruments, and place all of the remainder in highly speculative bets. The reasoning holds that the “sure things” will create a floor for performance, limiting your downside risk, while the “wild swings” will expose you to potentially life-changing gains.

adjacent possible, 189, 201 Administration Industrielle et Générale (Fayol), 24 advice process, 70, 72–73 Afshar, Vala, 119 Agile Manifesto, 19, 89, 182 agility, 19, 20, 28–29 Airbnb, 140, 188, 254 al-Qaeda, 128 Amazon, 61, 86, 88, 89, 104–5, 254, 259, 268 Andreessen, Marc, 256 ants, 106 Apple, 86 Ask Me Anything sessions (AMAs), 135–36 authority, 14, 54, 63, 65–74 Automattic, 120 autonomy, 22, 42, 66–67, 74, 194, 258 Bain Capital, 253 Ballpoint, 199–200 banks, 94, 252 Handelsbanken, 13, 94, 227–28 barbell strategy, 86–87, 105–6 Barksdale, Jim, 59 Basecamp, 68, 69, 120 Bell, Alexander Graham, 103 Benchley, Robert, 39 Beyond Budgeting Institute, 97–98 Bezos, Jeff, 61 B Lab, 249, 259 Black Swan, The (Taleb), 86–87 Blank, Steve, 27 Boman, Pär, 227–28 bonuses, 171–72 boundaries, 193, 196–97 Box, George, 49 Boyd, John, 88 Braintrust, 119–20 Brandeis, Louis D., 22–23 Bridgewater Associates, 152, 153 Brin, Sergey, 136 Brookings Institution, 33 Bryant, Adam, 147 budgets, 25, 26, 27, 94–97, 99–100 Buffer, 130–31, 166, 170 bureaucracy, 26–27, 29, 68, 77, 112, 190, 193, 198, 212, 236 Burning Man, 139, 140 butterfly effect, 45 Buurtzorg, 13, 34–36, 38, 79, 105, 144, 218 Catmull, Ed, 120, 191, 192 centralization and decentralization, 77–79 CEOs, 80, 86, 223 change, 14, 28 authority in, 73–74 changing approach to, 187–91 compensation in, 172 continuous participatory, see continuous participatory change information in, 136 innovation in, 108 mastery in, 161 meetings in, 125 membership in, 149 plan for, 185–87 purpose in, 63 resistance to, 233–34 resources in, 100 strategy in, 91, 92 structure in, 81 workflow in, 116 charity: water, 224–25 Chesky, Brian, 254 Christensen, Clayton, 91, 237 Cointelegraph, 251 Colleague Letter of Understanding (CLOU), 55 commitment, 69, 193–96, 212 communities of practice, 160 compensation, 14, 54, 163–73 competence, 42 competition, 144 complexity, 43–45, 68, 79 Complexity Conscious mindset, 13, 36–37, 43–47, 53, 55–57, 190, 195, 199, 244, 258–59, 267 authority and, 74 compensation and, 173 information and, 137 innovation and, 109 mastery and, 162 meetings and, 126 membership and, 150 purpose and, 64 resources and, 101 strategy and, 90, 92 structure and, 82 workflow and, 117 complex systems, 45 adaptive, 129, 187–88 relationships and interactions in, 45, 140 compliance, 27, 46, 66, 122, 258 Cone, Sarah, 253 confidence, 236 consensus, 70 consent, 70–73, 195 continuity, 193, 218–19 continuous participatory change, 191–219 boundaries in, 193, 196–97 commitment in, 193–96 continuity in, 193, 218–19 criticality in, 193, 216–18 learning by doing in, 230–31 looping in, see looping participation in, 228–29 priming in, 193, 197–201, 236 principles for, 228–34 resistance and, 233–34 scaling of, 234–39 sensing and responding in, 231–32 starting by stopping in, 232–33 starting small in, 229–30 constraints, 46 contribution-based pay, 167 control, locus of, 154, 155 Control Inc., 181–83, 185, 196–97, 219, 220, 222 cooperatives, 250 Cornell University, 42 Corner Office, 147 Corning Inc., 103 corporations: new forms of incorporation, 248–51, 252 see also organizations Creativity, Inc.

Done right, it’s an inexpensive way to wrestle with uncertainty. To get started, gather a group with as much diversity as you can fit in the room, and ask them to generate as many possible futures or micro scenarios as possible. Cluster them and discuss the factors involved. Discuss what could be done to mitigate or navigate these scenarios. Listen closely for anything that’s too quickly dismissed—the black swans that are overlooked until it’s too late. And remember, we’re not trying to anticipate the future; that’s not Complexity Conscious. We’re trying to create awareness. Readiness. Preparedness. So that when something unexpected happens—and it will—your team is less likely to be surprised. Your OODA loop is short. And you’re ready to act. Red Team. Over time, it can be hard to separate strategy from the status quo.


pages: 354 words: 105,322

The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis by James Rickards

"Robert Solow", Affordable Care Act / Obamacare, Albert Einstein, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Bayesian statistics, Ben Bernanke: helicopter money, Benoit Mandelbrot, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, bitcoin, Black Swan, blockchain, Bonfire of the Vanities, Bretton Woods, British Empire, business cycle, butterfly effect, buy and hold, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, cellular automata, cognitive bias, cognitive dissonance, complexity theory, Corn Laws, corporate governance, creative destruction, Credit Default Swap, cuban missile crisis, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, disintermediation, distributed ledger, diversification, diversified portfolio, Edward Lorenz: Chaos theory, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, fiat currency, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, Fractional reserve banking, G4S, George Akerlof, global reserve currency, high net worth, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Isaac Newton, jitney, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Rogoff, labor-force participation, large denomination, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, money market fund, mutually assured destruction, Myron Scholes, Naomi Klein, nuclear winter, obamacare, offshore financial centre, Paul Samuelson, Peace of Westphalia, Pierre-Simon Laplace, plutocrats, Plutocrats, prediction markets, price anchoring, price stability, quantitative easing, RAND corporation, random walk, reserve currency, RFID, risk-adjusted returns, Ronald Reagan, Silicon Valley, sovereign wealth fund, special drawing rights, stocks for the long run, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transfer pricing, value at risk, Washington Consensus, Westphalian system

On September 10, 2009, I testified under oath before Congress on the role of risk-management models in the 2008 financial crisis. A fellow witness was Nassim Taleb, celebrated author of The Black Swan. In the hearing, Taleb and I said Wall Street compensation of “heads I win, tails you lose” design was a contributing factor to the crash. We testified that bankers were grossly overpaid and incentivized to reckless behavior. One free market oriented member of Congress chastised us from his high dais and said our proposals to limit compensation would keep Wall Street from attracting “talent.” Taleb’s answer was priceless: “What talent? These people destroyed ten trillion dollars of wealth.” Taleb was right. Most traders on Wall Street are not super-talented. Decamping from an investment bank to a hedge fund does not improve a trader’s talent; it just moves the compensation model in favor of the trader.

Complex dynamics exhibit memory or feedback, called path dependence. Risk in capital markets is an exponential function of scale. Small changes in initial system conditions produce divergent results. System output can be orderly or chaotic. These observations are the scientific basis for what is popularly known as a black swan event. The term “black swan” is used widely to describe any surprising headline even by those who lack a theoretical understanding of the underlying dynamics. Black swan discussion tends to trivialize science with a fatalistic tinge, as if to say “stuff happens.” Stuff doesn’t just happen. Crises emerge because regulators don’t comprehend the statistical properties of the systems they regulate. LTCM was a textbook case in ignoring complexity theory. For example, traders at LTCM frequently constructed two-sided strategies using real government notes and synthetic notes in swap form.

The difficulties of replacing trades of a bankrupt counterparty when notional amounts are in the tens of trillions of dollars, represented by thousands of contracts covering underlying instruments in stocks, bonds, commodities, and currencies, spread across the books of scores of subsidiaries and special purpose entities in multitudinous markets around the world, are extraordinary. This is why select banks are too big to fail. A single point of failure collapses the entire system. A crack-up has names like “Tipping Point,” “Black Swan,” and “Minsky Moment” given by sociologists, economists, and media. Those concepts, colorful as they may be, are not science. The dynamics of ruin are best understood using complexity theory, a hard science that offers tools to see collapse coming in advance. The term “complexity” is often used loosely as synonymous with complication or connectedness. In dynamic systems analysis, those terms have quite different meanings.


pages: 176 words: 55,819

The Start-Up of You: Adapt to the Future, Invest in Yourself, and Transform Your Career by Reid Hoffman, Ben Casnocha

Airbnb, Andy Kessler, Black Swan, business intelligence, Cal Newport, Clayton Christensen, commoditize, David Brooks, Donald Trump, en.wikipedia.org, fear of failure, follow your passion, future of work, game design, Jeff Bezos, job automation, Joi Ito, late fees, lateral thinking, Marc Andreessen, Mark Zuckerberg, Menlo Park, out of africa, Paul Graham, paypal mafia, Peter Thiel, recommendation engine, Richard Bolles, risk tolerance, rolodex, shareholder value, side project, Silicon Valley, Silicon Valley startup, social web, Steve Jobs, Steve Wozniak, Tony Hsieh, transaction costs

These results jibe with conventional wisdom—risk-averse people may be teachers or doctors (or lawyers or bankers), whereas risk-taking people may be starting companies or trying out on Broadway. But is this assumption right? The Volatility Paradox: Small Fires Prevent the Big Burn In his book The Black Swan, Nassim Taleb writes about the unexpected, rare, high-impact event. The September 11 terrorist attack, the stock market crash in 1987, and the Indian Ocean tsunami in 2004 were black swans. They were impossible to predict beforehand, had a low chance of happening in the first place, and exacted a large impact. Joshua Cooper Ramo, a friend, in his excellent book The Age of the Unthinkable, argues that we should expect to see more black swans in our lifetime. Ramo believes the number of unthinkable disruptions in the world is on the rise in part because we’ve become so globally interconnected that a minor disturbance anywhere can cause disruption everywhere.

In this sense, the world of tomorrow will be more like the Silicon Valley of today: constant change and chaos. So does that mean you should try to avoid those shocks by going into low-volatility careers like health care or teaching? Not necessarily. The way to intelligently manage risk is to make yourself resilient to these shocks by pursuing those opportunities with some volatility baked in. Taleb argues—furthering an argument popularized by ecologists who study resilience—that the less volatile the environment, the more destructive a black swan will be when it comes. Nonvolatile environments give only an illusion of stability: “Dictatorships that do not appear volatile, like, say, Syria or Saudi Arabia, face a larger risk of chaos than, say, Italy, as the latter has been in a state of continual political turmoil since the [Second World War].”5 Ramo explains why: Italy is resilient to dangerous chaos because it has absorbed frequent attacks like “small, controlled burns in a forest, clearing away just enough underbrush to make [them] invulnerable to a larger fire.”6 These small burns strengthen the political system’s capacity to respond to unexpected crises.

Jonathan Haidt, The Happiness Hypothesis: Finding Modern Truth in Ancient Wisdom (New York: Basic Books, 2006), 29. 3. Anthony Iaquinto and Stephen Spinelli Jr., Never Bet the Farm: How Entrepreneurs Take Risks, Make Decisions—and How You Can, Too (San Francisco: Jossey-Bass, 2006), 78. 4. Stephen H. Shore and Raven Saks, “Risk and Career Choice,” Advances in Economic Analysis and Policy 5, no. 1 (2005), http://​www.​bepress.​com/​bejeap/​advances/​vol5/​iss1/​art7 5. Nassim Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2010), 204. 6. Joshua Cooper Ramo, The Age of the Unthinkable: Why the New World Disorder Constantly Surprises Us and What We Can Do About It (New York: Back Bay Books, 2010), 181. 7. Ibid. 8. Aaron B. Wildavsky, Searching for Safety (Piscataway, NJ: Transaction Publishers, 2004), 98. Chapter 7 1. Bill Gates, Business @ the Speed of Thought: Using a Digital Nervous Sysem (New York: Warner Books, 1999), 3. 2.


pages: 349 words: 134,041

Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives by Satyajit Das

accounting loophole / creative accounting, Albert Einstein, Asian financial crisis, asset-backed security, beat the dealer, Black Swan, Black-Scholes formula, Bretton Woods, BRICs, Brownian motion, business process, buy and hold, buy low sell high, call centre, capital asset pricing model, collateralized debt obligation, commoditize, complexity theory, computerized trading, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, currency peg, disintermediation, diversification, diversified portfolio, Edward Thorp, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, financial innovation, fixed income, Haight Ashbury, high net worth, implied volatility, index arbitrage, index card, index fund, interest rate derivative, interest rate swap, Isaac Newton, job satisfaction, John Meriwether, locking in a profit, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Marshall McLuhan, mass affluent, mega-rich, merger arbitrage, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mutually assured destruction, Myron Scholes, new economy, New Journalism, Nick Leeson, offshore financial centre, oil shock, Parkinson's law, placebo effect, Ponzi scheme, purchasing power parity, quantitative trading / quantitative finance, random walk, regulatory arbitrage, Right to Buy, risk-adjusted returns, risk/return, Satyajit Das, shareholder value, short selling, South Sea Bubble, statistical model, technology bubble, the medium is the message, the new new thing, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, volatility smile, yield curve, Yogi Berra, zero-coupon bond

It is said that many firms constantly debate whether they should be in the business of serving clients at all rather than trading on their own account. So, dealers are off to a day at the races, every day. Black swans, black sheep There is a huge number of books on trading secrets. Victor Niederhoffer, an eccentric contrarian investor, chronicled his trading ‘secrets’ in Education of a Speculator. Niederhoffer started in the early 1980s and was managing over $100 million by the late 1990s. His clients included George Soros, he had an impeccable track record, but in 1997 large losses caused his fund to close. It seems his education wasn’t complete. Some trading books take a philosophical approach. Nassim Taleb, a philosopher of trading and quantitative finance, in his celebrated Fooled By Randomness,4 introduced traders to John Stuart Mill’s ‘black swan hypotheses’. ‘No amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.’

Chapter 4 Show me the money – greed lost and regained 1 Quoted in Francis Wheen (2004) How Mumbo Jumbo Conquered the World; Harper Perennial, London, p. 36. 2 Quoted in Frank Partnoy (2004) Infectious Greed; Owl Books, New York, p. 117. 3 Quoted in Frank Partnoy (2004) Infectious Greed; Owl Books, New York, pp. 117–118. 4 Nassim Taleb (2004) Fooled by Randomness; Texere, New York. 5 22 September 2002 Media Availability en route to Poland. 6 4 June 2002 Department of Defense News Briefing. Chapter 5 The perfect storm – risk mismanagement by the numbers 1 Tanya Styblo Beder ‘The Great Risk Hunt’ (May 1999) The Journal of Portfolio Management, p. 29. 2 Peter Bernstein (1998) Against the Gods: The Remarkable Story of Risk; John Wiley, New York. 3 See ‘The Jorion-Taleb Debate’ (April 1997) Derivatives Strategy, 25. 4 Roger Lowenstein (2002) When Genius Fails: The Rise and Fall of Long-Term Capital Management; Fourth Estate, London, p. 15. 5 For details of Salomon’s Treasury Bond trading scandal, see Nicholas Dunbar (2000) Inventing Money; John Wiley & Sons, Chichester, pp. 110–112; and Roger Lowenstein (2002) When Genius Fails: The Rise and Fall of Long-Term Capital Management; Fourth Estate, London, pp. 19–22; Frank Partnoy (2004) Infectious Greed; Owl Books, New York, pp. 97–109. 6 Quoted by Merton Miller in ‘Trillion Dollar Bet’ (8 February 2000) Nova PBS. 12_NOTES.QXD 17/2/06 4:43 pm Page 323 Notes 323 7 LTCM’s 1997 return is somewhat in dispute.

DAS_A01.QXD 5/3/07 8:01 PM Page viii Contents List of figures and tables xii Preface xiii Prologue 1 Miracles and mirages Serial crimes Beginning of the end/end of the beginning Knowns and unknowns Unreliable recollections Summary judgment 2 5 9 12 13 17 1 Financial WMDs – derivatives demagoguery 19 School days It’s all Chinese to me A derivative idea Betting shops Secret subtexts Leveraged speculations Under the radar Whole lotta swapping going on The golden age/LIBOR minus 50 Warehouses Serial killings Forbidden fruit Derived logic 2 Beautiful lies – the ‘sell’ side Smile and dial Market colour Rough trade Analyze this 21 22 23 25 27 29 32 33 37 40 43 45 50 53 55 56 59 62 DAS_A01.QXD 5/3/07 ix 8:01 PM Page ix Tr a d e r s , G u n s & M o n e y Class wars Ultra vires Feudal kingdoms Uncivil wars Golden rules Business models The medium is the message Bondage Tabloid cultures Conspicuous currency Ethnic cleansing Foreign affairs FILTH Lost in translation A day in the life 3 True lies – the ‘buy’ side Turn of the fork Risky business Magic kingdoms Stripping or stacking/hedging perils, again Me too ‘Zaiteku’ or the bride stripped bare The gamble in P & G Tobashi, baby Gnomes of Zermatt and Belgian dentists Death swaps Investment fashions Alpha, beta, zeta Looking after the relatives Agents all Unique selling propositions 4 Show me the money – greed lost and regained Money uncertainty Toll booths Take a seat Efficient markets On the platform A day at the races Black swans, black sheep Trading places 64 66 67 68 70 71 74 75 76 77 79 80 81 82 83 87 88 89 91 95 97 98 101 105 107 108 110 112 115 116 117 121 122 123 125 126 127 129 130 131 DAS_A01.QXD 5/3/07 8:01 PM Page x Contents Secret intelligence Overwhelming force Oracle of Delphi Free money The colour of money In reserve A comedy of errors Black holes What’s the number? Nothing like excess Nice work if you can get it Dukes of Hazard 5 The perfect storm – risk mismanagement by the numbers Shock therapy Holy risk!


Systematic Trading: A Unique New Method for Designing Trading and Investing Systems by Robert Carver

asset allocation, automated trading system, backtesting, barriers to entry, Black Swan, buy and hold, cognitive bias, commodity trading advisor, Credit Default Swap, diversification, diversified portfolio, easy for humans, difficult for computers, Edward Thorp, Elliott wave, fixed income, implied volatility, index fund, interest rate swap, Long Term Capital Management, margin call, merger arbitrage, Nick Leeson, paper trading, performance metric, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, survivorship bias, systematic trading, technology bubble, transaction costs, Y Combinator, yield curve

Rogue Trader, Nick Leeson, 1999, Sphere Very famous example of when negative skew goes wrong; amongst other things Nick lost much of his money selling option straddles. Optional reading. The Greatest Trade Ever, Gregory Zuckerman, 2010, Penguin A great story about a positive skew trade that worked: John Paulson’s bearish bet on mortgage backed securities. Optional reading. The Black Swan, Nassim Taleb, 2008, Penguin A book about unknown unknowns. Taleb’s usual mixture of unique philosophy and market folklore. Interesting, but optional reading. Trading rule fitting Fooled by Randomness, Nassim Taleb, 2001, Penguin A very interesting book on uncertainty in general. Compulsory for anyone who thinks back-testing is worthwhile. Trading rules and forecasts Trading Systems and Methods, 5th Edition, Perry J. Kaufman, 2013, John Wiley & Sons The bible of trading strategies. Great resource for trading rule ideas if you need them.

Although they will not use any dynamic trading rules, they will use the systematic framework to ensure that their portfolio risk remains as desired. 30. In a speech in 2002 US Secretary of State Donald Rumsfeld identified three kinds of knowledge: known knowns, known unknowns and unknown unknowns. 31. I’ll discuss exactly how you measure recent levels of standard deviation in chapter ten, ‘Position sizing’. If you can’t wait, it’s on page 155. 32. This essentially is the problem that Nassim Taleb discusses in his book The Black Swan. 39 Systematic Trading CONCEPT: VOLATILITY STANDARDISATION One of the most powerful techniques I use in my trading system framework is volatility standardisation. This is adjusting the returns of different assets so that they have the same expected risk. As I discussed above, my standard definition of expected risk is to use an estimate of recent standard deviation. This has a number of benefits.

Thank you for that, and for everything. 301 Index 2001: A Space Odyssey, 19f 2008 crash, 170 Active management, 3 AIG, 2 Algorithms, 175, 199 Alpha, 3, 37, 106, 136 Alternative beta, 3-4 Amateur investors, 4, 6, 16, 48, 177, 210 and lack of diversification, 20 and over-betting, 21 and leverage, 35 and minimum sizes, 102 as day traders, 188 Anchored fitting: see Back-testing, expanding out of sample Annual returns, 178-179 Annualised cash volatility target, 137, 139, 149, 151, 159, 161, 171, 230, 250 Asset allocating investors, 3, 7, 42, 69, 98, 116, 147, 188, 225-244, 259 and Sharpe ratios, 46 and modular frameworks, 96 and the ‘no-rule’ rule, 116, 167, 196, 225, 228 and forecasts, 122-123, 159 and instrument weights, 166, 175, 189, 198-199 and correlation, 170 and instrument diversification multiplier, 175 and rules of thumb, 186 and trading speeds, 190-191, 205 and diversification, 206 Asset classes, 246&f Automation, 18-19 Back-testing, 5, 13-15, 16, 18, 19&f, 28, 53, 64, 67, 87, 113, 122, 146, 170, 182f, 187, 197, 205 and overfitting, 20, 29, 53f, 54, 68, 129f, 136, 145, 187 and skew, 40 and short holding periods, 43 in sample, 54-56 out of sample, 54-56 expanding out of sample, 56-57, 66, 71f, 84, 89f, 167f, 193-194 rolling window, 57-58, 66, 129f and portfolio weights, 69-73 and handcrafting, 85 and correlations, 129, 167&f, 175 and cost of execution, 179 simple and sophisticated, 186 need for mistrust of, 259 See also: Bootstrapping Barclays Bank, 1-2, 11, 31, 114 Barings, 41 Barriers to entry, 36, 43 Behavioural finance, 12 Beta, 3 Bid-Offer spread, 179 Block value, 153-154, 161, 182-183, 214, 219 Bollinger bands, 109 303 Systematic Trading Bond ETFs, 226 Bootstrapping, 70, 75-77, 80, 85-86, 146, 167, 175, 193-194&f, 199, 230, 248, 250 and forecast weights, 127, 205 see also Appendix C BP, 12, 13 Braga, Leda, 26 Breakouts, 109 Buffett, Warren, 37, 42 Calibration, 52-53 Carry, 67, 119, 123, 126, 127-128, 132, 247 and Skew, 119 Koijen et al paper on, 119f Central banks, 36, 103 Checking account value, recommended frequency, 149 Clarke, Arthur C, 19f ‘Close to Open’, 120-121 Cognitive bias, 12, 16, 17, 19-20, 28, 64, 179 and skew, 35 Collective funds, 4, 106, 116, 225 and derivatives, 107 and costs, 181 Commitment mechanisms, 17, 18 Compounding of returns, 143&f Contango: see Carry Contracts for Difference, 106, 181 Contrarians, 45 Corn trading, 247f Correlation, 42, 59f, 63, 68, 70, 73, 104, 107, 122, 129, 131, 167-168, 171 and Sharpe ratios, 64 and trading subsystems, 170 and ETFs, 231 Cost of execution, 179-181, 183, 188, 199, 203 Cost of trading, 42, 68, 104, 107, 174, 178, 181, 230 Credit Default Swap derivatives, 105 Crowded trades, 45 Crude oil futures, 246f Curve fitting: see over-fitting Daily cash volatility target, 137, 151, 158, 159, 161, 162, 163, 172, 175, 217, 218, 233, 254, 262. 270, 271, 217 Data availability, 102, 107 Data mining, 19f, 26-28 Data sources, 43-44 Day trading, 188 Dead cat bounces, 114 Death spiral, 35 DeMiguel, Victor, 743f Derivatives, 35 versus cash assets, 106 Desired trade, 175 Diary of trading, for semi-automatic trader, 219-224 Diary of trading, for asset allocating investor, 234- 244 Diary of trading, for staunch systems trader, 255- 257 Diversification, 20, 42, 44, 73f, 104, 107, 165, 170, 206 and Sharpe ratios, 65f, 147, 165 of instruments rather than rules, 68 and forecasts, 113 Dow Jones stock index, 23 Education of a Speculator, 17 Einstein, 70 Elliot waves, 109 Emotions, 2-3 Equal portfolio weights, 72-73 Equity value strategies, 4, 29, 31 Equity volatility indices, 34, 246, 247 Eurex, 180 Euro Stoxx 50 Index Futures, 179-180, 181, 182, 187-188, 193, 198 Eurodollar, trading recommendation, 247 Exchange rate, 161, 185 Exchange traded funds (ETFs), 4, 106, 183-184, 189, 197, 200, 214, 225, 226-228 holding costs of, 230 daily regearing of, 230f correlations, 231 Exchanges, trading on, 105, 107 Exponentially Weighted Moving Average Crossover 304 Index (EWMAC), 117-123, 126, 127-128, 132, 247 see also Appendix B Human qualities of successful traders, 259-260 Hunt brothers, 17 Fannie Mae and Freddie Mac, 2 Fees, 3 Fibonacci, 37, 109 Forecasts, 110-115, 121-123, 159, 175, 196, 211 scaling of, 112-113, 115, 133 combined, 125-133, 196, 248, 251 weighted average of, 126 and risk, 137 and speed of trading, 178 and turnover, 185 not changing once bet open, 211 see also Appendix D Forecast diversification multiplier, 128-133, 193f, 196, 249, 251 see also Appendix D Foreign exchange carry trading, 36 Fortune’s Formula, 143f FTSE 100 futures, 183, 210 Futures contracts, 181 and block value, 154-155 ‘Ideas First’, 26-27, 52-54, 103, 146 Ilmanen, Antti, 30f Illiquid assets, 198 Index trackers, 106 Inflation, 67 Instrument blocks, 154-155, 175, 182-183, 185, 206 Instrument currency volatility, 182-183, 203, 214 and turnover, 185, 195, 198 Instrument diversification multiplier, 166, 169-170, 171, 173, 175, 201, 206, 215, 229, 232, 253 Instrument forecast, 161, 162 Instrument riskiness, 155, 182 Instrument subsystem position, 175, 233 Instrument weights, 166-167, 169, 173, 175, 189, 198, 201, 202, 203, 206, 215, 229, 253 and Sharpe ratios, 168 and asset allocating investors, 226 and crash of 2008, 244 Gambling, 15, 20 Gaussian normal distribution, 22, 32&f, 39, 111f, 113, 114, 139f German bond futures, 112, 155, 181, 198 Gold, 246f Google, 29 Gross Domestic Product, 1 ‘Handcrafting’, 78-85, 116, 167-168, 175, 194, 199, 230, 248, 259 and over-fitting, 84 and Sharpe ratios, 85-90 and forecast weights, 127, 205 worked example for portfolio weights, 231-232 and allocation for staunch systems traders, 253 Hedge funds, 3, 177 High frequency trading, 6, 16, 30, 36, 180 Holding costs, 181 Housekeeping, daily, 217 for staunch systems traders, 254 Japan, 36 Japanese government bonds, 102, 112, 114, 200 JP Morgan, 156f Kahn, Richard, 42 Kaufman, Perry, 117 Kelly, John, and Kelly Criterion, 143-146, 149, 151 ‘Half-Kelly’ 146-147, 148, 230, 260 Koijen, Ralph, 119 Law of active management, 41-42, 43, 44, 46, 129f and Sharpe ratios, 47 Leeson, Nick, 41 Lehman Brothers, 2, 237 Leverage, 4, 21&f, 35, 95f, 138f, 142-143 and skew, 44-45 and low-risk assets, 103 and derivatives, 106 and volatility targeting, 151 realised leverage, 229 Life expectancy of investor, and risk, 141 305 Systematic Trading Limit orders, 179 Liquidity, 35, 104-105, 107 Lo, Andrew, 60f, 63f Long Term Capital Management (LTCM), 41, 46 Sharpe ratio of, 47 Low volatility instruments, need to avoid, 143, 151, 210, 230, 260 Lowenstein, Roger, 41, 46f Luck, need for, 260 Lynch, Peter, 37 Markowitz, Harry, 70, 72 Maximum number of bets, 215 Mean reversion trading, 31, 43, 45, 52, 213f ‘Meddling’, 17, 18, 19, 21, 136, 260 and forecasts, 115 and volatility targets, 148 Merger arbitrage, 29 Mid-price, 179, 181 Minimum sizes, 102, 107 Modular frameworks, 93, 95-99 Modularity, 5 Momentum, 42, 67, 68, 117 Moving averages, 94, 195, 197 MSCI, 156f Narrative fallacy, 20, 27, 28, 64 NASDAQ futures, 188 Nervousness, need for, 260 New position opening, 218 Niederhoffer, Victor, 17 Odean, Terence, 13, 20f Odysseus, 17 Oil prices, standard deviation of, 211 O’Shea, Colm, 94f Online portfolio calculators, 129f Overbetting, 21 Over the counter (OTC) trading, 105, 106, 107, 183f Overconfidence, 6, 17, 19f, 54, 58, 136 and lack of diversification, 20 and overtrading, 179 306 Over-fitting, 19-20, 27-28, 48, 51-54, 58, 65, 68, 121f, 156, 259 and Sharpe ratios, 46f, 47, 146 avoiding fitting, 67-68 of portfolio weights, 68-69 possibility of in ‘handcrafting’, 84 Overtrading, 179 Panama method, 247&f Passive indexing, 3 Passive management, 3, 4 Paulson, John, 31, 41 Pension funds, 3 ‘Peso problem’, 30&f Position inertia, 173-174, 193f, 196, 198, 217 Position sizing, 94, 153-163, 214 Poundstone, William, 143f Price movements, reasons for, 103, 107 Portfolio instrument position, 173, 175, 218, 254, 256, 257 Portfolio optimization, 70-90, 167 Portfolio size, 44, 178 Portfolio weighted position, 97, 99, 101, 109, 125, 135, 153, 165, 167, 177, 267 and diversification, 170 Price-to-earnings (P/E) ratios 4 Prospect theory, 12-13, 37 and momentum, 117 Quant Quake, the, 46 Raspberry Pi micro computers, 4 Relative value, 30, 43, 44-46, 213f Retail stockbrokers, 4 Risk, 39, 137-148, 170 Risk targeting, 136 Natural risk and leverage, 142 Risk parity investing, 38, 116&f Risk premia, 31, 119 RiskMetrics (TM), 156&f Roll down: see Carry Rolling up profits and losses, 149 Rogue Trader, 41 Rounded target position, 173, 175, 218 Index Rules of thumb, 186, 230 see also Appendix C Rumsfeld, Donald, 39&f Safe haven assets, 34 Schwager, Jack, 94f Self-fulfilling prophecies, 37 Semi-automatic trading, 4, 7, 11f, 18, 19f, 37, 38, 98, 163, 169, 209-224, 259 and portfolio size, 44, 203 and Sharpe ratios, 47, 147-148 and modular frameworks, 95 and trading rules, 109 and forecasts, 114, 122-123, 159 and eyeballing charts, 155, 195, 197, 214 and diversification, 166, 206 and instrument weights, 166, 175, 189 and correlation, 169 and trading subsystems, 169 and instrument diversification multiplier, 171, 175 and rules of thumb, 186 and overconfidence, 188 and stop losses, 189, 192 and trading speeds, 190-192, 205 Sharpe ratios, 25, 31-32, 34, 35, 42, 43, 44, 46-48, 53, 58, 60f, 67, 72, 73, 112, 184, 189, 210, 214, 250, 259 and overconfidence, 54, 136, 151 and rule testing, 59-60, 65 and T-Test, 61-63 and skew, 62f, 66 and correlation, 64 and diversification, 65f difficulty in distinguishing, 74 and handcrafting, 85-90 and factors of pessimism, 90 and risk, 137f, 138 and volatility targets, 144-145, 151 and speed of trading, 178-179, 196, 204 need for conservative estimation of, 195 and asset allocating investors, 225 and crash of 2008, 240 Schatz futures: see German bond futures Shefrin, Hersh, 13&f Short option strategies, 41 Short selling, 30, 37 Single period optimisation, 71, 85, 89 Skew, positive and negative, 32-34, 40-41, 48, 105, 107, 136, 139-141, 247, 259 and liquidity, 36 and prospect theory, 37 and risk, 39, 138 and leverage, 44-45, 142 and Sharpe ratios, 47, 62f, 146 and trend following, 115, 117 and EWMAC, 119 and carry, 119 and V2TX, 250 ‘Social trading’, 4f Soros, George, and sterling, 36f Speed of trading, 41-43, 47, 48, 104, 122, 174f, 177-205, 248 speed limits, 187-189, 196, 198-199, 204, 213, 228, 251, 260 Spread betting, 6, 106, 181, 197, 214 and block value, 154-155 and UK tax, 183f oil example, 214 Spreadsheets, 218 Stamp duty, 181 Standardised cost estimates, 203-205, 210, 226, 230 Standard deviations, 21-22, 31-32, 38, 40, 70, 103, 107, 111f, 129 and skew, 105 and forecasts, 112, 114, 128 recent, 155-158 returns, 167 and standardised cost, 182, 188, 192 and stop losses, 211 Static and dynamic trading, 38, 43, 168, 188 Staunch systems trading, 4, 7, 51-68, 69, 98, 109, 117-123, 167, 245-257 and Sharpe ratios, 46, 146, 189 and forecasts, 110-114, 122-123, 189 and instrument forecast, 161 and instrument weights, 166, 175, 198-199 and correlation, 170 and rules of thumb, 186 and trading speeds, 191-192, 205 307 Systematic Trading and back-testing, 193 and diversification, 206 Stop losses, 94-5, 115, 121f, 137f, 189, 192, 214, 216f, 217, 218 and forecasts, 211-212 and different instruments, 213 and price volatility, 216 Survivorship bias, 29 Swiss franc, 36, 103, 105, 142-143 System parameters, 186 Systematica hedge fund, 26 Taking profits and losses, 13-15, 16-18, 58, 94-95, 149 and trend following, 37 see also Appendix B Taleb, Nassim, 39f, 41 Tax (UK), 106, 183f Technical analysis, 18, 29 Technology bubble of 1999, 35 Templeton, John, 37 The Black Swan, 39f The Greatest Trade Ever, 31f, 41 Thorpe, Ed, 146f Thriftiness, need for, 260 Timing, 2 Too much/little capital, 206, 246f Trading capital, 150-151, 158, 165, 167, 178, 192, 199-202 starting low, 148 reducing, 149 and turnover, 185 daily calculation of, 217 Trading rules, 3-4, 7, 16, 25-26, 78, 95, 97-98, 101, 109, 121, 125, 135, 159, 161, 187, 249, 259 need for small number of, 67-68, 193 Kaufman, Perry’s guide to, 117 and speed of trading, 178, 205 cost calculations for, 204 see also Appendix B Trading subsystems, 98-99, 116, 159, 162, 163, 165, 166, 167&f, 169, 171f, 172, 175-176, 185, 187, 230, 251-252, 260 and correlation, 170 308 and turnover, 196 cost calculations for, 204 Traditional portfolio allocation, 167 Trend following, 28, 30, 37, 45, 47f, 67, 117, 137f, 194f, 212f, 247 and skew, 105, 115, 117, 213 Turnover, 184-186, 195, 197, 198, 205, 228, 260 methods of calculation, 204 back-testing of, 247-248 Twitter, 29 V2TX index, 246, 247, 249 Value at risk, 137 VIX futures, 105 Volatility, 21, 103, 107, 116, 129, 150, 226, 229 and targets, 95, 98, 106, 158, 159, 185 unpredictability of, 45 price volatility, 155-158, 162-163, 189, 196, 197, 200, 205, 214, 228, Appendix D and crash of 2008, 240-244 instrument currency volatility, 158, 161 instrument value volatility, 161, 172, 250 scalars, 159-160, 162, 185, 201, 206, 215, 217, 218, 229 look-back period, 155, 195-197 and speed of trading, 178 Volatility standardisation, 40, 71, 72, 73, 167, 182, 185 and forecasts, 112, 121, 129 and block value, 155 Volatility standardized costs, 247 Volatility targeting, 135-151, 171f, 188, 192, 201f, 213-215, 230, 233, 250, 259 Walk forward fitting: see Back testing, rolling window Weekly rebalancing process, for asset allocating investors, 233 When Genius Failed, 40, 46f Women as makers of investment decisions, 17&f www.systematictrading.org, 234 Zuckerman, Gregory, 31f THANKS FOR READING!


Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan

algorithmic trading, asset allocation, automated trading system, backtesting, Black Swan, Brownian motion, business continuity plan, buy and hold, compound rate of return, Edward Thorp, Elliott wave, endowment effect, fixed income, general-purpose programming language, index fund, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

But, of course, the returns are not really Gaussian: large losses occur at far higher frequencies than would be predicted by a nice bell-shaped curve. Some people refer to the true distributions of returns as having “fat tails.” What this means is that the probability of an event far, far away from the mean is much higher than allowed by the Gaussian bell curve. These highly improbable events have been called “black swan” events by the author Nassim Taleb (see Taleb, 2007). To handle extreme events that fall outside the Gaussian distribution, we can use our simple backtest technique to roughly estimate what the maximum one-period loss was historically. (The period may be one week, one day, or one hour. The only criterion to use is that you should be ready to rebalance your portfolio according to the Kelly formula at the end of every period.)

Schiller, Robert. 2007. “Historic Turning Points in Real Estate.” Cowles Foundation Discussion Paper No. 1610. Available at: cowles.econ.yale.edu. Schiller, Robert. 2008. “Economic View; How a Bubble Stayed under the Radar.” New York Times, March 2. Available at www.nytimes.com/2008/ 03/02/business/02view.html?ex=1362286800&en=da9e48989b6f937a&ei= 5124&partner=permalink&exprod=permalink. Taleb, Nassim. 2007. The Black Swan: The Impact of the Highly Improbable. Random House. Thaler, Richard. 1994. The Winner’s Curse. Princeton, NJ: Princeton University Press. P1: JYS bib JWBK321-Chan Bibliography September 24, 2008 15:5 Printer: Yet to come 171 Thorp, Edward. 1997. “The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market.” Handbook of Asset and Liability Management, Volume I, Zenios and Ziemba (eds.).

See Factor models Artificial intelligence, 116 and stock picking, 26–27 B Backtesting, 20, 22, 24, 31–67, 144–148 common pitfalls to avoid, 50–60 data-snooping bias, 52–60 look-ahead bias, 51–52 common platforms, 32–36 Excel, 32 high-end, 35–36 R , 32–34 MATLAB TradeStation, 35 historical databases, finding and using, 36–43 high and low data, use of, 42–43 split and dividend-adjusted data, 36–40 survivorship bias, 40–42 January effect, 144–146 performance measurement, 43–50 strategy refinement, 65–66 transaction costs, 61–65 year-on-year seasonal trending strategy, 146–148 Bank of Montreal, 150 Bayes Net toolbox, 168 Behavioral bias, 108–109 Behavioral finance, 108–111 Beta, 14 Black Monday, 106 “Black swan” events, 105 Bloomberg, 14, 36, 75 Bollinger bands, 23 Brett Steenbarger Trading Psychology, 10 Bright Trading, 70 Business, setting up a, 69–78 choosing a brokerage or proprietary trading firm, 71–75 physical infrastructure, 75–77 structure, 69–71 175 P1: JYS ind JWBK321-Chan October 2, 2008 14:7 176 C C#, 80, 85, 164 C++, 80, 85, 164 Calendar effect. See Seasonal trading strategies Capacity, 27, 158 Capital availability, effect on choices, 15 Capital allocation, optimal, 95–103 Capital IQ, 136 Chicago Mercantile Exchange (CME), 16 Clarifi, 35 CNBC Plus, 76 Cointegrating augmented Dickey-Fuller test, 128 Cointegration, 126–133 forming a good cointegrating pair of stocks, 128–130 Compustat, 136 Contagion, financial, 104–105 Correlation, 131 Covariance matrix, 97 CSIdata.com, 37 CRSP.com, 37 D Dark-pool liquidity, 71, 73, 88 Data mining, 121 Databases, historical, 37 Data-snooping bias, 25–27, 52–60, 91 out-of-sample testing, 53–55 sample size, 53 sensitivity analysis, 60 and underperformance of live trading, 91 Decimalization of stock prices, 91, 120 Printer: Yet to come INDEX Deleveraging, 152 Despair, 110 Disasters, physical or natural, 108 Discovery (Alphacet), 35, 36, 55, 85, 122–126 charting application, 125 Dollar-neutral portfolio, 43–44 Dow Jones, 36, 75 Drawdown, 20, 21–22, 43, 95 maximum, 21 calculating, 48–50 maximum duration, 21 calculating, 48–50 DTN.com, 37 Dynamic data exchange (DDE) link, 80, 81–82, 83, 84, 85 E ECHOtrade, 70 Econometrics toolbox, 168 The Economist, 10 Elite Trader, 10, 74 Elliott wave theory, 116 E-mini S&P 500 future, 16 Endowment effect, 108–109 Equity curve, 20 Excel, 3, 21, 32, 51, 163 dynamic data exchange (DDE) link to, 80, 81–82, 83, 84, 85 using in automated trading systems, 80, 81, 83, 84, 85 using to avoid look-ahead bias, 51 using to calculate maximum drawdown and maximum drawdown duration, 48 using to calculate Sharpe ratio for long-only strategies, 45–46, 47 P1: JYS ind JWBK321-Chan October 2, 2008 14:7 Printer: Yet to come 177 Index Execution systems, 79–94 automated trading system, advantages of, 79–87 fully automated trading system, building a, 84–87 semiautomated trading system, building a, 81–84 paper trading, testing your system by, 89–90 performance, divergence from expectations, 90–92 transaction costs, minimizing, 87–88 Exit strategy, 140–143 F Factor exposure, 134 Factor models, 133–139 principal component analysis as an example of, 136–139 Factor return, 134 FactSet, 35, 36 Fama-French Three-Factor model, 134–135, 153 Financial web sites and blogs, 10 G GainCapital.com, 37 GARCH toolbox, 168 Gasoline futures, seasonal trade in, 148–151 Gaussian probability distributions, 96, 105 derivation of Kelly formula in, 112–113 Generalized autoregressive conditional heteroskedasticity (GARCH) model, 120 Genesis Securities, 70, 73, 82 Global Alpha fund (Goldman Sachs), 104 Greed, 110–111 H “Half-Kelly” betting, 98, 105–106 High-frequency trading strategies, 151–153 transaction costs, importance of in testing, 152 High-leverage versus high-beta portfolio, 153–154 High watermark, 21, 48 Historical databases errors in, 117 finding and using, 36–43 high and low data, use of, 42–43 split and dividend-adjusted data, 36–40 survivorship bias, 40–42 HQuotes.com, 37, 81 Hulbert, Mark (New York Times), 10 I Information ratio.


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Stuffocation by James Wallman

3D printing, Airbnb, back-to-the-land, Berlin Wall, big-box store, Black Swan, BRICs, carbon footprint, Cass Sunstein, clean water, collaborative consumption, commoditize, creative destruction, crowdsourcing, David Brooks, Fall of the Berlin Wall, happiness index / gross national happiness, hedonic treadmill, high net worth, income inequality, Intergovernmental Panel on Climate Change (IPCC), James Hargreaves, Joseph Schumpeter, Kitchen Debate, Martin Wolf, mass immigration, McMansion, means of production, Nate Silver, Occupy movement, Paul Samuelson, post-industrial society, post-materialism, Richard Florida, Richard Thaler, sharing economy, Silicon Valley, Simon Kuznets, Skype, spinning jenny, The Signal and the Noise by Nate Silver, Thorstein Veblen, Tyler Cowen: Great Stagnation, World Values Survey, Zipcar

Can’t see it myself, but just to be clear: it is not my intention to insult dustmen. Dustmen do possess expertise, but not, generally, in the field of forecasting. (If you are a dustman with forecasting skill, or know one who has an uncanny ability to know what’s next, please let me know by emailing james@stuffocation.org.) Forecasts Are Not Facts, They Are Maps For Nassim Nicholas Taleb’s dismal view of forecasting, read Nassim Nicholas Taleb, The Black Swan (New York: Penguin, 2007). The story of the turkey is borrowed, as Taleb notes, from the philosopher Bertrand Russell. Using the Past to Tell the Future I am indebted to three sources for this section: Peter N Stearns, “Why Study History?”, American Historical Association, 1998; Nate Silver, The Signal and the Noise (New York: Allen Lane, 2012); and Rob Hyndman, “Why are some things easier to forecast than others?”

How could the dustmen do so well compared with people who have been trained to know and who had far more information, the so-called experts? And, which is more worrying, what is the point in trying to forecast the future if an expert’s forecasts are no more accurate than a prediction from the proverbial man on the street, no better, you might say, than rubbish? Forecasts Are Not Facts, They Are Maps The author Nassim Nicholas Taleb painted a similarly gloomy picture in his book The Black Swan, especially in a story about a turkey farmer and a turkey. See the world, for a moment, through a turkey’s eyes. Each day, the farmer comes to feed you. For a thousand days or so, he turns up, red bucket in hand, pulling out oats and corn and carrot peelings, sprinkling them on the ground. Every one of those thousand days, you go gobble-gobbling over, pecking at the food.

What if instead of scattering food, he is going to wring your neck? What use would all your data points from the past be then? Life, Taleb says, is like that. No matter how much data we have, the world is unknowable. We can never know what is going to happen in the future. No matter how sure and safe and good things feel, fate might have other plans. It might even wring your neck. What all this tells us is that someone with no special knowledge, dustmen, for instance, can be just as good as people with insider knowledge at predicting the future. It also shows that wild, unexpected unforeseen events can happen, and suggests that any attempt to tell the future will therefore be pointless. But we need not conclude, as Taleb does, that all forecasting is futile and that we should simply give up on forecasts altogether. After all, every one of us, and every business, and every government, uses predictions every day to work out what might happen and what we should do to plan for that.


pages: 459 words: 103,153

Adapt: Why Success Always Starts With Failure by Tim Harford

Andrew Wiles, banking crisis, Basel III, Berlin Wall, Bernie Madoff, Black Swan, car-free, carbon footprint, Cass Sunstein, charter city, Clayton Christensen, clean water, cloud computing, cognitive dissonance, complexity theory, corporate governance, correlation does not imply causation, creative destruction, credit crunch, Credit Default Swap, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dava Sobel, Deep Water Horizon, Deng Xiaoping, disruptive innovation, double entry bookkeeping, Edmond Halley, en.wikipedia.org, Erik Brynjolfsson, experimental subject, Fall of the Berlin Wall, Fermat's Last Theorem, Firefox, food miles, Gerolamo Cardano, global supply chain, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Jane Jacobs, Jarndyce and Jarndyce, Jarndyce and Jarndyce, John Harrison: Longitude, knowledge worker, loose coupling, Martin Wolf, mass immigration, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Netflix Prize, New Urbanism, Nick Leeson, PageRank, Piper Alpha, profit motive, Richard Florida, Richard Thaler, rolodex, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, South China Sea, special economic zone, spectrum auction, Steve Jobs, supply-chain management, the market place, The Wisdom of Crowds, too big to fail, trade route, Tyler Cowen: Great Stagnation, web application, X Prize, zero-sum game

We might be tempted to think of such projects as lottery tickets, because they pay off rarely and spectacularly. They’re rather better than that, in fact. Lotteries are a zero-sum game – all they do is redistribute existing resources, whereas research and development can make everyone better off. And unlike lottery tickets, bold innovation projects do not have a known payoff and a fixed probability of victory. Nassim Taleb, author of The Black Swan, calls such projects ‘positive black swans’. Whatever we call them, such ventures present us with a headache. They are vital, because the payoff can be so enormous. But they are also frustrating and unpredictable. Usually they do not pay off at all. We cannot ignore them, and yet we cannot seem to manage them effectively either. It would be reassuring to think of new technology as something we can plan.

.), Virtual History: Alternatives and Counterfactuals (New York: Basic Books, 1997), p. 284. 82 The RAF boasted fewer than 300 Spitfires: McKinstry, Spitfire, pp. 188–9. 82 Predicted that the Luftwaffe’s first week: Roberts, ‘Hitler’s England’, pp. 285–6. 82 It might even have given Germany the lead: Roberts, ‘Hitler’s England’, pp. 310, 320. 82 The prototype cost the government: McKinstry, Spitfire, p. 51, and Lawrence H. Officer, ‘Purchasing power of British pounds from 1264 to present’, MeasuringWorth, 2009, http://www.measuring-worth.com/ppoweruk/ 83 ‘Positive black swans’: Nassim Nicholas Taleb, The Black Swan (New York: Random House, 2007). 85 We should now build: McKinstry, Spitfire, p. 12. 86 He soon discovered some remarkable examples: Richard Dawkins, The Greatest Show on Earth (London: Bantam, 2009), pp. 254–73. 87 Bright ideas emerge from the swirling mix of other ideas: See also Richard Florida, ‘The world is spiky’, The Atlantic Monthly, October 2005, my The Logic of Life (2008), Matt Ridley’s The Rational Optimist (2010) and Steven Johnson’s Where Good Ideas Come From (2010). 87 A playboy politician most famous as a campaigner against lesbianism: McKinstry, Spitfire, pp.17–18. 88 ‘Bloody good cup of tea, Mitchell’: McKinstry, Spitfire, p. 20. 88 ‘It’s either him or me!’

Insightful and clever’ Alex Bellos, author of Alex’s Adventures in Numberland ‘Adapt is a highly readable, even entertaining, argument against top-down design. It debunks the Soviet-Harvard command-and-control style of planning and approach to economic policies and regulations, and vindicates trial-and-error (particularly the error part in it) as a means to economic and general progress. Very impressive!’ Nassim N. Taleb, author of The Black Swan and Fooled by Randomness ‘This is a brilliant and fascinating book – Harford’s range of research is both impressive and inspiring, and his conclusions are provocative. The message about the need to accept failure has important implications, not just for policy making but also for peoples’ professional and personal lives. It should be required reading for anyone serving in government, working at a company, trying to build a career – or simply trying to navigate an increasingly complex world’ Gillian Tett, US Managing Editor, Financial Times, and author of Fool’s Gold ‘Tim Harford has made a compelling and expertly informed case for why we need to embrace risk, failure, and experimentation in order to find great ideas that will change the world.


pages: 350 words: 109,379

How to Run a Government: So That Citizens Benefit and Taxpayers Don't Go Crazy by Michael Barber

Affordable Care Act / Obamacare, Atul Gawande, battle of ideas, Berlin Wall, Black Swan, Checklist Manifesto, collapse of Lehman Brothers, collective bargaining, deliberate practice, facts on the ground, failed state, fear of failure, full employment, G4S, illegal immigration, invisible hand, libertarian paternalism, Mark Zuckerberg, Nate Silver, North Sea oil, obamacare, performance metric, Potemkin village, Ronald Reagan, school choice, The Signal and the Noise by Nate Silver, transaction costs, WikiLeaks

From a delivery perspective, we saw this as a model of best practice which we then recommended to the Home Office in dealing with its multiple challenges, and to the education department in dealing with truancy. In short, we were becoming a centre of expertise in delivery rather than policy. Table 17 It therefore makes sense to try to categorize problems by their nature so that systems can improve at diagnosis and learn systematically what the options are for solving different types of problem. Table 17 illustrates, as a start, what could be done. It is just a sketch, but as Nassim Nicholas Taleb’s ‘black swan’ argument makes clear, unexpected events of one sort or another will recur. How much more effectively could government work if it was systematic in identifying the type of problem and the intensity of the response required? Of course, any given problem may involve a number of these characteristics – for instance, poor leadership and poor implementation are likely to go together. Using real examples, here are some considerations about how to respond.

Bobst Center for Peace and Justice, Princeton University Schlesinger, R. (2008), White House Ghosts: Presidents and Their Speechwriters, New York, Simon & Schuster Seldon, A. (2005), Blair, London, Simon & Schuster —, Snowden, P. and Collings, D. (2007), Blair Unbound, London, Simon & Schuster Sellar, W. and Yeatman, R. (1998), 1066 and All That, London, Methuen Shlaes, A. (2013), Coolidge, New York, HarperCollins Silver, N. (2012), The Signal and the Noise: The Art and Science of Prediction, London, Penguin Smillie, I. (2009), Freedom from Want: The Remarkable Success Story of BRAC, the Global Grassroots Organisation That’s Winning the Fight Against Poverty, Dhaka, Kumarian Press Smith, J. E. (2012), Eisenhower in War and Peace, New York, Random House State of Victoria (2005), Growing Victoria Together Steinberg, J. (2011), Bismarck: A Life, Oxford, Oxford University Press Stevenson, A. (2013), The Public Sector: Managing the Unmanageable, London, Kogan Page Sugden, J. (2012), Nelson: The Sword of Albion, London, Bodley Head Taleb, N. N. (2007), The Black Swan: The Impact of the Highly Improbable, London, Allen Lane — (2012), Antifragile: How to Live in a World We Don’t Understand, London, Allen Lane Taliaferro, J. (2013), All the Great Prizes: The Life of John Hay, from Lincoln to Roosevelt, New York, Simon & Schuster Thaler, R. and Sunstein, C. (2008), Nudge: Improving Decisions About Health, Wealth and Happiness, London, Penguin Timmins, N. (1995), The Five Giants: A Biography of the Welfare State, London, HarperCollins Trewhitt, K. et al (2014), How to Run a Country: A Collection of Essays, London, Reform Tuchman, B. (1990), The March of Folly: From Troy to Vietnam, St Ives, Abacus US Senate Committee on the Budget and Taskforce on Government Performance, Report, 29 October 2009 Wainwright, A. (2007), The Southern Fells, revised edition, London, Frances Lincoln Wales Audit Office (2006), Ambulance Services in Wales Weiss, M. (2014), ‘Government Entrepreneur’ is not an Oxymoron, Cambridge MA, Harvard Business Review Blog Network, 28 March 2014 Whelan, F. (2014), The Learning Challenge, self-published Wiggins, B. (2012), My Time, London, Yellow Jersey Press Williams, J. and Rossiter, A. (2004), Choice: The Evidence, London, Social Market Foundation Wolmar, C. (2013), To the Edge of the World: The Story of the Trans-Siberian Railway, London, Atlantic Books Notes PREFACE 1.

Meanwhile, Team Sky, also managed by Brailsford, provided two consecutive (British) winners of the Tour de France. In the Delivery Unit, we were on the way to becoming as obsessive as Dave Brailsford. As Nassim Nicholas Taleb puts it with only slightly less obsession than Brailsford: Your last recourse against randomness is how you act – if you can’t control outcomes, you can control the elegance of your behaviour.6 This kind of obsession with your own processes at a level of detail unlocks the door to irreversibility. Next time someone tells you that leaders should focus on the big picture and leave the detail to subordinates, show them the door. RULE 46 LEARN THE LEARNABLE AND CONTROL THE CONTROLLABLE (obsessively) BUILDING CAPACITY Nassim Nicholas Taleb’s book Antifragile describes how organizations can become more than resilient; they can develop so that they don’t just survive shocks, they benefit from them.


pages: 293 words: 81,183

Doing Good Better: How Effective Altruism Can Help You Make a Difference by William MacAskill

barriers to entry, basic income, Black Swan, Branko Milanovic, Cal Newport, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, effective altruism, en.wikipedia.org, end world poverty, experimental subject, follow your passion, food miles, immigration reform, income inequality, index fund, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, job automation, job satisfaction, Lean Startup, M-Pesa, mass immigration, meta analysis, meta-analysis, microcredit, Nate Silver, Peter Singer: altruism, purchasing power parity, quantitative trading / quantitative finance, randomized controlled trial, self-driving car, Skype, Stanislav Petrov, Steve Jobs, Steve Wozniak, Steven Pinker, The Future of Employment, The Wealth of Nations by Adam Smith, universal basic income, women in the workforce

The situation could be exacerbated if geoengineering, previously used to cool the planet, was discontinued during the societal collapse, which could cause even more warming. Even in a situation of this sort it is unlikely that the human race would end, however. (the death tolls from disasters form a fat-tailed distribution): A comprehensive overview is given by Anders Sandberg, “Power Laws in Global Catastrophic and Existential Risks,” unpublished paper, 2014. (Nassim Taleb describes these as Black Swans): Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007). most people who’ve died in war have died in the very worst wars: Steven Pinker, The Better Angels of Our Nature: Why Violence Has Declined (New York: Viking, 2011). This is what the Skoll Global Threats Fund focuses on: “About Us/Mission & Strategy,” Skoll Global Threats Fund, http://www.skollglobalthreats.org/about-us/mission-and-approach/.

In that case, the true expected social cost of CO2 could be much higher than thirty-two dollars per metric ton, justifying much more extensive efforts to reduce emissions than the estimates the economists first suggested. Just as most of the value from aid programs comes from the very best aid programs (which we discussed in chapter three), it’s often the case that most of the expected harm from disasters come from the very worst disasters. (That is: the death tolls from disasters form a fat-tailed distribution. Nassim Taleb describes these as Black Swans: very rare events that have a very great impact.) For example, most people who’ve died in war have died in the very worst wars: of the four hundred wars in the last two hundred years, about 30 percent of deaths were from World War II alone. This means that if we’re concerned by war, we should spend most of our efforts trying to prevent the very largest wars from occurring, or to limit their scope.

academia, careers in, 171–73 accounting careers, 165 actuarial careers, 165 administrative costs of charities, 106, 107–8 advocacy careers, 174–75 Against Malaria Foundation cost effectiveness of, 53 and expected values, 81–82 founder of, 157, 177 funding needs of, 118 GiveWell’s endorsement of, 125, 127, 197 and program implementation, 117 AIDS, 35, 38, 60 aid skeptics/critics on best aid programs, 47 critiques of sums spent, 44 and Easterly, 43 on ineffectiveness of aid, 45–46 ALS Association, 110 Amazon jungle, 138 American Apparel, 128–29, 132 American Cancer Society, 110 Animal Charity Evaluators, 143, 190 animal welfare, 141–43, 189–90 Annan, Kofi, 104 antiretroviral therapy, 52, 52, 53 Apple, 129, 152 automation of jobs, 165 Banerjee, Abhijit, 19 Bangladesh, 128, 130, 131–32 Barré-Sinoussi, Françoise, 171 bed nets and Against Malaria Foundation, 125 cost effectiveness of, 52, 53 and evidence behind claims, 113–14 implementation of, 117 beef consumption, 136, 141, 142 bereavement, 42 Berger, Alexander, 162 Berger, Ken, 40 best aid programs, 47, 50, 52–53 BetaGov, 187 Black Swan events, 98 blindness, 37–38, 47, 61 Bolivia, 130 Books for Africa (BFA), 103–4, 106, 107, 108–9 Borlaug, Norman, 171 Bosch, Carl, 171 Brazil, 130 Brooks, David, 166 Brown, Laura, 89–94, 174 Bunker, John, 63 Burkina Faso, 104, 116, 122 Burma, 130 Bush, Laura, 3, 9 Cambodia, 130 Cameron, David, 90 Cameroon, 104, 123 Canada, homicide rate in, 185 cancer scale of problem, 182 treatment costs and funding, 61–62 carbon dioxide (CO2), 98, 138–40 carbon dioxide equivalent (CO2eq), 97, 135, 136 carbon footprint, 135–40 career choices, 147–78 and altruistic motivation, 166–67 and building career capital, 157, 158, 173 and career coaching, 199 and doctoral studies, 167–68 and entrepreneurship, 168–71 and exploration value, 158–59 and following one’s passion, 149–53, 154 and impact on later career, 158–60 and impact on the job, 155–57 and job satisfaction, 151 in later life, 176–77 and law of diminishing returns, 62–66 in medicine, 55–56, 62–66, 74–75, 164 and outsourcing or automation of jobs, 165 and personal fit, 148–55, 181 in politics and advocacy, 89–94, 174–75 with for-profit companies, 163 in research, 171–73 and skill building, 167–68 and volunteering, 175–76 working for effective organizations, 162–63 See also earning to give Case, Steve and Jean, 2–3 Case Foundation, 5, 9 cash transfers to aid recipients, 106, 111, 113, 115–16, 122 cattle, 141–42 Causevox.com, 198 Center for Global Development, 189 Centre for Effective Altruism, 180 Centre for the Study of Existential Risk, 194 CEOs of charities, 106, 107–8 Charity Navigator, 40, 105–7 Charity Science, 198–99 CheatNeutral.com, 140 chickens, 141–42, 143, 189–90 Child Labor Deterrence Act, 131–32 child workers, 131–32 China’s earthquake disaster of 2008, 59–60 Clemens, Michael, 188 climate change between 2 to 4°C, 190–91 catastrophic climate change, 191–92 evaluation of cause, 195 and expected values, 95–99 severity of, 179 Climate Works, 191, 193 Clinton, Bill, 3, 9 Cochrane Collaboration, 70–74 coffee, 133 Colgate Palmolive, 2, 5 Colorado, 86 condom promotion, 52, 52, 53 consumerism, ethical, 128–46 and fair-trade products, 132–35 and food choices, 87–88, 141–43, 189–90 and green living, 135–40 and moral licensing, 144–46 and sweatshop laborers, 129–32 Cool Earth, 138–40, 191, 193, 197 Costa Rica, 133 cost effectiveness and evaluation of aid programs, 109, 110–13, 119–20 in poor vs. rich countries, 62, 121 costs associated with saving a life, 54 cows, 141–42 Cramer, Christopher, 134 criminality, 70–74 criminal justice reform, 185–87, 194 dairy, 136 Dead Aid (Moyo), 43 deforestation, 138–40 dehydration, 112 Democratic Republic of the Congo, 104, 123, 191 depression, 35 Development Media International (DMI) cost effectiveness of, 111–13, 119–20 and evidence behind claims, 115–17, 119–20 financial overview of, 106, 108 founder of, 157 funding needs of, 118–19 GiveWell’s endorsement of, 122–23, 127, 197 implementation of, 117–18 mission of, 104 deworming school children, 8–9, 51, 51 Deworm the World Initiative cost effectiveness of, 9, 11, 123–24 founder of, 157 GiveWell’s endorsement of, 9, 123–24, 127, 197 dialysis, 38 diamonds paradox, 57 diarrhea deaths associated with, 47, 112 and effectiveness of aid programs, 121 disasters and disaster relief and effective uses of funding, 120 expected harm from disasters, 98 and law of diminishing returns, 58–61 Disney, 129 District of Columbia, 87 doctoral studies, 167–68 doctors as career choice, 64–66, 74–78 and lives saved, 63–66, 74–75 Doctors Without Borders, 30–31 Duflo, Esther, 19 Durbin, Drew, 170 earning to give and altruistic motivation, 166 and career choices, 74–78, 162, 163–67 and Giving Pledge, 166 political career compared to, 90, 93 earthquakes, 58–59 Easterly, William, 43, 47 economic empowerment aid programs, 121 economic perspectives on climate change, 97–98 economics degrees, 172 Edlin, Aaron, 85 education and attendance, 51, 51 best programs for, 50 and deworming programs, 8–9, 51, 51 testing effectiveness of programs benefiting, 7–9 and textbook availability, 7, 108 effective altruism and choosing causes to support, 32–33 community of, 198 definition of, 11–12 five key questions of, 13 telling others about, 198–99 effectiveness of aid programs and aid skeptics, 46–47 cost effectiveness compared to, 110–11 definition of, 12 and financial health of charities, 107–8 and regression to the mean, 73–74 of Scared Straight program, 70–74 See also cost effectiveness 80,000 Hours organization, 13, 153, 173, 199 80/20 rule, 49 Eldering, Grace, 171 electricity, 135, 136 Eliasch, Johan, 138 emotional appeal of causes, 9–10, 11, 60–61 employment, 55.


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Black Box Thinking: Why Most People Never Learn From Their Mistakes--But Some Do by Matthew Syed

Airbus A320, Alfred Russel Wallace, Arthur Eddington, Atul Gawande, Black Swan, British Empire, call centre, Captain Sullenberger Hudson, Checklist Manifesto, cognitive bias, cognitive dissonance, conceptual framework, corporate governance, creative destruction, credit crunch, crew resource management, deliberate practice, double helix, epigenetics, fear of failure, fundamental attribution error, Henri Poincaré, hindsight bias, Isaac Newton, iterative process, James Dyson, James Hargreaves, James Watt: steam engine, Johannes Kepler, Joseph Schumpeter, Kickstarter, Lean Startup, mandatory minimum, meta analysis, meta-analysis, minimum viable product, publication bias, quantitative easing, randomized controlled trial, selection bias, Shai Danziger, Silicon Valley, six sigma, spinning jenny, Steve Jobs, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, Toyota Production System, US Airways Flight 1549, Wall-E, Yom Kippur War

But none of this successful “evidence” would have expanded our knowledge very much. Indeed, in one sense, it would not have even increased the probability of the assertion “water always boils at 100ºC.”8 This point was originally made by the Scottish philosopher David Hume in the eighteenth century, and popularized recently by Nassim Nicholas Taleb, the mathematician and author.9 Taleb has pointed out that you could observe a million white swans, but this would not prove the proposition: all swans are white. The observation of a single black swan, on the other hand, would conclusively demonstrate its falsehood. Failure, then, is hardwired into both the logic and spirit of scientific progress. Mankind’s most successful discipline has grown by challenging orthodoxy and by subjecting ideas to testing. Individual scientists may sometimes be dogmatic but, as a community, scientists recognize that theories, particularly those at the frontiers of our knowledge, are often fallible or incomplete.

It took further experiments for scientists to universally agree that light is attracted to heavy bodies. 5. See Karl Popper, Conjectures and Refutations. 6. Philip H. Gosse, Omphalos: An Attempt to Untie the Geological Knot (Rochester, NY: Scholar’s Choice, 2015). 7. Karl Popper, Conjectures and Refutations. 8. This example is cited in Bryan Magee’s Philosophy and the Real World: An Introduction to Karl Popper (Chicago: Open Court Publishing, 1985). 9. Nassim N. Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Penguin, 2008). 10. Daniel Kahneman and Gary Klein, “Conditions for Intuitive Expertise, Failure to Disagree,” American Psychologist 64, no. 6 (2009): 515–26. 11. Ibid. 12. K. Anders Ericsson (ed.), Development of Professional Expertise: Toward Measurement of Expert Performance and Design of Optimal Learning Environments (New York: Cambridge University Press, 2009). 13.

Architecture is a particularly interesting case, because it is widely believed that ancient buildings and cathedrals, with their wonderful shapes and curves, were inspired by the formal geometry of Euclid. How else could the ancients have built these intricate structures? In fact, geometry played almost no role. As Taleb shows, it is almost certain that the practical wisdom of architects inspired Euclid to write his Book of Elements, so as to formalize what the builders already knew. “Take a look at Vitruvius’ manual, De architectura, the bible of architects, written about three hundred years after Euclid’s Elements,” Taleb writes. “There is little formal geometry in it, and, of course, no mention of Euclid, mostly heuristics, the kind of knowledge that comes out of a master guiding his apprentices . . . Builders could figure out the resistance of materials without the equations we have today—buildings that are, for the most part, still standing.”6 These examples do not show that theoretical knowledge is worthless.


pages: 265 words: 74,000

The Numerati by Stephen Baker

Berlin Wall, Black Swan, business process, call centre, correlation does not imply causation, Drosophila, full employment, illegal immigration, index card, Isaac Newton, job automation, job satisfaction, McMansion, Myron Scholes, natural language processing, PageRank, personalized medicine, recommendation engine, RFID, Silicon Valley, Skype, statistical model, Watson beat the top human players on Jeopardy!

(This type of program detected financial irregularities on the part of New York governor Eliot Spitzer in 2007. The trail of these monies led to the discovery of payments for prostitutes and his resignation in March 2008.) But such tools are useless when it comes to recognizing or predicting something never seen before—the unexpected earth-shaking events that the author Nassim Nicholas Taleb discusses in his book The Black Swan. The second problem is that suspected terrorists, unlike most shoppers or voters, take measures to blur the data signal to cover their tracks. The simplest way is to conduct important business off the network—to hold meetings face to face and send coded messages on paper or committed to the memory of human couriers. But terrorists can also manipulate the data that gets picked up, distorting what industry insiders call the "feedback loop."

David Danks, a philosophy professor at Carnegie Mellon University, told me that NASA processes data from 40,000 different sensors on the space shuttles, much of it coming in numerous times per second. This provides sufficient data to create detailed simulations of launches. And yet during the first quarter-century of shuttle flights, there have been only two disasters. "We have a sample size of two," he said. This makes it difficult to pick out patterns of data that point to problems. [>] Unexpected earth-shaking events. Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable, Random House, 2007. Jerry Friedman, a statistics professor at Stanford. See The Mathematical Sciences' Role in Homeland Security: Proceedings of a Workshop, National Academies Press, 2004. [>] Jeff Jonas, like many others. Jonas writes at length about security and privacy challenges surrounding data on his blog, http://www.jeffjonas.typepad.com/. [>] As many as 300 cameras.

Barnes & Noble Books, 2005 (originally published in 1911) * * * Index Accenture (company), [>]–[>], [>]–[>], [>]–[>], [>] Acxiom (company), [>]–[>] AdSense (Google service), [>] Advertisers, [>] calculating rate of return for, [>], [>] changes in methods of, [>]—[>], [>], [>], [>]–[>] customer lists shared by, [>], [>], [>]–[>], [>] and the Internet, [>]–[>], [>]–[>], [>], [>]–[>], [>], [>] microtargeting by, [>], [>], [>] and retail stores, [>]–[>], [>], [>], [>], [>], [>] selling our own data to, [>] See also "Buckets"; Shoppers; "Tribes" African American voters, [>] Age (generations) distinguishing, through word analysis, [>]–[>], [>], [>] on online dating questionnaire, [>]–[>] Alamo Rent A Car, [>]–[>], [>]–[>], [>], [>] Algorithms for analyzing patients, [>], [>], [>] for analyzing shoppers, [>], [>]–[>] for analyzing terrorists, [>]–[>], [>], [>] for analyzing voters, [>], [>], [>] for biological analysis, [>], [>] dating services' use of, [>], [>], [>], [>]–[>], [>], [>] defined, [>]–[>], [>]–[>] as Numerati tool, [>], [>], [>]–[>], [>], [>]–[>], [>], [>]–[>] Alhazmi, Nawaf, [>]–[>] Allen, Paul, [>] AllianceTech (company), [>] Almihdhar, Khalid, [>]–[>] Al Qaeda, [>], [>], [>], [>] Alzheimer's disease, [>], [>], [>], [>], [>] Amazon.com, [>], [>], [>], [>], [>] Andresen, Dan, [>]–[>], [>] ANNA (privacy software), [>] Anthropologists, [>]–[>], [>]–[>], [>], [>], [>] AOL (company), [>]–[>] Applebee's America (Dowd), [>], [>], [>] Arnold, Douglas, [>] Artificial intelligence, [>]–[>] See also Computers; Machine learning ASWORG, [>]–[>] AttentionTrust (company), [>] Baker, Mary Jane and Walter (author's parents), [>]–[>], [>], [>], [>], [>]–[>] Baker, Stephen, [>]–[>], [>]–[>] "Barnacle" shoppers, [>]–[>], [>] "Barn Raisers" tribe, [>], [>], [>], [>], [>], [>], [>] Bartering, [>] Baseball Prospective (website), [>] Baseball statistics, [>]–[>] BBN (company), [>] Bed sensors, [>], [>], [>] Behavior altering, [>]–[>], [>]–[>] predicting, [>], [>]–[>], [>], [>]–[>], [>], [>], [>], [>], [>], [>], [>]–[>], [>]–[>], [>] proxies for, [>], [>]–[>], [>] tracking of cell phone users', [>]–[>] tracking of elderly people's, [>]–[>] tracking of Internet users', [>]–[>], [>]–[>], [>], [>] tracking of terrorists', [>]–[>] tracking shoppers' patterns of, [>]–[>], [>] See also Data; Mathematical models "Behavioral markers," [>] Beltran, Carlos, [>]–[>], [>] Bin Laden, Osama, [>], [>] Biology and biologists, [>], [>], [>], [>], [>], [>], [>]–[>], [>]–[>], [>], [>] See also DNA; Genetics Black, Fischer, [>] The Black Swan (Taleb), [>] Bloggers, [>]–[>] Bluetooth data connections, [>]–[>] "Bootstrapper" tribe, [>]–[>] "Bootstrapping," [>] Brands, [>], [>], [>] Brin, Sergey, [>] Britain, [>] "Buckets," [>]–[>], [>], [>], [>]–[>], [>], [>], [>], [>], [>], [>] See also "Tribes" "Builders" (personality type), [>]–[>] Bush, George W., [>], [>]–[>], [>], [>]–[>], [>], [>] BusinessWeek (magazine), [>], [>] "Butterfly" shoppers, [>], [>] BuzzMetrics (company), [>], [>] Cameras (surveillance) at Accenture, [>], [>]–[>] in casinos, [>]–[>] in homes of the elderly, [>]–[>], [>] in public places, [>], [>], [>]–[>], [>]–[>] See also Facial recognition; Photos; Surveillance Capital IQ, [>], [>] Capital One, [>] Carbonell, Jaime, [>] Carbon nanotube, [>] Carley, Kathleen, [>]–[>] Carnegie Mellon University (CMU), [>], [>]–[>], [>]–[>], [>]–[>], [>] Casablanca (movie), [>] Casinos, [>]–[>], [>] Cavaretta, Michael, [>] Cell phones Bluetooth technology for, [>]–[>] data produced by, [>], [>], [>], [>]–[>] technical issues associated with, [>] tracking use of, [>], [>], [>]–[>] Central Intelligence Agency.


pages: 271 words: 77,448

Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will by Geoff Colvin

Ada Lovelace, autonomous vehicles, Baxter: Rethink Robotics, Black Swan, call centre, capital asset pricing model, commoditize, computer age, corporate governance, creative destruction, deskilling, en.wikipedia.org, Freestyle chess, future of work, Google Glasses, Grace Hopper, industrial cluster, industrial robot, interchangeable parts, job automation, knowledge worker, low skilled workers, Marc Andreessen, meta analysis, meta-analysis, Narrative Science, new economy, rising living standards, self-driving car, sentiment analysis, Silicon Valley, Skype, social intelligence, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, theory of mind, Tim Cook: Apple, transaction costs

WHY WE’RE TRANSFIXED BY STORIES When U.S. forces captured Saddam Hussein on December 13, 2003, U.S. bond prices rose, meaning investors were seeking safety in ultrasafe U.S. Treasury securities. The headline on Bloomberg reflected investors’ apparent aversion to risk: “U.S. Treasuries Rise; Hussein Capture May Not Curb Terrorism.” Within an hour, however, prices had fallen back. So Bloomberg rewrote its headline: “U.S. Treasuries Fall; Hussein Capture Boosts Allure of Risky Assets.” This nonsensical behavior was noted by Nassim Nicholas Taleb in his book The Black Swan, and it gives a tiny glimpse of how deeply embedded in us is the power of story. In this case, the Bloomberg headline writer could not stop himself from seeing a story—a cause-and-effect sequence of events—in the news of that day, and he apparently didn’t care that he told two directly contradictory stories, with a single cause leading to exactly opposite effects. If we want to make the most of storytelling’s extraordinary power, we need to understand how it works.

Denning told his story at a symposium on organizational storytelling, held under the auspices of the Smithsonian Associates in April 2001. It was reprinted in Storytelling in Organizations: Why Storytelling is Transforming 21st Century Organizations and Management (Elsevier Butterworth-Heinemann, 2005), pp. 97–133. Research finds that we judge a person’s trustworthiness and likeability in about a tenth of a second . . . Gino, op. cit. (chap. 4, n. 6). This nonsensical behavior . . . Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (Random House, 2007). On the contrary, for centuries the conventional view . . . This is explained in Daniel Kahneman, Thinking, Fast and Slow (Farrar, Straus and Giroux, 2011), p. 76. The Belgian psychologist Albert Michotte . . . Ibid. Daniel Kahneman, the Nobelist who popularized the notions of two separate and distinct modes of thinking . . .

Abelson, Robert, 155 Affectiva, 25, 27 affective computing, 25 Afghanistan, 50, 51, 113, 201 after-action review (AAR), 94–96, 101–6 AI (artificial intelligence), 14 airline crews, 138–39, 140 American Express, 73 Anders, George, 72 Apple, 131, 138, 208 Appletree Answers, 134 Armed Forces Journal, 93 artisans, 15 Associated Press, 20 Autonomy, 17 Average Is Over (Cowen), 30–31 Avon, 204 Azinger, Paul, 117–20 Bair, Sheila, 185–86 Bank of America, 128 Baron-Cohen, Simon, 182, 183, 191 Bartlett, Marian, 27 Barton, Dominic, 42 Battle of 73 Easting, 107–11, 112 Baum, Dan, 51 Bear, Meg, 73 Berry, James, 175 Black Swan, The (Taleb), 148 Blank Slate, The: The Modern Denial of Human Nature (Pinker), 39 Bloomberg News, 148 body language, 51, 52, 56, 129, 136, 147, 187, 189 Boissy, Adrienne, 70–71, 86–88 bonding, 63–64, 153 Bossert, William H., 45 Boyd, John, 95 Boyd, Stowe, 14 brain, 37, 58, 79, 84 and empathizing vs. systemizing, 182–84, 191 and gender differences in focus, 185 handshakes and, 60 left, 24–25, 30, 44–46 neural coupling and, 151–52 oxytocin in, 153–54, 158 right, 25, 30, 44 and scanning vs. focusing, 184–86 social relationships and, 36–40, 57, 64–66 stories and, 147, 157 synchronizing of, 64 technology and, 59 testosterone in, 187–88 brain games, 66 Brennan, Neal, 171 Brown, Donald E., 39, 145 Buffett, Warren, 171 bullying, 84 Bush, Jim, 73 business(es), 122, 175, 204 management of, 74, 189 models for, 58 training in, 97 women leaders of, 184–85 business schools, 196–98 Harvard, 60, 139, 147, 196–97 call centers, 73, 128, 134 Campbell, Chad, 119–20 cancer, 69–70, 154 capital, 13–14 Carnegie Mellon University, 122–24 cars, 30 autonomous, 3, 13, 22, 40 stress-monitoring, 29 Carter, Ashton, 52 Casebeer, William, 157–58 Casey, George, 50–51 Chappelle, Dave, 171 Charoen Pokphand Group, 205 Chatham, Ralph, 52, 100, 102, 103, 107 Chen, Jessie, 23–24 chess, 22, 30–31, 38, 40 Cink, Stewart, 119–20 cities, 172, 191 civil-confinement laws, 33–36 Clearwell, 17 Cleveland Clinic, 70, 86–88, 90, 210 cloth, 10–11 cognitive computing systems, 19 cognitive skills, 59, 65, 89, 134–35 collaborative skills, 72, 121, 174, 176–77, 189–90, 195, 203 see also teams and groups computers: affective computing and, 25 coding of, 184, 211–12 cognitive computing and, 19 creativity in, 161–65, 166, 175 in Desk Set, 7–10 emerging economy and, 183–84 emotion recognition and, 25, 27–28, 79, 125 empathy and, 79–80, 83, 90 estimations of capabilities of, 40–41, 53 group work and, 140 in military training, 111–13 power of, 5–6, 14, 24, 41, 121 Watson, 1–3, 9–10, 18, 19, 161–63, 166 writing by, 19–22, 146–48, 164, 165 Cone, Robert, 23 conformity, 135–36 conversations: gender differences in, 183, 189 in groups, 125, 127, 132 in person, 56–58, 63–66 by phone, 61–63, 67, 82, 173, 174 spoken, 61 by text, 56, 61, 63, 67, 82, 83 cooking, 161–63 Cope, David, 163 Corporate Insight, 19 Country Meadows, 158–59 Cowen, Tyler, 30–31 creativity and innovation, 131, 160, 161–77 in cities, 172 computer-generated, 161–65, 166, 175 empathy and, 175–76 engagement in, 169–71, 74 exploration in, 169, 171, 173 in groups, 169, 170, 176–77 in groups of two, 170–71 human, 165–67, 175–77 interaction and, 167–69 motivation in, 175 real-world problems and, 166–67 as set of skills, 176 trust and, 170–71 Watson and, 161–63 Crick, Francis, 170 Cuckoo’s Calling, The (Galbraith), 165 Curtis, Ben, 119–20 cyborgs, 42 DARPA (Defense Advanced Research Projects Agency), 106, 197, 201–3 storytelling studied by, 156–57 Darwars Ambush, 201–2 Death of Distance, The (Cairncross), 171–72 de Beauvoir, Simone, 170, 171 Deep Blue, 30, 40 dementia, 158–60 Denning, Stephen, 141–47 DePuy, William E., 99, 101 Desk Set, 7–10 digital devices, 55–58 dishwashers, 41 distance, 171–74 Dream On, 134 Dreyfus, Hubert, 40 Drucker, Peter, 49 economics, 133, 172 economies, 11–12, 46, 160, 209, 212, 213 emerging, women in, 125, 178–92 trust and, 64 education, 16, 44–48 online, 197–98 STEM subjects in, 49, 209 student engagement in, 28–29 work and, 16, 47–48 writing and grading software in, 20–22 Education Week, 21 edX, 21 Ekman, Paul, 25–28 Elizabeth I, Queen, 10 Emotient, 25–27 emotions, 25 computers’ reading of, 25, 27–28, 79, 125 contagion of, 76–77 digital devices and, 55–57 empathy and, see empathy facial expressions and, see facial expressions nonverbal cues and, 55–56 oxytocin and, 153 pupil size as clue to, 77–80 reading, 3, 25–30 self-knowledge of, 29–30 touch and, 59–60 empathizers vs. systemizers, 182–84, 191 empathy, 57, 69–70, 129, 140, 207, 209 building, 83–84 in children, 83–84 computers and, 79–80, 83, 90 creativity and, 175–76 decline in, 80–82, 190 definitions of, 71 disorders of, 183 emotional contagion and, 76–77 genuine, two parts of, 89 influence of, 75–76 literary fiction and, 208 medicine and, 69–71, 74–76, 84–90 military and, 91–116, 200, 204 oxytocin and, 153 pupil size and, 77–80 and saying “I understand,” 87–88 as skill, not trait, 89–90, 98 survival and, 78–79 testosterone and, 187–88 as two-way street, 79–80 unconscious and, 78 in work, 71–74 endorphins, 136–37 engagement, 169–72, 174 engineers, 49, 174, 181, 184, 211 executive functions, 65, 66 expert witnesses, 33–36, 53 exploration, 169, 171–73 eyes: eye contact, 56, 181 pupil size in, 77–80 reading, 125, 179–80, 182 F-4 Phantom fighter jet, 91–92 Facebook, 62–63, 83, 130–31 Facial Action Coding System, 26 facial expressions, 25–28, 55–56, 77–79, 125, 147, 187 eyes in, 125, 179–80, 182 pain and, 27–28, 79 factories, 16, 53–54 farming, 14 Female Advantage, The (Helgesen), 184–85 fiction, reading of, 207–8 financial crisis and recession of 2008–2009, 11–12, 47–48, 64, 82, 185–86 financial services, 19 fitness monitors, 164 Fitzgerald, F.


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This Will Make You Smarter: 150 New Scientific Concepts to Improve Your Thinking by John Brockman

23andMe, Albert Einstein, Alfred Russel Wallace, banking crisis, Barry Marshall: ulcers, Benoit Mandelbrot, Berlin Wall, biofilm, Black Swan, butterfly effect, Cass Sunstein, cloud computing, congestion charging, correlation does not imply causation, Daniel Kahneman / Amos Tversky, dark matter, data acquisition, David Brooks, delayed gratification, Emanuel Derman, epigenetics, Exxon Valdez, Flash crash, Flynn Effect, hive mind, impulse control, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Jaron Lanier, Johannes Kepler, John von Neumann, Kevin Kelly, lifelogging, mandelbrot fractal, market design, Mars Rover, Marshall McLuhan, microbiome, Murray Gell-Mann, Nicholas Carr, open economy, Pierre-Simon Laplace, place-making, placebo effect, pre–internet, QWERTY keyboard, random walk, randomized controlled trial, rent control, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Satyajit Das, Schrödinger's Cat, security theater, selection bias, Silicon Valley, Stanford marshmallow experiment, stem cell, Steve Jobs, Steven Pinker, Stewart Brand, the scientific method, Thorstein Veblen, Turing complete, Turing machine, twin studies, Vilfredo Pareto, Walter Mischel, Whole Earth Catalog, WikiLeaks, zero-sum game

The landscape provides much reason for hope, as we continue to innovate and strive to reach the balance and continuity that has served complex biological ecosystems so well for billions of years on Earth. Black Swan Technologies Vinod Khosla Technology entrepreneur and venture capitalist, Khosla Ventures; formerly general partner at Kleiner Perkins Caufield & Byers; founder, Sun Microsystems Think back to the world ten years ago. Google had just gotten started; Facebook and Twitter didn’t exist. There were no smartphones; no one had remotely conceived of the possibility of the hundred thousand iPhone apps that exist today. The few large-impact technologies (versus slightly incremental advances in technologies) that have occurred in the past ten years are black-swan technologies. In his book The Black Swan, Nassim Taleb defines a black swan as an event of low probability, extreme impact, and only retrospective predictability. Black swans can be positive or negative in their effects and are found in every sector.

., 86–87 Bayesian inference, 70 behavior, ignorance of causes of, 349–52 behavioral sciences, 365–66 belief, 336–37 proof, 355–57 Bell, Alexander Graham, 110 bell curve (Gaussian distribution), 199, 200 benchmarks, 186 bias, 18, 43–45 confirmation, 40, 134 self-serving, 37–38, 40 in technologies, 41–42 biochemical cycles, 170–71 bioengineering, 16 biological ecosystems, 312–14 biological teleology, 4 biology, 234, 312 biophilia, 386 Bird, Sheila, 274 birds, 155, 359 chickens, 62–63, 155 herring gulls, 160 songbirds, 154–55 black box, 303 Blackmore, Sue, 215–17 Black Swan, The (Taleb), 315 black-swan technologies, 314–17 Blake, William, 44 blame, 35–36, 106, 386 blindness, 144 Bloch waves, 297 Boccaletti, Giulio, 184–87 body, life-forms in, 13, 290–91, 292 Boeri, Stefano, 78 Bohr, Niels, 28 Bolyai, János, 109 Bony, Jean, 247–48 Bostrom, Nick, 275–77 bottom-up thinking, 157–59 Boyer, Pascal, 182–83 bradykinesia, 63 brain, 48, 129–30, 148, 149, 150, 158, 172, 346, 347, 389, 394 consciousness and, 217 evolution of, 10, 207, 257 mind and, 364, 366 neurons in, see neurons plasticity of, 250–51 predictive coding and, 132–34 self and, 212 size of, 257 of split-brain patients, 349–50 synapses in, 164 temperament traits and, 229–30 white and gray matter in, 162–63 Bramante, Donato, 248–49 Brand, Stewart, 15–16 Bray, Dennis, 171–72 bricolage, 271–72 Brin, Sergey, xxv Bronowski, Jacob, 340, 341–42 Brooks, David, xxv–xxviii Brown, Louise, 165 Bryson, Bill, 387 Buddha, 373 business planning, 186 Buss, David M., 353–54 Byars, James Lee, xxix–xxx Cabot, John, 90 calculus, 34, 109 Calvin, William, 201–2 cancer, 390 body scans and, 69, 259–60, 264, 265 tests for, 264–65 cannibalism, 361–62 carbon, 81, 82 carbon dioxide (CO2) emissions, 202, 207, 217, 262 car insurance, 66–67 Carr, Nicholas, 116–17 Carroll, Sean, 9–10 Cartesian science, 82–83 Caspi, Avshalom, 279 cats, 286 causality, 34–36, 58–61, 396 blame, 35–36, 106, 386 confabulation, 349–52 correlation and, 215–17, 219 of diseases, 59, 303–4 entanglement and, 331 information flow and, 218–20 nexus, 34–35 root-cause analysis, 303–4 in universe, 9–10 web of causation, 59–60, 61 central-limit theorem, 107–8 certainty, 73, 260 proof, 355–57 uselessness of, 51–52 see also uncertainty Challenger, 236 chance, 7, 18 change, 127–28, 290 fixation on, 373 chaos theory, 103, 202 character traits, 229 charitable activities, 194 cheating, 351 chess, 343 chickens, 62–63, 155 children, 148, 155, 252 chocolate, 140 cholera, 338 Chomsky, Noam, xxv Christakis, Nicholas A., xxvii, 81–83, 306 Church, George, 88–89 CINAC (“correlation is not a cause”), 215–17 civil rights movement, 370 Clark, Andy, 132–34 Clarke, Arthur C., 61 climate change, 51, 53, 99, 178, 201–2, 204, 268, 309, 315, 335, 386, 390 CO2 levels and, 202, 207, 217, 262 cultural differences in view of, 387–88 global economy and, 238–39 procrastination in dealing with, 209, 210 clinical trials, 26, 44, 56 cloning, 56, 165 coastlines, xxvi, 246 Cochran, Gregory, 360–62 coffee, 140, 152, 351 cognition, 172 perception and, 133–34 cognitive humility, 39–40 cognitive load, 116–17 cognitive toolkit, 333 Cohen, Daniel, 254 Cohen, Joel, 65 Cohen, Steven, 307–8 cold fusion, 243, 244 Coleman, Ornette, 254, 255 collective intelligence, 257–58 Colombia, 345 color, 150–51 color-blindness, 144 Coltrane, John, 254–55 communication, 250, 358, 372 depth in, 227 temperament and, 231 companionship, 328–29 comparative advantage, law of, 100 comparison, 201 competition, 98 complexity, 184–85, 226–27, 326, 327 emergent, 275 computation, 227, 372 computers, 74, 103–4, 146–47, 172 cloud and, 74 graphical desktops on, 135 memory in, 39–40 open standards and, 86–87 computer software, 80, 246 concept formation, 276 conduction, 297 confabulation, 349–52 confirmation bias, 40, 134 Conner, Alana, 367–70 Conrad, Klaus, 394 conscientiousness, 232 consciousness, 217 conservatism, 347, 351 consistency, 128 conspicuous consumption, 228, 308 constraint satisfaction, 167–69 consumers, keystone, 174–76 context, sensitivity to, 40 continental drift, 244–45 conversation, 268 Conway, John Horton, 275, 277 cooperation, 98–99 Copernicanism, 3 Copernican Principle, 11–12, 25 Copernicus, Nicolaus, 11, 294 correlation, and causation, 215–17, 219 creationism, 268–69 creativity, 152, 395 constraint satisfaction and, 167–69 failure and, 79, 225 negative capability and, 225 serendipity and, 101–2 Crick, Francis, 165, 244 criminal justice, 26, 274 Croak, James, 271–72 crude look at the whole (CLAW), 388 Crutzen, Paul, 208 CT scans, 259–60 cultural anthropologists, 361 cultural attractors, 180–83 culture, 154, 156, 395 change and, 373 globalization and, see globalization culture cycle, 367–70 cumulative error, 177–79 curating, 118–19 currency, central, 41 Cushman, Fiery, 349–52 cycles, 170–73 Dalrymple, David, 218–20 DALYs (disability-adjusted life years), 206 danger, proving, 281 Darwin, Charles, 2, 44, 89, 98, 109, 156, 165, 258, 294, 359 Das, Satyajit, 307–9 data, 303, 394 personal, 303–4, 305–6 security of, 76 signal detection theory and, 389–93 Dawkins, Richard, 17–18, 180, 183 daydreaming, 235–36 DDT, 125 De Bono, Edward, 240 dece(i)bo effect, 381–85 deception, 321–23 decision making, 52, 305, 393 constraint satisfaction and, 167–69 controlled experiments and, 25–27 risk and, 56–57, 68–71 skeptical empiricism and, 85 deduction, 113 defeasibility, 336–37 De Grey, Aubrey, 55–57 delaying gratification, 46 democracy, 157–58, 237 Democritus, 9 Demon-Haunted World, The (Sagan), 273 Dennett, Daniel C., 170–73, 212, 275 depth, 226–28 Derman, Emanuel, 115 Descent of Man, The (Darwin), 156 design: mind and, 250–53 recursive structures in, 246–49 determinism, 103 Devlin, Keith, 264–65 Diagnostic and Statistical Manual of Mental Disorders (DSM-5), 233–34 “Dial F for Frankenstein” (Clarke), 61 Diesel, Rudolf, 170 diseases, 93, 128, 174 causes of, 59, 303–4 distributed systems, 74–77 DNA, 89, 165, 223, 244, 260, 292, 303, 306 Huntington’s disease and, 59 sequencing of, 15 see also genes dopamine, 230 doughnuts, 68–69, 70 drug trade, 345 dualities, 296–98, 299–300 wave-particle, 28, 296–98 dual view of ourselves, 32 dynamics, 276 Eagleman, David, 143–45 Earth, 294, 360 climate change on, see climate change distance between sun and, 53–54 life on, 3–5, 10, 15 earthquakes, 387 ecology, 294–95 economics, 100, 186, 208, 339 economy(ies), 157, 158, 159 global, 163–64, 238–39 Pareto distributions in, 198, 199, 200 and thinking outside of time, 223 ecosystems, 312–14 Edge, xxv, xxvi, xxix–xxx education, 50, 274 applying to real-world situations, 40 as income determinant, 49 policies on, controlled experiments in, 26 scientific lifestyle and, 20–21 efficiency, 182 ego: ARISE and, 235–36 see also self 80/20 rule, 198, 199 Einstein, Albert, 28, 55, 169, 301, 335, 342 on entanglement, 330 general relativity theory of, 25, 64, 72, 234, 297 memory law of, 252 on simplicity, 326–27 Einstellung effect, 343–44 electrons, 296–97 Elliott, Andrew, 150 Eliot, T.

., 310–11 Smith, John Maynard, 96 Smolin, Lee, 221–24 social microbialism, 16 social networks, 82, 262, 266 social sciences, 273 Socrates, 340 software, 80, 246 Solomon Islands, 361 something for nothing, 84 specialness, see uniqueness and specialness Sperber, Dan, 180–83 spider bites, 68, 69, 70 spoon bending, 244 stability, 128 Standage, Tom, 281 stars, 7, 128, 301 statistically significant difference, 378–80 statistics, 260, 356 stem-cell research, 56, 69–70 stock market, 59, 60–61, 151, 339 Flash Crash and, 60–61 Pareto distributions and, 199, 200 Stodden, Victoria, 371–72 stomach ulcers, 240 Stone, Linda, 240–41 stress, 68, 70, 71 string theories, 113, 114, 299, 322 subselves and the modular mind, 129–31 success, failure and, 79–80 sun, 1, 7, 11, 164 distance between Earth and, 53–54 sunk-cost trap, 121 sunspots, 110 Superorganism, The (Hölldobler and Wilson), 196–97 superorganisms, 196 contingent, 196–97 supervenience, 276, 363–66 Susskind, Leonard, 297 Swets, John, 391 symbols and images, 152–53 synapses, 164 synesthesia, 136–37 systemic equilibrium, 237–39 Szathmáry, Eörs, 96 Taleb, Nassim, 315 TANSTAAFL (“There ain’t no such thing as a free lunch”), 84 Tapscott, Don, 250–53 taste, 140–42 tautologies, 355–56 Taylor, F. W., 186 Taylor, G. I., 185–86 Taylor, Timothy, 333 Taylorism, 186 technology(ies), 223, 249, 251, 257, 259, 273, 315 biases in, 41–42 black-swan, 315–17 humanity and, 333 Tegmark, Max, 19–22 telepathy, 244, 245 telephone game, 177, 178, 179 television, 287 temperament dimensions, 229–31 temperature, 151–52 ten, powers of, 162–64 terrorism, 69, 262, 264, 265 September 11 attacks, 386 testosterone, 230, 231 Thaler, Richard, 338–39 theater, science vs., 262–63 theory, effective, 192–93 Theory of Everything, 365 There’s No Such Thing as a Free Lunch (Friedman), 84 thermodynamics, 108, 227, 237, 302 Thich Nhat Hanh, 289 ’t Hooft, Gerard, 297 thought, thinking, 395 bottom-up vs. top-down, 157–59 design for, 250–53 flow of, 211–13 language and, 242 projective, 240–41 reactive, 240 thought, thinking (cont.)


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Capitalism: Money, Morals and Markets by John Plender

activist fund / activist shareholder / activist investor, Andrei Shleifer, asset-backed security, bank run, Berlin Wall, Big bang: deregulation of the City of London, Black Swan, bonus culture, Bretton Woods, business climate, business cycle, Capital in the Twenty-First Century by Thomas Piketty, central bank independence, collapse of Lehman Brothers, collective bargaining, computer age, Corn Laws, corporate governance, creative destruction, credit crunch, Credit Default Swap, David Ricardo: comparative advantage, deindustrialization, Deng Xiaoping, discovery of the americas, diversification, Eugene Fama: efficient market hypothesis, eurozone crisis, failed state, Fall of the Berlin Wall, fiat currency, financial innovation, financial intermediation, Fractional reserve banking, full employment, God and Mammon, Gordon Gekko, greed is good, Hyman Minsky, income inequality, inflation targeting, information asymmetry, invention of the wheel, invisible hand, Isaac Newton, James Watt: steam engine, Johann Wolfgang von Goethe, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, joint-stock company, Joseph Schumpeter, labour market flexibility, liberal capitalism, light touch regulation, London Interbank Offered Rate, London Whale, Long Term Capital Management, manufacturing employment, Mark Zuckerberg, market bubble, market fundamentalism, mass immigration, means of production, Menlo Park, money market fund, moral hazard, moveable type in China, Myron Scholes, Nick Leeson, Northern Rock, Occupy movement, offshore financial centre, paradox of thrift, Paul Samuelson, plutocrats, Plutocrats, price stability, principal–agent problem, profit motive, quantitative easing, railway mania, regulatory arbitrage, Richard Thaler, rising living standards, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, shareholder value, short selling, Silicon Valley, South Sea Bubble, spice trade, Steve Jobs, technology bubble, The Chicago School, The Great Moderation, the map is not the territory, The Wealth of Nations by Adam Smith, Thorstein Veblen, time value of money, too big to fail, tulip mania, Upton Sinclair, Veblen good, We are the 99%, Wolfgang Streeck, zero-sum game

That applies in spades to the global financial crisis and subsequent Great Recession. Many have been tempted to attribute the crisis to what the author Nassim Nicholas Taleb calls black swans, or high-impact, hard-to-predict events that are beyond the realm of normal expectations. Yet anyone who knew anything about the financial crises of 1907, which prompted the creation of the US Federal Reserve, and of 1929, which led to the 1930s depression, would have been well equipped to see the risks in the property bubble that preceded the latest crisis. Indeed, financial crises have been normal, regular events since the invention of modern banking. And Nassim Nicholas Taleb was himself immensely prescient about the crisis. Those black swans, if the reader will excuse a solecism, were a canard. With financial crises the problem of prediction, as we saw earlier, is about timing and scale rather than the probability of them happening.

Muller. 204 This was attributed to Coolidge in the Reader’s Digest of June 1960, but I have been unable to find the original source. 205 http://www.project-syndicate.org/commentary/a-crisis-in-two-narratives 206 See Efficiency, Equality and the Ownership of Private Property, Harvard University Press, 1964. I was alerted to this by Benjamin M. Friedman’s thought-provoking article ‘“Brave New Capitalists’ Paradise”: The Jobs?’ in the New York Review of Books, 7 November 2013. 207 The Black Swan: The Impact of the Highly Improbable, Random House, 2007. 208 http://www.worldbank.org/en/topic/poverty/overview 209 Divided We Stand: Why Inequality Keeps Rising, OECD, 2011. 210 See Jon Bakija, Adam Cole, and Bradley T. Heim, ‘Jobs and Income Growth of Top Earners and the Causes of Changing Income Inequality: Evidence from US Tax Return Data’, Department of Economics Working Paper, Williams College, Williamstown, MA, 2012. 211 Figures from Lawrence Mishel of the Economic Policy Institute of the US, see http://www.epi.org/publication/ceo-pay-231-times-greater-average-worker 212 ‘The Crises of Democratic Capitalism’, New Left Review, September–October 2011. 213 Remarks at THEARC, Washington DC, 4 December 2013. 214 ‘What did QE achieve, apart from boosting the price of Warhols?’

Wilson) 1, 2 Alberti, Leon Battista 1 Alessandri, Piergiorgio 1 Allen, Maurice 1 Ambassadors, The (Henry James) 1 Americans for Tax Reform 1 Anatomy of Change-Alley (Daniel Defoe) 1 Angell, Norman 1 Anglosphere 1, 2 Arab Spring 1 Aramaic 1 arbitrage 1 Argentina 1 Aristotle 1, 2, 3, 4, 5, 6, 7, 8, 9 art 1 Asian Tiger economies 1 Atlas Shrugged (Ayn Rand) 1 Austen, Jane 1 Austrian school 1 aviation 1 Babbitt (Sinclair Lewis) 1 Bair, Sheila 1 Balloon Dog (Orange) (sculpture) 1 Balzac 1 Bank for International Settlements 1, 2, 3, 4, 5, 6 Bank of England 1, 2, 3, 4, 5 bank runs 1 bankers 1, 2 bankruptcy laws 1, 2 Banks, Joseph 1 Banksy 1 Barbon, Nicholas 1, 2, 3 Bardi family 1 Barings 1 Baruch, Bernard 1, 2 base metal, transmutation into gold 1 Basel regulatory regime 1, 2, 3 Baudelaire, Charles 1 Baum, Frank 1 behavioural finance 1 Belgium 1, 2 Bell, Alexander Graham 1 Benjamin, Walter 1 Bernanke, Ben 1, 2, 3 Bi Sheng 1 Bible 1 bimetallism 1 Bismarck, Otto von 1 Black Monday (1987) 1 black swans 1 Blake William 1, 2, 3 Bloch, Marcel 1 Bloomsbury group 1, 2 Boccaccio 1 bond market 1 bonus culture 1 Bootle, Roger 1 Boston Tea Party 1 Boswell, James 1 Boulton, Matthew 1 Bowra, Maurice 1 Brandeis, Louis 1 Bretton Woods conference 1 British Land (property company) 1 British Rail pension fund 1 Brookhart, Smith 1, 2 Brunner, Karl 1 Bryan, William Jennings 1 Bubble Act (Britain 1720) 1 bubbles 1, 2, 3 Buchanan, James 1 Buffett, Warren 1, 2, 3 Buiter, Willem 1 Burdett, Francis 1 van Buren, Martin 1 Burke, Edmund 1, 2 Burns, Robert 1 Bush, George W. 1, 2 Butler, Samuel 1 Candide (Voltaire) 1 Carlyle, Thomas 1, 2, 3 Carnegie, Andrew 1 Carville, James 1 cash nexus 1 Cash Nexus, The (Niall Ferguson) 1 Cassel, Ernest 1, 2 Catholic Church 1, 2, 3 Cecchetti, Stephen 1 Centre for the Study of Capital Market Dysfunctionality, (London School of Economics) 1 central bankers 1 Cervantes 1 Chamberlain, Joseph 1 Chancellor, Edward 1 Chapter 11 bankruptcy 1 Charles I of England 1, 2 Charles II of England 1 Chaucer 1 Cheney, Dick 1 Chernow, Ron 1 Chicago school 1, 2 Child & Co. 1 China 1, 2 American dependence on 1, 2 industrialisation 1, 2, 3 manufacturing 1 paper currency 1 Christianity 1, 2, 3, 4, 5 Churchill, Winston 1 Cicero 1, 2 Citizens United case 1 Cleveland, Grover 1 Clyde, Lord (British judge) 1 Cobden, Richard 1, 2, 3, 4 Coggan, Philip 1 Cohen, Steven 1 Colbert, Jean-Baptiste 1, 2 Cold War 1 Columbus, Christopher 1 commodity futures 1 Companies Act (Britain 1862) 1 Condition of the Working Class in England (Engels) 1 Confucianism 1, 2, 3 conquistadores 1 Constitution of Liberty, The (Friedrich Hayek) 1 Coolidge, Calvin 1, 2, 3 Cooper, Robert 1 copyright 1 Cort, Cornelis 1 Cosimo the Elder 1 crash of 1907 1 crash of 1929 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 creative destruction 1, 2 credit crunch (2007) 1, 2, 3 cum privilegio 1 Cyprus 1, 2 Dale, Richard 1, 2 Dante 1 Darwin, Erasmus 1 Das Kapital (Karl Marx) 1 Dassault, Marcel 1 Daunton, Martin 1 Davenant, Charles 1, 2, 3 Davies, Howard 1 debt 1 debt slavery 1 Decameron (Boccaccio) 1 Defoe, Daniel 1, 2, 3, 4, 5, 6, 7, 8 Dell, Michael 1 Deng Xiaoping 1, 2 derivatives 1 Deserted Village, The (Oliver Goldsmith) 1, 2, 3 Devil Take the Hindmost (Edward Chancellor) 1 Dickens, Charles 1, 2, 3, 4, 5, 6, 7, 8, 9 portentously named companies 1 Die Juden und das Wirtschaftsleben (Werner Sombart) 1 A Discourse of Trade (Nicholas Barbon) 1 Ding Gang 1 direct taxes 1, 2 Discorsi (Machiavelli) 1 diversification 1 Dodd–Frank Act (US 2010) 1, 2, 3 ‘dog and frisbee’ speech 1 dot.com bubble 1, 2, 3, 4 Drayton, Harley 1 Dumas, Charles 1, 2 Dürer, Albrecht 1 Duret, Théodore 1, 2 Dutch East India Company 1 Duttweiler, Gottlieb 1 Dye, Tony 1 East of Eden (film version) 1 Economic Consequences of the Peace (Keynes) 1, 2 Edison, Thomas 1, 2 efficient market hypothesis 1 electricity 1 Eliot, T.


pages: 1,088 words: 228,743

Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen

Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, G4S, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stocks for the long run, survivorship bias, systematic trading, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game

Sun, Zheng; Ashley Wang; and Lu Zheng (2009), “The road less traveled: Strategy distinctiveness and hedge fund performance,” working paper, available at SSRN: http://ssrn.com/ abstract = 1337424 Swensen, David F. (2009), Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment (Revised Edition), New York: Free Press. Taleb, Nassim Nicholas (2001), Fooled by Randomness: The Hidden Role of Chance in the Markets and Life, New York: Texere. Taleb, Nassim Nicholas (2004), “Bleed or blowup? What does empirical psychology tell us about the preference for negative skewness?” Journal of Behavioral Finance 5(1), 2–7. Taleb, Nassim Nicholas (2007), The Black Swan: The Impact of the Highly Improbable, New York: Penguin Books. Takats, Elod (2010), “Ageing and asset prices,” BIS working paper 318. Teo, Melvyn (2009), “Does size matter in the hedge fund industry?” working paper, available at SSRN: http://ssrn.com/abstract=1331754 Teo, Melvyn (2010), “How liquid are liquid hedge funds?”

These caveats are more relevant to forming a skeptical assessment of high average returns than they are for critiquing findings of time series predictability. Peso problem Return regularities may also reflect an abnormal or unrepresentative sample period (say, 2003–2007). The peso problem refers to the difficulty of assessing time series that are plagued by important rare events which may or may not be realized in sample—closely linked to fat tails and black swans (see Rogoff, 1980; Taleb, 2007) [1]. Peso problems are especially important when we interpret the performance of assets or strategies with asymmetric return distributions. If a rare adverse event to which the market assigns a low probability does not materialize during the sample period, selling insurance against such an event appears highly profitable. Just consider the absence of double-digit equity market crashes after October 1987 and the persistent profitability of selling out-of-the-money put options over the two decades following the crash.

The analogy, forced though it may be, continues to work as erosion, or expected return, might be mind-numbingly boring at any one moment, but it has a heck of a lot to say about how the future will be shaped. For perspective, you want to see a black swan? Try making nothing on your equities in your final 20 years until retirement because you bought them at an extremely high price (low expected return). That is one pile of ebony long-necked bird. If risk is about surviving the short term, expected return is about whether, after surviving, you think it was worth it. Here’s another secret. Besides being at least as important, estimating expected returns is much harder than estimating risk. Now, estimating risk is no easy task. But at least finance wears its insecurity on its sleeve here. Are returns “normal” or do we have to worry about “black swans”, “fat tails”, and all that? This worry and effort is worthwhile, but those trying to estimate the expected return on various assets smile at those taking on the comparatively easy task of estimating risk.


pages: 726 words: 172,988

The Bankers' New Clothes: What's Wrong With Banking and What to Do About It by Anat Admati, Martin Hellwig

Andrei Shleifer, asset-backed security, bank run, banking crisis, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, bonus culture, break the buck, business cycle, Carmen Reinhart, central bank independence, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, diversified portfolio, en.wikipedia.org, Exxon Valdez, financial deregulation, financial innovation, financial intermediation, fixed income, George Akerlof, Growth in a Time of Debt, income inequality, information asymmetry, invisible hand, Jean Tirole, joint-stock company, joint-stock limited liability company, Kenneth Rogoff, Larry Wall, light touch regulation, London Interbank Offered Rate, Long Term Capital Management, margin call, Martin Wolf, money market fund, moral hazard, mortgage debt, mortgage tax deduction, negative equity, Nick Leeson, Northern Rock, open economy, peer-to-peer lending, regulatory arbitrage, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, Satyajit Das, shareholder value, sovereign wealth fund, technology bubble, The Market for Lemons, the payments system, too big to fail, Upton Sinclair, Yogi Berra

At the time, Wuffli was the chief financial officer and Gummerlock the chief risk officer of Swiss Bank Corporation, which later merged into UBS; Freeland was the deputy secretary general of the Basel Committee for Banking Supervision. The limitations of quantitative models and stress tests are discussed in Chapter 11. 50. Taleb (2001, 2010) refers to such risks as “black swan” risks. Black swans are events that have been deemed impossible and that have significant consequences when they occur anyway. Taleb gives several examples in which neglect of black swan risk led to disaster. Das (2010, Chapter 5) discusses the pitfalls of “risk management by the numbers,” including the story of LTCM. Gillian Tett, in “Clouds Sighted off CDO Asset Pool” (Financial Times, April 18, 2005), noted that “if a nasty accident did ever occur with CDOs, it could ricochet through the financial system in unexpected ways,” and that “while banks insist that these risks can be accurately measured by their models … projecting default probabilities remains an art, not science.”

Summers, Lawrence H. 2000. “International Financial Crises: Causes, Prevention, and Cure.” Richard T. Ely Lecture. American Economic Review 90 (2): 1–16. Sundaresan, Suresh, and Zhenyu Wang. 2010. “Design of Contingent Capital with Stock Price Trigger for Conversion.” Staff Report 448. Federal Reserve Bank of New York, New York. April 23. Taleb, Nassim N. 2001. Fooled by Randomness: The Hidden Role of Chance in the Markets and in Life. New York: W. W. Norton. ———. 2010. The Black Swan, Second Edition: The Impact of the Highly Improbable, with a New Section: On Robustness and Fragility. New York: Random House. Tarullo, Daniel K. 2008. Banking on Basel: The Future of International Financial Regulation. Washington, DC: Peter G. Peterson Institute of International Economics. Tett, Gillian. 2009. Fool’s Gold: How the Bold Dreams of a Small Tribe at J.

., 242n20, 243n27, 290n29, 292n41, 308n44 Berglöf, Erik, 253n35 Bernanke, Ben, 11, 238n47, 246n16, 249n15, 253n38, 330n13 Berra, Yogi, 109, 110, 129, 141 Better Markets, 233n19, 237n42, 259n32, 265n4, 288n13, 327n66 Bhagat, Sanjai, 284n24, 284n27, 285n35 Bhide, Amar, 282n14 Big Short, The (Lewis), 60 bills of exchange, as liquid assets, 272n45 BIS. See Bank for International Settlements Black Rock, 257n16 black swan risks, 261n50 blanket guarantees, 139, 142–43, 146, 287n6, 291n30 Blankfein, Lloyd, 230n8 Bloomberg, data on Fed lending programs, 288n14 BNP Paribas: formed by merger of Banque Nationale de Paris and Compagnie Financière de Paris et des Pays-Bas, 269n28, 324n45; liquidity problems of, 256n13 boards of directors, corporate: conflicts of interest in, 126–27; in culture of ROE, 126–27, 285n32; focus of, 126; responses to price declines, 106, 277n13; responsibilities of, 285n32 boards of directors, of Federal Reserve banks, 205 Bolton, Brian, 284n27, 285n35 Bolton, Patrick, 300n54, 306n29 Bomhard, Nikolaus von, 327n65 bonds: as liquid assets, 272n44; required return on, 107, 277n14.


pages: 484 words: 136,735

Capitalism 4.0: The Birth of a New Economy in the Aftermath of Crisis by Anatole Kaletsky

"Robert Solow", bank run, banking crisis, Benoit Mandelbrot, Berlin Wall, Black Swan, bonus culture, Bretton Woods, BRICs, business cycle, buy and hold, Carmen Reinhart, cognitive dissonance, collapse of Lehman Brothers, Corn Laws, correlation does not imply causation, creative destruction, credit crunch, currency manipulation / currency intervention, David Ricardo: comparative advantage, deglobalization, Deng Xiaoping, Edward Glaeser, Eugene Fama: efficient market hypothesis, eurozone crisis, experimental economics, F. W. de Klerk, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, George Akerlof, global rebalancing, Hyman Minsky, income inequality, information asymmetry, invisible hand, Isaac Newton, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kickstarter, laissez-faire capitalism, Long Term Capital Management, mandelbrot fractal, market design, market fundamentalism, Martin Wolf, money market fund, moral hazard, mortgage debt, Nelson Mandela, new economy, Northern Rock, offshore financial centre, oil shock, paradox of thrift, Pareto efficiency, Paul Samuelson, peak oil, pets.com, Ponzi scheme, post-industrial society, price stability, profit maximization, profit motive, quantitative easing, Ralph Waldo Emerson, random walk, rent-seeking, reserve currency, rising living standards, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, short selling, South Sea Bubble, sovereign wealth fund, special drawing rights, statistical model, The Chicago School, The Great Moderation, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, Vilfredo Pareto, Washington Consensus, zero-sum game

Mandelbrot’s analysis, presented to nonspecialist readers in his 2004 book (Mis)behavior of Markets, shows with mathematical certainty that these standard statistical models based on neoclassical definitions of efficient markets and rational expectations among investors cannot be true. Had these models been valid, events such as the 1987 stock market crash and the bankruptcy of the 1998 hedge fund crisis would not have occurred even once in the fifteen billion years since the creation of the universe.9 In fact, four such extreme events occurred in just two weeks after the Lehman bankruptcy. Mandelbrots’s ideas were popularized by Nassim Taleb in Fooled by Randomness and The Black Swan.10 These books, and the mathematical research they reflect, show that movements in financial prices are not “normally” distributed11 and that markets are much riskier than standard models indicate. The implication is that all the standard risk-management employed by bankers, regulators, and credit-rating agencies before the Lehman crisis were deeply flawed, and their use was bound eventually to produce enormous losses leading to a total breakdown of the financial system.

For more details, see Chapter 11. 4 This accelerator-multiplier concept, first proposed by Sir Roy Harrod, was later refined by Paul Samuelson and Sir John Hicks and became the standard Keynesian business cycle model. 5 Justin Lahart, “In Time of Tumult, Obscure Economist Gains Currency,” Wall Street Journal, August 18, 2007. 6 George Soros, The Soros Lectures: At the Central European University. 7 Alan Greenspan, “The Challenge of Central Banking,” remarks at the Annual Dinner and Francis Boyer Lecture of the American Enterprise Institute for Public Policy Research, Washington, DC, December 5, 1996. Available from http://www.federalreserve.gov/boarddocs/speeches/1996/19961205.htm. 8 Robert Shiller, Irrational Exuberance. 9 Benoit Mandelbrot and Richard Hudson, The (Mis)behavior of Markets: A Fractal View of Risk, Ruin and Reward, 4. 10 Nassim Nicholas Taleb, Fooled by Randomness: The Hidden Role of Chance in the Markets and in Life and the Black Swan: The Impact of the Highly Probable. 11 The term normal distribution describes prices or any other form of data that cluster predictably and reliably around a mean value in a bell curve pattern. 12 Malcolm C. Sawyer, The Economics of Michal Kalecki. See also Robert Rowthorn, “The Political Economy of Full Employment in Modern Britain,” Kalecki Memorial Lecture, Department of Economics, University of Oxford, October 19, 1999. 13 Robert Skidelsky, Keynes: The Return of the Master, 62. 14 The question is taken up on IBM’s Web site: http://www-03.ibm.com/ibm/history/reference/faq_0000000047.html. 15 Gartner, Inc., “Press Release: Gartner Says Worldwide PC Shipments to Grow 2.8 Percent in 2009, but PC Revenue to Decline 11 Percent,” November 23, 2009. 16 Nicolo Machiavelli, The Prince, 88.

Available from www.house.gov/apps/list/hearing/financialsvcs_dem/stiglitz.pdf. Stiglitz, Joseph, Jaime Jaramillo-Vallejo, and Yung Chal Park. “The Role of the State in Financial Markets.” World Bank Research Observer Annual Conference on Development Economics Supplement (1993): 19-61. Studwell, Joe. “Nurturing the Chinese Economy.” Far Eastern Economic Review (December 2009). Swift, Jonathan. Gulliver’s Travels. New York: Penguin Classics, 2003. Taleb, Nassim Nicholas. The Black Swan: The Impact of the Highly Probable. New York: Random House, 2007. ——. Fooled by Randomness: The Hidden Role of Chance in the Markets and in Life. New York: W.W. Norton, 2001. Tobin, James. “The Monetarist Counter-Revolution Today—An Appraisal.” Cowles Foundation Paper No. 532. Yale University, 1981. Available from http://cowles.econ.yale.edu/P/cp/p05a/p0532.pdf. ——. “Stabilization Policy Ten Years After.”


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A Man for All Markets by Edward O. Thorp

3Com Palm IPO, Albert Einstein, asset allocation, beat the dealer, Bernie Madoff, Black Swan, Black-Scholes formula, Brownian motion, buy and hold, buy low sell high, carried interest, Chuck Templeton: OpenTable:, Claude Shannon: information theory, cognitive dissonance, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Edward Thorp, Erdős number, Eugene Fama: efficient market hypothesis, financial innovation, George Santayana, German hyperinflation, Henri Poincaré, high net worth, High speed trading, index arbitrage, index fund, interest rate swap, invisible hand, Jarndyce and Jarndyce, Jeff Bezos, John Meriwether, John Nash: game theory, Kenneth Arrow, Livingstone, I presume, Long Term Capital Management, Louis Bachelier, margin call, Mason jar, merger arbitrage, Murray Gell-Mann, Myron Scholes, NetJets, Norbert Wiener, passive investing, Paul Erdős, Paul Samuelson, Pluto: dwarf planet, Ponzi scheme, price anchoring, publish or perish, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, RFID, Richard Feynman, risk-adjusted returns, Robert Shiller, Robert Shiller, rolodex, Sharpe ratio, short selling, Silicon Valley, Stanford marshmallow experiment, statistical arbitrage, stem cell, stocks for the long run, survivorship bias, The Myth of the Rational Market, The Predators' Ball, the rule of 72, The Wisdom of Crowds, too big to fail, Upton Sinclair, value at risk, Vanguard fund, Vilfredo Pareto, Works Progress Administration

Profits of less than 1 percent annualized were magnified with borrowed money into yearly returns of 40 percent or so. As long as the world of asset prices was normal, all was well, but just as in 1929 when investors on 10 percent margin were wiped out by a small reversal in prices, so LTCM, with its margin ranging from 1 to 3 percent, was ruined by a sea change in markets. As Nassim Taleb points out eloquently in his book The Black Swan, apparent excess returns like those for LTCM in normal times may be illusory as they may be more than offset by infrequent large losses from extreme events. Such “black swans” can be bad for some and good for others. Ironically, having passed in 1994 on the chance to invest in LTCM and temporarily get rich, I made money in 1998 by exploiting the distorted market prices left in the wake of their collapse. LTCM’s loss was our gain in Ridgeline Partners. LTCM’s collapse threatened to put $100 billion or so of bad assets on the books of other institutions.

New York: Hill and Wang, 2005. Schroeder, Alice. The Snowball: Warren Buffett and the Business of Life. New York: Bantam, 2008. Segel, Joel. Recountings: Conversations with MIT Mathematicians. Wellesley, MA: A K Peters/CRC Press, 2009. Siegel, Jeremy J. Stocks for the Long Run: The Definitive Guide to Financial Market Returns and Long-Term Investment Strategies. New York: McGraw-Hill, 2008. Taleb, Nassim Nicholas. The Black Swan: The Impact of the Highly Improbable. New York: Random House, 2007. ———. Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. New York: Random House, 2005. Thorp, Edward O., and Sheen T. Kassouf. Beat the Market: A Scientific Stock Market System. New York: Random House, 1967. Available at www.edwardothorp.com. ———. Beat the Dealer: A Winning Strategy for the Game of Twenty-One.

However, to price them accurately we need to know how much we are going to lose on defaults. When I studied this for Princeton Newport, I learned that the practice in the financial industry was to assume that default rates would follow normal historical experience. There was no attempt to quantify and adjust for infrequent large-scale bad events like the Great Depression, and the massive increase in defaults that could occur. The models failed to incorporate Black Swan risk into the pricing. Another problem was forecasting the rate at which homeowners might pay off their mortgages early, perhaps to refinance their existing home. A thirty-year mortgage held for the full period is much like a long-term bond. Paid off in five to ten years, it is more like an intermediate bond, and if it is retired in two or three years, the payments resemble those from a very short-term bond.


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The End of Nice: How to Be Human in a World Run by Robots (Kindle Single) by Richard Newton

3D printing, Black Swan, British Empire, Buckminster Fuller, Clayton Christensen, crowdsourcing, deliberate practice, disruptive innovation, fear of failure, Filter Bubble, future of work, Google Glasses, Isaac Newton, James Dyson, Jaron Lanier, Jeff Bezos, job automation, lateral thinking, Lean Startup, low skilled workers, Mark Zuckerberg, move fast and break things, move fast and break things, Paul Erdős, Paul Graham, recommendation engine, rising living standards, Robert Shiller, Robert Shiller, Silicon Valley, Silicon Valley startup, skunkworks, social intelligence, Steve Ballmer, Steve Jobs, Y Combinator

In his book The Black Swan, the author Nassim Taleb said: “The narrative fallacy addresses our limited ability to look at sequences of facts without weaving an explanation into them, or, equivalently, forcing a logical link, an arrow of relationship upon them. Explanations bind facts together. They make them all the more easily remembered; they help them make more sense. Where this propensity can go wrong is when it increases our impression of understanding.” (In his book, Nassim Taleb explained that our certainties are liable to be unravelled in an instant because of the partial information on which they are based. Thus it was once a certain fact that all swans were white because no one had seen a swan of another colour. Then in 1697, the Dutch explorer Willem de Vlaming discovered black swans in Australia.)


pages: 524 words: 143,993

The Shifts and the Shocks: What We've Learned--And Have Still to Learn--From the Financial Crisis by Martin Wolf

air freight, anti-communist, Asian financial crisis, asset allocation, asset-backed security, balance sheet recession, bank run, banking crisis, banks create money, Basel III, Ben Bernanke: helicopter money, Berlin Wall, Black Swan, bonus culture, break the buck, Bretton Woods, business cycle, call centre, capital asset pricing model, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, collateralized debt obligation, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, debt deflation, deglobalization, Deng Xiaoping, diversification, double entry bookkeeping, en.wikipedia.org, Erik Brynjolfsson, Eugene Fama: efficient market hypothesis, eurozone crisis, Fall of the Berlin Wall, fiat currency, financial deregulation, financial innovation, financial repression, floating exchange rates, forward guidance, Fractional reserve banking, full employment, global rebalancing, global reserve currency, Growth in a Time of Debt, Hyman Minsky, income inequality, inflation targeting, information asymmetry, invisible hand, Joseph Schumpeter, Kenneth Rogoff, labour market flexibility, labour mobility, light touch regulation, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, mandatory minimum, margin call, market bubble, market clearing, market fragmentation, Martin Wolf, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mortgage debt, negative equity, new economy, North Sea oil, Northern Rock, open economy, paradox of thrift, Paul Samuelson, price stability, private sector deleveraging, purchasing power parity, pushing on a string, quantitative easing, Real Time Gross Settlement, regulatory arbitrage, reserve currency, Richard Feynman, risk-adjusted returns, risk/return, road to serfdom, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, Second Machine Age, secular stagnation, shareholder value, short selling, sovereign wealth fund, special drawing rights, The Chicago School, The Great Moderation, The Market for Lemons, the market place, The Myth of the Rational Market, the payments system, The Wealth of Nations by Adam Smith, too big to fail, Tyler Cowen: Great Stagnation, very high income, winner-take-all economy, zero-sum game

The Austrian school derives from the work of pre-Second World War Austrian economists, particularly Ludwig von Mises and Friedrich Hayek, but is now most influential in the US. Thus, ‘Austrian’ refers to a set of staunchly free-market ideas, not to the nationality of the believers. 7. Nouriel Roubini and Stephen Mihm, Crisis Economics: A Crash Course in the Future of Finance (London: Penguin, 2011), ch. 1. For ‘white swan’ and ‘black swan’ events, see Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007). 8. For a succinct discussion of Minsky’s views of big government and central banks, see Hyman P. Minsky, ‘Can “It” Happen Again? A Reprise’ (1982), Hyman P. Minsky Archive, Paper 155. http://digitalcommons.bard.edu/hm_archive/155. 9. The preface to Why Globalization Works discusses my intellectual history.

‘Why Stagnation Might Prove to Be the New Normal’, 15 December 2013, Financial Times. http://www.ft.com/cms/s/2/87cb15ea-5d1a-11e3-a558-00144feabdc0.html. Summers, Lawrence and Martin Wolf. ‘A Conversation on New Economic Thinking’, Bretton Woods Conference, Institute for New Economic Thinking, 8 April 2011. http://ineteconomics.org/video/bretton-woods/larry-summers-and-martin-wolf-new-economic-thinking. Taleb, Nassim Nicholas. Fooled by Randomness: The Hidden Role of Chance in Life and the Markets (London: Penguin, 2004). Taleb, Nassim Nicholas.The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007). Tarullo, Daniel K. ‘Statement by Daniel K. Tarullo, Member, Board of Governors of the Federal Reserve System before the Committee on Banking, Housing, and Urban Affairs, US Senate’, Washington DC, 11 July 2013. http://www.federalreserve.gov/newsevents/testimony/tarullo20130711a.htm.

Despite their huge differences, the ‘post-Keynesian’ school, with its suspicion of free markets, and the ‘Austrian’ school, with its fervent belief in them, would agree on that last point, though they would disagree on what causes crises and what to do about them when they happen.6 Minsky’s view that economics should include the possibility of severe crises, not as the result of external shocks, but as events that emerge from within the system, is methodologically sound. Crises, after all, are economic phenomena. Moreover, they have proved a persistent feature of capitalist economies. As Nouriel Roubini and Stephen Mihm argue in their book Crisis Economics, crises and subsequent depressions are, in the now celebrated terminology of Nassim Nicholas Taleb, not ‘black swans’ – rare and unpredictable events – but ‘white swans’ – normal, if relatively infrequent, events that even follow a predictable pattern.7 Depressions are indeed one of the states a capitalist economy can fall into. An economic theory that does not incorporate that possibility is as relevant as a theory of biology that excludes the risk of extinctions, a theory of the body that excludes the risk of heart attacks, or a theory of bridge-building that excludes the risk of collapse.


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Bad Money: Reckless Finance, Failed Politics, and the Global Crisis of American Capitalism by Kevin Phillips

algorithmic trading, asset-backed security, bank run, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business cycle, buy and hold, collateralized debt obligation, computer age, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency peg, diversification, Doha Development Round, energy security, financial deregulation, financial innovation, fixed income, Francis Fukuyama: the end of history, George Gilder, housing crisis, Hyman Minsky, imperial preference, income inequality, index arbitrage, index fund, interest rate derivative, interest rate swap, Joseph Schumpeter, Kenneth Rogoff, large denomination, Long Term Capital Management, market bubble, Martin Wolf, Menlo Park, mobile money, money market fund, Monroe Doctrine, moral hazard, mortgage debt, Myron Scholes, new economy, oil shale / tar sands, oil shock, old-boy network, peak oil, plutocrats, Plutocrats, Ponzi scheme, profit maximization, Renaissance Technologies, reserve currency, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, Satyajit Das, shareholder value, short selling, sovereign wealth fund, The Chicago School, Thomas Malthus, too big to fail, trade route

If speculative excesses represent an albatross for the U.S. financial sector, the prospective burden of quantitative mathematics represents a black albatross. Or perhaps we should say a bevy of black swans, author Nassim Nicholas Taleb’s shorthand for mathematical impossibilities that cannot occur in hedge funds’ quantitative strategies but always manage to occur two, three, seven, or eleven times in the real world of every significant financial crisis.47 The idea that policymakers have allowed the U.S. economy to be guided by a financial sector increasingly dominated by black box makers and algorithm vendors itself seems like a black swan—an impossibility, save that it’s happening. According to one U.S. consultancy, by 2010 algorithmic trading, an aspect of “quant”based investing, is expected to account for half of all trading in U.S. equity markets.48 There is no better distillation of the harm inflicted—and probably yet to be inflicted—than that of hedge fund manager Richard Bookstaber in his 2007 volume, A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation.

Dialynas and Marshall Auerbeck, “Renegade Economics: The Bretton Woods II Fiction,” Pimco Viewpoints, September 2007. 40 “America’s Vulnerable Economy,” Economist, November 15, 2007. 41 “Dollar’s Last Lap as the Only Anchor Currency,” Financial Times, November 25, 2007. 42 John Authers, “The Short View: Weak Dollar,” Financial Times, September 10, 2007. 43 “Why Banking Is an Accident Waiting to Happen,” Financial Times, November 27, 2007. 44 Martin Wolf, “Why the Credit Squeeze Is a Turning Point for the World,” Financial Times, December 11, 2007. 45 “Mortgage Crisis Perplexes Even Shrewd Investor Warren Buffett,” San Francisco Chronicle, December 12, 2007. 46 “European Bosses Warming to Foreign Funds,” Financial Times, December 11, 2007. 47 Nassim Nicholas Taleb, The Black Swan (New York: Random House, 2007). 48 “Does Not Compute: How Misfiring Quant Funds Are Distorting the Markets,” Financial Times, December 9, 2007. 49 Richard Bookstaber, A Demon of Our Own Design (New York: John Wiley & Sons, 2007), pp. 5, 259-60. 50 Mike Muehleck, “Exit U.S.,” www.agorafinancial.com//afrude/. AFTERWORD: SPECULATIVE CAPITALISM ENDANGERED 1 “Out of Control Wall Street Chikefs Spurned Warnings at Davos,” Bloomberg News, October 24, 2008. 2 “U.S.

Treatises on the origin of scientific economics leave out mercantilism, and its preoccupation with gold, silver, and national aggrandizement of precious metals, as unscientific. But economics can be emotional as well as scientific—the psychologies of human nature, panic, and bubbling, for example. Indeed, this emotional explanation is becoming chic—witness the recent spate of books and articles on emotions trumping rationalism, the case for “black swan” (supposedly impossible) events, and the unlikely specialty of neuroeconomics.42 The early mercantilists propounded an economics based on the accumulation of precious-metal assets. These were the measure of a monarch’s or nation’s wealth. One got them from mines, from conquest, from captured ships, from colonies, and from exporting manufactures or commodities. What counted was amassing them.


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The End of Jobs: Money, Meaning and Freedom Without the 9-To-5 by Taylor Pearson

"side hustle", Airbnb, barriers to entry, Ben Horowitz, Black Swan, call centre, cloud computing, commoditize, creative destruction, David Heinemeier Hansson, Elon Musk, en.wikipedia.org, Frederick Winslow Taylor, future of work, Google Hangouts, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, loss aversion, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, market fragmentation, means of production, Oculus Rift, passive income, passive investing, Peter Thiel, remote working, Ronald Reagan: Tear down this wall, sharing economy, side project, Silicon Valley, Skype, software as a service, software is eating the world, Startup school, Steve Jobs, Steve Wozniak, Stewart Brand, telemarketer, Thomas Malthus, Uber and Lyft, uber lyft, unpaid internship, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog

Risk Lives in the Future “Artisans, say, taxi drivers, prostitutes (a very, very old profession), carpenters, plumbers, tailors, and dentists, have some volatility in their income but they are rather robust to a minor professional Black Swan, one that would bring their income to a complete halt. Their risks are visible. Not so with employees, who have no volatility, but can be surprised to see their income going to zero after a phone call from the personnel department. Employees’ risks are hidden. Thanks to variability, these artisanal careers harbor a bit of antifragility: small variations make them adapt and change continuously by learning from the environment and being, sort of, continuously under pressure to be fit.” N. N. Taleb Many of us have been fed the belief that traditional careers are safe and that they have always been safe. It’s what I believed for a long time, and if we look at historical trends, it makes sense.

The butcher will keep feeding the turkey until a few days before Thanksgiving…[The] turkey will have a revision of belief—right when its confidence in the statement that the butcher loves turkeys is maximal and ‘it is very quiet’ and soothingly predictable in the life of the turkey.” N.N. Taleb The most obvious example of a world where a single, irreversible decision dictates the future is that of a turkey before Thanksgiving. From the day a Thanksgiving turkey is born, everything about its life indicates that things are only going to get better. It’s hatched in a safe, sterile environment. It’s cared for and fed daily. Every single day, this pattern happens again. It wakes up to find plenty of food and a place to live. It is at the moment when the turkey has the most historical data to show that its life is likely to keep improving, on the 4th Wednesday of November, that it realizes—it’s not so good to be a turkey. Source: Antifragile – Nassim Taleb That is, the moment when we are most confident about our security is the moment in which we are in fact most likely to be endangered.

The Second Rule of Extremistan: DO NOT BE A TURKEY. You Were Raised in Mediocristan. You Live in Extremistan. “This is the central illusion in life: that randomness is risky, that it is a bad thing—and that eliminating randomness is done by eliminating randomness…Mediocristan has a lot of variations, not a single one of which is extreme; Extremistan has few variations, but those that take place are extreme.” N.N. Taleb Antifragile Source: 80/20 Sales and Marketing Everyone that attended high school or college is familiar with the graph above. When the teacher wrote everyone’s grades on the board, they looked like a bell curve. A few students did really well, a few did very poorly, and most people ended up somewhere in the middle. This is a model of the world that most people are raised to believe in: Mediocristan.


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Debunking Economics - Revised, Expanded and Integrated Edition: The Naked Emperor Dethroned? by Steve Keen

"Robert Solow", accounting loophole / creative accounting, banking crisis, banks create money, barriers to entry, Benoit Mandelbrot, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, business cycle, butterfly effect, capital asset pricing model, cellular automata, central bank independence, citizen journalism, clockwork universe, collective bargaining, complexity theory, correlation coefficient, creative destruction, credit crunch, David Ricardo: comparative advantage, debt deflation, diversification, double entry bookkeeping, en.wikipedia.org, Eugene Fama: efficient market hypothesis, experimental subject, Financial Instability Hypothesis, fixed income, Fractional reserve banking, full employment, Henri Poincaré, housing crisis, Hyman Minsky, income inequality, information asymmetry, invisible hand, iterative process, John von Neumann, Kickstarter, laissez-faire capitalism, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market clearing, market microstructure, means of production, minimum wage unemployment, money market fund, open economy, Pareto efficiency, Paul Samuelson, place-making, Ponzi scheme, profit maximization, quantitative easing, RAND corporation, random walk, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Coase, Schrödinger's Cat, scientific mainstream, seigniorage, six sigma, South Sea Bubble, stochastic process, The Great Moderation, The Wealth of Nations by Adam Smith, Thorstein Veblen, time value of money, total factor productivity, tulip mania, wage slave, zero-sum game

Unemployment in the USA doubled in the next year, while a 5 percent rate of inflation rapidly gave way to 2 percent deflation. The complete failure of neoclassical economics to anticipate the crisis also meant, as I expected, that economic theory and economists are under public attack as never before. Their defense has been to argue that ‘no one could have seen this coming.’ They have taken refuge in the phrase that this crisis was a ‘Black Swan,’ using Nassim Taleb’s phrase completely out of context (Taleb 2007), and ignoring the fact that I and many other non-neoclassical economists did in fact see this coming. I therefore decided that, for both positive and negative reasons, a new edition of Debunking Economics was needed. The negative reason is that there is no better time to attack a fallacious theory than after it has made a spectacularly wrong prediction. By arguing that the macroeconomy had entered a permanent ‘Great Moderation’ (the phrase Ben Bernanke popularized to describe the apparent reduction in economic volatility and falls in unemployment and inflation between 1975 and 2007), neoclassical economics couldn’t have been more wrong about the immediate economic future.

However, ‘rational expectations’ makes no sense in non-ergodic models: any predictions made from within such a model about the model’s future behavior would be wrong (let alone predictions made about the economy the model is alleged to simulate). Crucially, the errors made by agents within that model would not be ‘normally distributed’ – they would not be neatly distributed around the model’s mean as in the classic ‘Bell Curve.’ Instead the distribution would be ‘chaotic,’ with lots of what Nassim Taleb labeled ‘Black Swan events’ (Taleb 2007). It would be futile to have ‘rational expectations’ in such a model, because these would be misleading guides to the model’s future. The model’s future would be uncertain, and the best thing any agent in such a model could do would be to project forward its current trajectory, while also expecting that expectation to be wrong. What applies to a model applies in extremis to the real world, and parallels Keynes’s observations about how people in a market economy actually behave: they apply conventions, the most common of which is to extrapolate forward current conditions, even though ‘candid examination of past experience’ (Keynes 1937: 214) would show that these conditions did not persist.

Stiglitz, J. (1998) ‘The confidence game: how Washington worsened Asia’s crash,’ New Republic Online, 9 September. Stiglitz, J. (2000) ‘What I learned at the world economic crisis,’ New Republic, 17–24 April, pp. 56–60. Strange, S. (1997) Casino Capitalism, Manchester: Manchester University Press. Swan, T. W. (2002) ‘Economic growth,’ Economic Record, 78(243): 375–80. Sweezy, P. M. (1942) The Theory of Capitalist Development, New York: Oxford University Press. Taleb, N. (2007) The Black Swan: The Impact of the Highly Improbable, New York: Random House. Taslim, F. and A. Chowdhury (1995) Macroeconomic Analysis for Australian Students, Sydney: Edward Elgar. Taylor, J. B. (1993) ‘Discretion versus policy rules in practice,’ Carnegie-Rochester Conference Series on Public Policy, 39: 195–214. Taylor, J. B. (2007) ‘The explanatory power of monetary policy rules,’ Business Economics, 42(4): 8–15.


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Servant Economy: Where America's Elite Is Sending the Middle Class by Jeff Faux

back-to-the-land, Bernie Sanders, Black Swan, Bretton Woods, BRICs, British Empire, business cycle, call centre, centre right, cognitive dissonance, collateralized debt obligation, collective bargaining, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, David Brooks, David Ricardo: comparative advantage, disruptive innovation, falling living standards, financial deregulation, financial innovation, full employment, hiring and firing, Howard Zinn, Hyman Minsky, illegal immigration, indoor plumbing, informal economy, invisible hand, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kickstarter, lake wobegon effect, Long Term Capital Management, market fundamentalism, Martin Wolf, McMansion, medical malpractice, mortgage debt, Myron Scholes, Naomi Klein, new economy, oil shock, old-boy network, Paul Samuelson, plutocrats, Plutocrats, price mechanism, price stability, private military company, Ralph Nader, reserve currency, rising living standards, Robert Shiller, Robert Shiller, rolodex, Ronald Reagan, school vouchers, Silicon Valley, single-payer health, South China Sea, statistical model, Steve Jobs, Thomas L Friedman, Thorstein Veblen, too big to fail, trade route, Triangle Shirtwaist Factory, union organizing, upwardly mobile, urban renewal, War on Poverty, We are the 99%, working poor, Yogi Berra, Yom Kippur War

As examples, Tuchman presented the Renaissance popes who lost half of Christendom for the Catholic Church; the Aztec king Montezuma, who gave away his empire to the Spanish conquistador Hernando Cortez; King George III of England, who provoked the American Revolution; King Philip of Spain, who destroyed his navy in an effort to invade Britain; the World War I German general staff’s U-boat campaign against U.S. ships; Napoleon and Hitler, who each foolishly invaded Russia; the Japanese bombing of Pearl Harbor; and the three U.S. presidents who committed their nation to the Vietnam War. The future is, of course, unknowable, and prediction is always a matter of probabilities. History, like life, is marked by unexpected turns. Black swans, to use author Nassim Nicholas Taleb’s metaphor for the unforeseen, fly in undetected by our best radar. But major dislocating events that could not have been foretold are rarer than we commonly acknowledge. It’s true that plenty of forecasted disasters never occurred. An old joke has it that economists have predicted five out of the last three recessions. So judgment is required. But before you can make a judgment, you have to pay attention.

The United States will triumph, of course, and North America will remain the economic and political power center of the world. But because conflict among nation-states is a permanent condition, the century’s end will see the United States menaced by a resurgent, nationalist Mexico. Friedman does not expect that we will completely swallow his predicted scenarios. But he reminds us that the “black swan” thesis of financial contrarian Nassim Nicholas Taleb (see chapter 1 of this book) taught us to expect the improbable. And Friedman’s improbable future is built on assumptions about military technology, demographics, and the maintenance of global hegemony that are widely accepted by the U.S. governing class and are generally compatible with the beliefs of Zakaria, Slaughter, Rose, and Kotkin. As for the domestic political economy, Friedman seems less naive than the others.

Thus, aside from promoting the political soap opera that is delivered around the clock by the news media, the business and political establishment has little to say to Americans about their economic prospects other than that they should not give up hope. This is the United States of America, after all. A prominent economist, Robert Hall, succinctly summarizes the catechism: “We’re not Japan. In America, the bet is still that we will somehow find ways to get people spending and investing again.”1 “Somehow” something will come up. Some unpredictable black swan will appear to lead us back to the old-time prosperity. Some deus ex machina will descend from above the stage to rescue the middle class from the script described in chapter 11 without discomforting the rich and powerful. Perhaps we will invent another Internet, the Chinese will self-destruct, or the magical tax cut will bring back full employment. “I’ll go home,” says Scarlett O’Hara at the end of Gone with the Wind.


pages: 695 words: 194,693

Money Changes Everything: How Finance Made Civilization Possible by William N. Goetzmann

Albert Einstein, Andrei Shleifer, asset allocation, asset-backed security, banking crisis, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bretton Woods, Brownian motion, business cycle, capital asset pricing model, Cass Sunstein, collective bargaining, colonial exploitation, compound rate of return, conceptual framework, corporate governance, Credit Default Swap, David Ricardo: comparative advantage, debt deflation, delayed gratification, Detroit bankruptcy, disintermediation, diversified portfolio, double entry bookkeeping, Edmond Halley, en.wikipedia.org, equity premium, financial independence, financial innovation, financial intermediation, fixed income, frictionless, frictionless market, full employment, high net worth, income inequality, index fund, invention of the steam engine, invention of writing, invisible hand, James Watt: steam engine, joint-stock company, joint-stock limited liability company, laissez-faire capitalism, Louis Bachelier, mandelbrot fractal, market bubble, means of production, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, new economy, passive investing, Paul Lévy, Ponzi scheme, price stability, principal–agent problem, profit maximization, profit motive, quantitative trading / quantitative finance, random walk, Richard Thaler, Robert Shiller, Robert Shiller, shareholder value, short selling, South Sea Bubble, sovereign wealth fund, spice trade, stochastic process, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, time value of money, too big to fail, trade liberalization, trade route, transatlantic slave trade, tulip mania, wage slave

The story goes that a system built on mathematical formulas took finance higher and higher until their structural weaknesses were exposed and the entire edifice collapsed, leaving taxpayers around the world to clean up the mess. Provocative money manager Nassim Taleb went so far in 2008 as to propose jail time for quants who used standard risk models. “We would like society to lock up quantitative risk managers before they cause more damage.”5 Respondents to his blog used even stronger language. Of course, Taleb was promoting his book The Black Swan, which argues that the standard probability models, based as they are on Bernoulli’s original formulation, cannot account for the frequent occurrence of extreme events. A drop of 22% in a couple of days was not in Mark Rubenstein’s game plan, because standard models used for option pricing effectively assume that the logarithm of stock prices are “normal”—that is, they conform to the standard bell-curve distribution.

Other stochastic processes have such things as discontinuous jumps and unusually large shocks (which might, for example, explain the crash of 1987, when the US stock market lost 22.6% of its value in a single day). In the 1960s, Benoit Mandelbrot began to investigate whether Lévy processes described economic time series like cotton prices and stock prices. He found that the ones that generated jumps and extreme events better described financial markets. He developed a mathematics around these unusual Lévy processes that he called “fractal geometry.” He argued that unusual events—Taleb’s black swan—were in fact much more common phenomena than Brownian motion would suggest. The crash of 1987 was not a surprise to him—he took it as a vindication of his theory. One of his major contributions to the literature on finance (published in 1966) was a proof that an efficient market implies that stock prices may not follow a random walk, but that they must be unpredictable. It was a nice refinement of Regnault’s hypothesis articulated almost precisely a century prior.

The ‘financial physics’ of a 19th-century forerunner, Jules Regnault (avec Philippe Le Gall).” European Journal of the History of Economic Thought 8(3): 323–362. 3. Jovanovic, Franck. 2006. “Economic instruments and theory in the construction of Henri Lefèvre’s science of the stock market.” Pioneers of Financial Economics 1: 169–190. 4. William Sharpe, John Cox, Stephen Ross, and Mark Rubenstein. 5. This is Money. 2008. “Nassim Taleb and the Secret of the Black Swan,” Daily Mail, November 3. Available at: http://www.thisismoney.co.uk/markets/article.html?in_article_id=456175&in_page_id=3#ixzz161dvBHe7. CHAPTER 17 1. Fratianni, Michele. 2006. “Government debt, reputation and creditors’ protections: The tale of San Giorgio.” Review of Finance 10(4): 487–506. 2. Mundy, John. 1954. Liberty and Political Power in Toulouse 1050–1230. New York: Columbia University Press, p. 60.


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Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier

23andMe, Affordable Care Act / Obamacare, airport security, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, IBM and the Holocaust, index card, informal economy, intangible asset, Internet of things, invention of the printing press, Jeff Bezos, Joi Ito, lifelogging, Louis Pasteur, Mark Zuckerberg, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, paypal mafia, performance metric, Peter Thiel, post-materialism, random walk, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, speech recognition, Steve Jobs, Steven Levy, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!

. [>] Recommendations one-third of Amazon’s income—This figure has never been officially confirmed by the company but has been published in numerous analyst reports and articles in the media, including “Building with Big Data: The Data Revolution Is Changing the Landscape of Business,” The Economist, May 26, 2011 (http://www.economist.com/node/18741392/). The figure was also referenced by two former Amazon executives in interviews with Cukier. Netflix price information—Xavier Amatriain and Justin Basilico, “Netflix Recommendations: Beyond the 5 stars (Part 1),” Netflix blog, April 6, 2012. [>] “Fooled by Randomness”—Nassim Nicholas Taleb, Fooled by Randomness (Random House, 2008); for more, see Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (2nd ed., Random House, 2010). [>] Walmart and Pop-Tarts—Constance L. Hays, “What Wal-Mart Knows About Customers’ Habits,” New York Times, November 14, 2004 (http://www.nytimes.com/2004/11/14/business/yourmoney/14wal.html). [>] Examples of predictive models by FICO, Experian, and Equifax—Scott Thurm, “Next Frontier in Credit Scores: Predicting Personal Behavior,” Wall Street Journal, October 27, 2011 (http://online.wsj.com/article/SB10001424052970203687504576655182086300912.html). [>] Aviva’s predictive models—Leslie Scism and Mark Maremont, “Insurers Test Data Profiles to Identify Risky Clients,” Wall Street Journal, November 19, 2010 (http://online.wsj.com/article/SB10001424052748704648604575620750998072986.html).

A free electronic version is available (http://www2.hn.psu.edu/faculty/jmanis/adam-smith/Wealth-Nations.pdf). Solove, Daniel J. The Digital Person: Technology and Privacy in the Information Age. NYU Press, 2004. Surowiecki, James. “A Billion Prices Now.” New Yorker, May 30, 2011 (http://www.newyorker.com/talk/financial/2011/05/30/110530ta_talk_surowiecki). Taleb, Nassim Nicholas. Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Random House, 2008. ———. The Black Swan: The Impact of the Highly Improbable. 2nd ed., Random House, 2010. Thompson, Clive. “For Certain Tasks, the Cortex Still Beats the CPU.” Wired, June 25, 2007 (http://www.wired.com/techbiz/it/magazine/15-07/ff_humancomp?currentPage=all). Thurm, Scott. “Next Frontier in Credit Scores: Predicting Personal Behavior.” Wall Street Journal, October 27, 2011 (http://online.wsj.com/article/SB10001424052970203687504576655182086300912.html).

For instance, we could run correlations on individuals’ hair length and happiness and find that hair length is not especially useful in telling us much about happiness. Correlations let us analyze a phenomenon not by shedding light on its inner workings but by identifying a useful proxy for it. Of course, even strong correlations are never perfect. It is quite possible that two things may behave similarly just by coincidence. We may simply be “fooled by randomness,” to borrow a phrase from the empiricist Nassim Nicholas Taleb. With correlations, there is no certainty, only probability. But if a correlation is strong, the likelihood of a link is high. Many Amazon customers can attest to this by pointing to a bookshelf laden with the company’s recommendations. By letting us identify a really good proxy for a phenomenon, correlations help us capture the present and predict the future: if A often takes place together with B, we need to watch out for B to predict that A will happen.


pages: 249 words: 77,342

The Behavioral Investor by Daniel Crosby

affirmative action, Asian financial crisis, asset allocation, availability heuristic, backtesting, bank run, Black Swan, buy and hold, cognitive dissonance, colonial rule, compound rate of return, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Donald Trump, endowment effect, feminist movement, Flash crash, haute cuisine, hedonic treadmill, housing crisis, IKEA effect, impulse control, index fund, Isaac Newton, job automation, longitudinal study, loss aversion, market bubble, market fundamentalism, mental accounting, meta analysis, meta-analysis, Milgram experiment, moral panic, Murray Gell-Mann, Nate Silver, neurotypical, passive investing, pattern recognition, Ponzi scheme, prediction markets, random walk, Richard Feynman, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, science of happiness, Shai Danziger, short selling, South Sea Bubble, Stanford prison experiment, Stephen Hawking, Steve Jobs, stocks for the long run, Thales of Miletus, The Signal and the Noise by Nate Silver, tulip mania, Vanguard fund

Given both the psychological (we hate losses 2.5 times as much as gains) and mathematical (it takes a 100% gain to erase a 50% loss) realities of negative events, they warrant special consideration by behavioral investors. Nassim Taleb gives a wonderful example of this in his book, Fooled by Randomness. He relates the story of meeting with fellow traders and sharing with them his belief that the market would likely rise in the following week. Realizing that he had a short position on, the traders were confused. Why be short a market you think is likely to rise? To explain, Taleb showed them the following chart. Event Probability Outcome Expected Value Market Goes Up 70% 1% 0.7 Market Goes Down 30% −10% −3.0 Total 100% −2.3 Taleb believed there to be an asymmetry between the size of losses and gains that might occur.

Thus, we have a good memory for both the exceedingly commonplace (by virtue of repetition) and the exceptionally strange. Behavioral economist Robert Shiller suggested that the ubiquity of the internet made it easier for investors to bid up the prices of internet stocks to unprecedented levels during the dot.com bubble. Evidence of the usefulness of the WWW was everywhere, making it easy to create internal narratives about how the internet could be paradigm changing. Likewise, we see the effects of black swan events like the Great Recession linger in the public consciousness for years after the fact, unusual and impactful as they are. Unfortunately for us, the imperfections of the availability heuristic are hard at work as we attempt to gauge the riskiness of different ways of living and investing. The power of story The basic premise of the attention pillar is that we make probability-insensitive judgments because of our reliance on information that is vivid over information that is factually accurate.

It is only when our personal choices and pet beliefs come under scrutiny that we rally the ego to our defense. Backfire “My characterization of a loser is someone who, after making a mistake, doesn’t introspect, doesn’t exploit it, feels embarrassed and defensive rather than enriched with a new piece of information, and tries to explain why he made the mistake rather than moving on.” — Nassim Taleb “Paper or plastic?” It’s a question you’ve been asked a thousand times but one you’ve likely never thought too deeply about. But since I’m asking now, which do you think is better for the environment? Which do you choose when presented with alternatives at the grocery store? If you’re like me, you likely choose paper, assuming that you’re doing Mother Earth a favor in the process. If that’s what you think, consider these facts from the You Are Not So Smart (YANSS) podcast: Making a paper bag takes three times the amount of water as making a plastic bag.


The Ecotechnic Future: Envisioning a Post-Peak World by John Michael Greer

back-to-the-land, Black Swan, clean water, Community Supported Agriculture, David Strachan, deindustrialization, European colonialism, Extropian, failed state, feminist movement, financial innovation, Francis Fukuyama: the end of history, George Santayana, hydrogen economy, hygiene hypothesis, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), invention of agriculture, mass immigration, McMansion, oil shale / tar sands, peak oil, post-industrial society, Project for a New American Century, Ray Kurzweil, Stewart Brand, the scientific method, Thomas Kuhn: the structure of scientific revolutions, upwardly mobile, Whole Earth Catalog, Y2K

Even when the broad sweep of events can be predicted, the details usually head in unexpected directions. Many people predicted the First World War, but nobody dreamed that it would turn a penniless exile who wrote under the pen name “Lenin” into the Communist dictator of Russia and topple Nicholas II from what most people thought was the most secure throne in Europe. Surprises on the same scale are doubtless lying in wait in our own future. In his valuable book The Black Swan, Nassim Nicholas Taleb showed that most of the dominant facts of contemporary life have been shaped by such surprises. He pointed out, for example, that no one could have known that Google, which began as one Internet search engine among many, would rise to dominate much of the Internet, while other equally promising firms went under.1 He’s quite 37 38 T he E cotechnic F u t u re correct, but there’s another side to the story.

This way of approaching the history of agriculture differs sharply, of course, from the version common in alternative circles these days, which interprets the invention of agriculture as a form of “original sin” — ​sometimes quite literally; see, for example, Daniel Quinn, Ishmael, Bantam, 1992. See Colin Tudge, Neanderthals, Bandits, and Farmers: The Origins of Agriculture, Yale University Press, 1998, for a survey of recent (and less polemical) scholarship on the origins of agriculture, on which this section is based. 5. Ernest Callenbach, Ecotopia, Banyan Tress, 1975, is the classic example. Chapter Three: A Short History of the Future 1. Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable, Random House, 2007. 2. See, for example, the archives of housingpanic.blogspot.com, where nearly every element of 2008’s financial crisis was discussed at length up to three years in advance. 3. See John Kenneth Galbraith, The Great Crash 1929, Houghton Mifflin, 1954, for a discussion of the repetitive and predictable nature of speculative booms and busts. 4.

Stone, Nehemiah, “Thermal Performance of Straw Bale Wall Systems,” Ecological Building Network, (October 2003), ecobuildnetwork.org. Strachan, David, “Hay fever, hygiene, and household size,” British Medical Journal 299 (1989), pp. 1259–1260. Suskind, Ron, “Faith, certainty, and the presidency of George W. Bush,” New York Times Magazine, 17 October, 2004. Tainter, Joseph A., The Collapse of Complex Societies, Cambridge University Press, 1988. Taleb, Nassim Nicholas, The Black Swan: The Impact of the Highly Improbable, Random House, 2007. Taylor, Graeme, Evolution’s Edge: The Coming Collapse and Transformation of Our World, New Society Publishers, 2008. Telleen, Maurice, The Draft Horse Primer, Rodale Press, 1977. Thornburg, Newton, Valhalla, Little, Brown, 1980. Todd, John, and Nancy Jack, Tomorrow is our Permanent Address: The Search for an Ecological Science of Design as Embodied in the Bioshelter, HarperCollins, 1980.


pages: 292 words: 81,699

More Joel on Software by Joel Spolsky

a long time ago in a galaxy far, far away, barriers to entry, Black Swan, Build a better mousetrap, business process, call centre, Danny Hillis, David Heinemeier Hansson, failed state, Firefox, fixed income, George Gilder, Larry Wall, low cost airline, low cost carrier, Mars Rover, Network effects, Paul Graham, performance metric, place-making, price discrimination, prisoner's dilemma, Ray Oldenburg, Ruby on Rails, Sand Hill Road, Silicon Valley, slashdot, social software, Steve Ballmer, Steve Jobs, Superbowl ad, The Great Good Place, type inference, unpaid internship, wage slave, web application, Y Combinator

Even the people who claim that they have built some big multimillion dollar superduper ultra-redundant six nines system are gonna wake up one day, I don’t know when, but they will, and something completely unusual will have gone wrong in a completely unexpected way, three EMP bombs, one at each data center, and they’ll smack their heads and have fourteen days of outage. 286 More from Joel on Software Think of it this way: if your six nines system goes down mysteriously just once, and it takes you an hour to figure out the cause and fix it, well, you’ve just blown your downtime budget for the next century. Even the most notoriously reliable systems, like AT&T’s long distance service, have had long outages (six hours in 1991) that put them at a rather embarrassing three nines . . . and AT&T’s long distance service is considered “carrier grade,” the gold standard for uptime. Keeping Internet services online suffers from the problem of black swans. Nassim Taleb, who invented the term, defines it thus (www.edge. org/3rd_culture/taleb04/taleb_indexx.html): “A black swan is an outlier, an event that lies beyond the realm of normal expectations.” Almost all Internet outages are unexpected unexpecteds: extremely lowprobability outlying surprises. They’re the kind of things that happen so rarely it doesn’t even make sense to use normal statistical methods like “mean time between failure.” What’s the “mean time between catastrophic floods in New Orleans?”

What’s the “mean time between catastrophic floods in New Orleans?” Measuring the number of minutes of downtime per year does not predict the number of minutes of downtime you’ll have the next year. It reminds me of commercial aviation today: the NTSB has done such a great job of eliminating all the common causes of crashes that nowadays, each commercial crash they investigate seems to be a crazy, one-off, black-swan outlier. Somewhere between the “extremely unreliable” level of service, where it feels like stupid outages occur again and again and again, and the “extremely reliable” level of service, where you spend millions and millions of dollars getting an extra minute of uptime a year, there’s a sweet spot, where all the expected unexpecteds have been taken care of. A single hard drive failure, which is expected, doesn’t take you down.

In the meantime, our customer service folks have the authority to credit customers’ accounts if they feel like they were affected by an outage. We let the customer decide how much they want to be credited, up to a whole month, because not every customer is even going to notice the outage, let alone suffer from it. I hope this system will improve our 288 More from Joel on Software reliability to the point where the only outages we suffer are really the extremely unexpected black swans. P.S. Yes, we want to hire another system administrator so Michael doesn’t have to be the only one to wake up in the middle of the night. thirty-six SET YOUR PRIORITIES Wednesday, October 12, 2005 It was getting time to stop futzing around with FogBugz 4.0 and start working on 5.0. We just shipped a big service pack, fixing a zillion tiny little bugs that nobody would ever come across (and introducing a couple of new tiny little bugs that nobody will ever come across), and it was time to start adding some gen-yoo-ine new features.


pages: 204 words: 66,619

Think Like an Engineer: Use Systematic Thinking to Solve Everyday Challenges & Unlock the Inherent Values in Them by Mushtak Al-Atabi

3D printing, agricultural Revolution, Albert Einstein, Barry Marshall: ulcers, Black Swan, business climate, call centre, Clayton Christensen, clean water, cognitive bias, corporate social responsibility, dematerialisation, disruptive innovation, Elon Musk, follow your passion, global supply chain, happiness index / gross national happiness, invention of the wheel, iterative process, James Dyson, Kickstarter, knowledge economy, Lao Tzu, Lean Startup, On the Revolutions of the Heavenly Spheres, remote working, shareholder value, six sigma, Steve Jobs, Steven Pinker

Because such an event, like being hit with a meteor is not a common encounter, we actually do not know the probability of it happening. Such highly unlikely events are called “black swans,” a term popularised by Nassim Taleb who wrote a book carrying the same title. Black swans are a manifestation of the uncertainty in the system. The term black swan is derived from the fact that the statement “All swans are white” was thought to be validated by countless observations. It took only one sighting of a swan that is black in Australia to render this statement false. Black swans can be negative, like if a meteor hits where I am sitting now; or positive, like a gold mine is discovered underneath my house. In order for us to benefit from a positive black swan, it is necessary that we build a system and a culture that encourages tinkering, experimentation, risk taking and yes, failure.

In order for us to benefit from a positive black swan, it is necessary that we build a system and a culture that encourages tinkering, experimentation, risk taking and yes, failure. This culture should prevail in both the education and business world. The history of black swans is filled with unintended discoveries such as the microwave oven, vaccination, and Viagra. These discoveries were made possible because highly motivated people kept on pushing the limits of our knowledge while keeping an open mind to capture any unexpected opportunity or a positive black swan. 12.4 Education and Encouraging Failure The importance of failure can never be underestimated in learning, progress and development. Unfortunately, the education system is geared only to celebrate success. Failures are ostracised and students are put in an environment in which risk-taking is not rewarded and often punished. Believing that unless we tinker and take risks, we will never be able to teach others how to do so, at Taylor’s University we experimented with making failure a requirement for success by requiring each student to report a failure that they learned from while working on their projects.

Chan 78 Kohlieser, George 63 leader, 13, 29, 34, 55, 58-61, 71, 117, 120, 146, 152-153, 173 leadership, 55, 58-59, 121, 154-155, 178 Lean Entrepreneurship, 191, 194 Lean Startup, 191, 193 learner, 16, 21, 28, 151, 201 lifecycle, 3, 184-185, 187 logbook, 145 management, 5-6, 12, 57-59, 81, 104, management, 5-6, 12, 57-59, 81, 104, 170, 176, 226 170, 176, 226 160, 162, 164, 166, 168, 170, 172 Mann, Darrell 71 Manoeuvrability, 164 manufacturability, 91, 98, 100 marketability, 184 Massive Online Open Course, 220 mastery, 23, 27-29, 69, 138, 174, 198 Mauborgne, Renée 78 measurement, 43, 107, 132-133, 219 media, 13, 34, 61, 128, 165, 180, 190 medication, 37 mentor, 30 Mesopotamia, 10 methodology, 82, 147, 150, 214 Middle Brain, 24, 44, 46, 142 mindset, 2, 5, 19-20, 26-30, 36, 38, 42, 69, 173-174, 200, 217 mission, 50-52, 58, 65, 183 Mission Zero, 224-226, 228 MOOC, 16, 43, 55, 64, 173, 194, 217, 216-221, 223 multidisciplinary, 5, 104, 137, 155 myelin, 22-23, 27, 30-31, 50 myelinate, 26 myelinated, 23 myelination, 23, 31 Network Diagram, 166-167, 170 neuron, 21-23, 27-28, 30-31, 50, 53 neuroscience, 22, 25 New Brain, 24-25, 32, 44, 141 NGOs, 191-193 Norming, 154-155 Operate, 3, 5, 8-9, 11, 16, 27, 29, 34, 100, 102, 110-116, 118, 120, 122, 124, 127, 130-131, 137, 151, 155, 161, 175, 178, 182, 184, 220 optimisation, 98 optimise, 106 optimism, 49-50 optimistic, 20, 174 optimum, 115, 203 Orang Asli, 55-56 Organisation chart, 152-153, 183 outliers, 27 overdesign, 100 Panasonic, 215 Panzer, 214 paradigm, 29, 69-70, 174 passion, 2, 196, 219 PDM, 167 performance, 20, 27, 29-30, 81, 89, 94, 106, 156, 170, 173, 176, 184, 186-187, 191, 198, 200-203, 226 Performing, 15, 21, 23, 27, 42, 47, 50, 70, 78, 87, 97, 113, 133, 154-155, 168, 222 personalisation, 16, 88 Picasso, 138-139 Piketty,Thomas 224 pitchin, 194 Polaroid, 97-98 positivity, 196 pozible, 194-195 presentation, 143-144 project based, 122, 225 Project Based Learning, 2 proposal, 34, 144, 162, 164 Oei,John-Son 55-56 Old Brain, 24, 44, 140 openlearning, 190, 219 Random Entry, 74-77, 215 recyclability, 98 recyclable, 101 Reduce, 9, 15, 78, 80, 85-86, 90-91, 168, 204, 225 redundancy, 100 relationship, 42, 45-47, 50, 52, 58,122, 131, 142, 152, 161, 178, 181, 223 Relationship Management, 45, 58 reliability, 90-91, 100, 107 reliable, 7, 124 renewing, 47 reptilian, 24 requirements, 7, 17, 69-70, 83, 94-95, 97-99, 106-107, 115, 159, 169, 184, 222, 225 resilience, 217, 227 resilient, 42, 174 Return on Failure, 191, 198, 200, 202, 204, 206 revenue, 81, 180-181, 225 rewire, 196, 220 rewired, 49 rewiring, 27 Risk Management, 168 Root Cause Analysis, 210 Rumsfeld, Donald 204 SaniShop, 61 sanitation, 14, 61, 175 satisfaction, 159-161, 169-170, 196, 214, 217 scalable, 191 Segway, 187-188 Self Management, 45, 48-49, 223 Self Assessment, 47 Self Awareness, 42, 46 Shakespeare, 19 shareholder, 167-177 Sim, Jack 61-62 simulation, 98, 138, 143 Sinek, Simon 29 Social Awareness, 45, 53, 57, 63, 223 SOPs, 212 Stakeholders Management, 170 stimuli, 19-20, 24, 26, 43-44, 48-49 Storming, 54, 154-155 subsystem, 7, 37, 94-95, 97, 107 subtasks, 166 SUCCES, 138 success, 2, 20, 28-30, 34, 38, 43, 45, 53, 59, 64, 70, 118, 124, 137-138, 142, 144, 59, 64, 70, 118, 124, 137-138, 142, 144, 170, 172-173, 178, 182, 198, 200-205, 216-219, 221-223 SWOT, 47-48, 58 System Architecture, 94-96 systematic, 3-4, 9, 34-35, 69, 90, 124, 191, 209, 220, 228 systematically, 5, 44, 90, 214 systemic, 36 tactile, 19 Taleb, Nassim Nicholas 204 Tandemic, 191-192 Taylor's Racing Team, 107, 116-121, 151 teamwork, 7, 26, 45, 58, 137-138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158 Tesla, 4 Trend Recognition, 82 trimming, 77, 215 Tuckman Model, 153-154 Twitter, 88 uncertainty, 75, 204 Value, 3, 5, 9, 12, 15, 27-28, 49, 52, 69, 78-81, 90-91, 93, 124, 152, 156, 168, 78-81, 90-91, 93, 124, 152, 156, 168, 212, 214-215, 218-219, 225, 228 Verification, 106 viability, 204 viable, 9, 52, 55, 91, 101 Viagra, 205 vision, 29, 50-51, 58-60, 183, 219, 224, 228 Vodafail, 190 Vodafone, 189-190 Vujicic, Nick 68 Wagner, Tony 44 Warner, Jim 46 Warner, Jim 46 165 WD, 198-199 wellbeing, 218 WMSDs, 133 WTO, 61


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The Levelling: What’s Next After Globalization by Michael O’sullivan

"Robert Solow", 3D printing, Airbnb, algorithmic trading, bank run, banking crisis, barriers to entry, Bernie Sanders, bitcoin, Black Swan, blockchain, Boris Johnson, Branko Milanovic, Bretton Woods, British Empire, business cycle, business process, capital controls, Celtic Tiger, central bank independence, cloud computing, continuation of politics by other means, corporate governance, credit crunch, cryptocurrency, deglobalization, deindustrialization, disruptive innovation, distributed ledger, Donald Trump, eurozone crisis, financial innovation, first-past-the-post, fixed income, Geoffrey West, Santa Fe Institute, Gini coefficient, global value chain, housing crisis, income inequality, Intergovernmental Panel on Climate Change (IPCC), knowledge economy, liberal world order, Long Term Capital Management, longitudinal study, market bubble, minimum wage unemployment, new economy, Northern Rock, offshore financial centre, open economy, pattern recognition, Peace of Westphalia, performance metric, private military company, quantitative easing, race to the bottom, reserve currency, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, Scramble for Africa, secular stagnation, Silicon Valley, Sinatra Doctrine, South China Sea, South Sea Bubble, special drawing rights, supply-chain management, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, total factor productivity, trade liberalization, tulip mania, Valery Gerasimov, Washington Consensus

Other philosophers, notably Karl Popper, sought to overlay more stringent rules that would help prove or disprove the new models resulting from paradigm shifts (he introduced the idea of falsifiability or “black swan” principle, which states that the test of whether something can be called scientific depends on whether it can be proven to be false). Indeed, Kuhn’s paradigm shift is as badly abused and misused as Popper’s notion of black swans. Both phrases have become popularized and have fallen into the vocabulary of management consultants, and as a result they have lost their meaning. For example, many people today refer to a risky scenario (e.g., could the stock market fall by 5 percent?) as a black swan event, though the original meaning of the term is quite different. The idea of the levelling fits into the frame of a paradigm shift. Think of the levelling as a series of waves or reverberations of change.

One widely cited system failure was that the majority of academic economists were blinkered by a consensus that valued restrictive mathematical models of how economies and markets operate.19 In the United States, the job market for academic economists, and the fact that the Federal Reserve System is the largest employer for trained economists, helped produce a very conformist economics field. Indeed, there continue to be blistering attacks on this approach to economics from such experts as former World Bank chief economist Paul Romer, author Nassim Taleb, and former Federal Reserve governor Kevin Warsh. In their own ways they highlight the shortcomings in the socialization and groupthink of academic economists. Romer states, “As a result, if facts disconfirm the officially sanctioned theoretical vision, they are subordinated.” Taleb more provocatively holds, “Beware the semi-erudite who thinks he is an erudite. He fails to naturally detect sophistry.” And Warsh criticized the Federal Reserve: “Its models are unreliable, its policies erratic and its guidance confusing. It is also politically vulnerable.”20 A more conventional critique of economics by Oxford University’s David Vines and Samuel Wills continues to support many of the conventional economic models but criticizes their failure to, say, fully incorporate financial market variables.21 Today, the failure of such dry models may be to not take into account the kind of social and political behavior we are now seeing (e.g., the impact of social media on productivity or the impact of populism on asset prices) and the impact that behavior can have on consumption and investment patterns.

., the United Nations, the World Bank) were set up to solve at least two problems: the coordination of policy and views across countries and the development of technical expertise. One field that needs more rather than less coordination and expertise is climate policy. Climate change and the increasingly obvious damage being done to the planet is something I would have liked to feature more in this book. When people ask me to state what the biggest risk to the world is (they usually use the phrase “black swan”), I usually reply that it is climate change. The slow buildup of evidence for global warming, the risks it poses to the world, the denial, and the lack of real policy change remind me all too much of the lead-up to the global financial crisis. Consistent with this template, I do not expect drastic action by the larger industrialized countries to address damage to the earth’s atmosphere until the human cost of climate change becomes stark.


pages: 274 words: 75,846

The Filter Bubble: What the Internet Is Hiding From You by Eli Pariser

A Declaration of the Independence of Cyberspace, A Pattern Language, Amazon Web Services, augmented reality, back-to-the-land, Black Swan, borderless world, Build a better mousetrap, Cass Sunstein, citizen journalism, cloud computing, cognitive dissonance, crowdsourcing, Danny Hillis, data acquisition, disintermediation, don't be evil, Filter Bubble, Flash crash, fundamental attribution error, global village, Haight Ashbury, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Netflix Prize, new economy, PageRank, paypal mafia, Peter Thiel, recommendation engine, RFID, Robert Metcalfe, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, social graph, social software, social web, speech recognition, Startup school, statistical model, stem cell, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, the scientific method, urban planning, Whole Earth Catalog, WikiLeaks, Y Combinator

., xxi–xxii. 83 “predictably irrational”: Dan Ariely, Predictably Irrational: The Hidden Forces That Shape Our Decisions (New York: HarperCollins, 2008) 83 figuring out what makes us happy: Dan Gilbert, Stumbling on Happiness (New York: Knopf, 2006). 83 only one part of the story: Kathryn Schulz, Being Wrong: Adventures in the Margin of Error (New York: HarperCollins, 2010). 84 “Information wants to be reduced”: Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007), 64. 85 quickly converted into schemata: Doris Graber, Processing the News: How People Tame the Information Tide (New York: Longman, 1988). 85 “condensation of all features of a story”: Ibid., 161. 85 woman celebrating her birthday: Steven James Breckler, James M. Olson, and Elizabeth Corinne Wiggins, Social Psychology Alive (Belmont, CA: Thomson Wadsworth, 2006), 69. 86 added details to their memories: Graber, Processing the News, 170. 86 Princeton versus Dartmouth: A.

After all, the Ptolemaic universe, with the earth in the center and the sun and planets revolving around it, survived an awful lot of mathematical scrutiny and scientific observation. Popper posed his problem in a slightly different way: Just because you’ve only ever seen white swans doesn’t mean that all swans are white. What you have to look for is the black swan, the counterexample that proves the theory wrong. “Falsifiability,” Popper argued, was the key to the search for truth: The purpose of science, for Popper, was to advance the biggest claims for which one could not find any countervailing examples, any black swans. Underlying Popper’s view was a deep humility about scientifically induced knowledge—a sense that we’re wrong as often as we’re right, and we usually don’t know when we are. It’s this humility that many algorithmic prediction methods fail to build in. Sure, they encounter people or behaviors that don’t fit the mold from time to time, but these aberrations don’t fundamentally compromise their algorithms.

It’s been around for millions of years—indeed, it was around before humans even existed. Even for animals with rudimentary senses, nearly all of the information coming in through their senses is meaningless, but a tiny sliver is important and sometimes life-preserving. One of the primary functions of the brain is to identify that sliver and decide what to do about it. In humans, one of the first steps is to massively compress the data. As Nassim Nicholas Taleb says, “Information wants to be reduced,” and every second we reduce a lot of it—compressing most of what our eyes see and ears hear into concepts that capture the gist. Psychologists call these concepts schemata (one of them is a schema), and they’re beginning to be able to identify particular neurons or sets of neurons that correlate with each one—firing, for example, when you recognize a particular object, like a chair.


pages: 263 words: 75,455

Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray, Tobias E. Carlisle

activist fund / activist shareholder / activist investor, Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, beat the dealer, Black Swan, business cycle, butter production in bangladesh, buy and hold, capital asset pricing model, Checklist Manifesto, cognitive bias, compound rate of return, corporate governance, correlation coefficient, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, Edward Thorp, Eugene Fama: efficient market hypothesis, forensic accounting, hindsight bias, intangible asset, Louis Bachelier, p-value, passive investing, performance metric, quantitative hedge fund, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, statistical model, survivorship bias, systematic trading, The Myth of the Rational Market, time value of money, transaction costs

Eugene Fama and Kenneth French, “Luck versus Skill in the Cross Section of Mutual Fund Returns.” Journal of Finance, forthcoming. 5. J. B. Berk and R. C. Green, “Mutual Fund Flows and Performance in Rational Markets.” Journal of Political Economy 112 (2004): 1269–1295. 6. Leinweber. 7. J. D. Freeman, “Behind the Smoke and Mirrors: Gauging the Integrity of Investment Simulations,” Financial Analysts Journal 48 (6) (November–December 1992): 26–31. 8. Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007). 9. Claire I. Tsai, Joshua Klayman, and Reid Hastie, “Effects of Amount of Information on Judgment Accuracy and Confidence.” Organizational Behavior and Human Decision Processes 107 (2008): 97–105. Available at http://ssrn.com/abstract=1297347. 10. Paul Watzlawick, How Real Is Real? (Vintage, 1977). 11. Joel Greenblatt, “Adding Your Two Cents May Cost a Lot Over the Long Term.”

People would still find it tempting to day-trade and perform technical analysis of stock charts. A country of security analysts would still overreact. In short, even the best-trained investors would make the same mistakes that investors have been making forever, and for the same immutable reason—that they cannot help it. If mere awareness that our judgments are biased does little to correct the errors we make, how then can we protect against these errors? Nassim Taleb, author of Fooled by Randomness44and who calls himself a “literary essayist and mathematical trader,” argues that we should not even attempt to correct our behavioral flaws, but should instead seek to “go around” our emotions: We are faulty and there is no need to bother trying to correct our flaws. We are so defective and so mismatched to our environment that we can just work around these flaws.

As an empiricist (actually a skeptical empiricist) I despise the moralizers beyond anything on this planet: I wonder why they blindly believe in ineffectual methods. Delivering advice assumes that our cognitive apparatus rather than our emotional machinery exerts some meaningful control over our actions. We will see how modern behavioral science shows this to be completely untrue. Research seems to support Taleb's method—tricking ourselves into doing the right thing—works better than simply trying to do the right thing (or flagellating ourselves if we don't).45 Montier says, “Even once we are aware of our biases, we must recognize that knowledge does not equal behavior. The solution lies in designing and adopting an investment process that is at least partially robust to behavioral decision-making errors.”46The advantage of the quantitative method is that it starts with the idea that most of us are temperamentally unsuited to investment, and then seeks to protect against those potential errors.


pages: 350 words: 103,270

The Devil's Derivatives: The Untold Story of the Slick Traders and Hapless Regulators Who Almost Blew Up Wall Street . . . And Are Ready to Do It Again by Nicholas Dunbar

asset-backed security, bank run, banking crisis, Basel III, Black Swan, Black-Scholes formula, bonus culture, break the buck, buy and hold, capital asset pricing model, Carmen Reinhart, Cass Sunstein, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delayed gratification, diversification, Edmond Halley, facts on the ground, financial innovation, fixed income, George Akerlof, implied volatility, index fund, interest rate derivative, interest rate swap, Isaac Newton, John Meriwether, Kenneth Rogoff, Kickstarter, Long Term Capital Management, margin call, market bubble, money market fund, Myron Scholes, Nick Leeson, Northern Rock, offshore financial centre, Paul Samuelson, price mechanism, regulatory arbitrage, rent-seeking, Richard Thaler, risk tolerance, risk/return, Ronald Reagan, shareholder value, short selling, statistical model, The Chicago School, Thomas Bayes, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, yield curve, zero-sum game

On the final leg of my trip in April 1998, I went to New York, where I had brunch with Nassim Taleb, an option trader at the French bank Paribas (now part of BNP Paribas). Not yet the fiery, best-selling intellectual he subsequently became (author of 2007’s The Black Swan), Taleb had already attacked VAR in a 1997 magazine interview as “charlatanism,” but he was in no doubt about how options theory had changed the world. “Merton had the premonition,” Taleb said admiringly. “One needs arbitrageurs to make markets efficient, and option markets provide attractive opportunities for replicators. We are indeed lucky . . . the world of finance has agreed to resemble the textbook, in order to operate better.”8 Although Taleb would subsequently change his views about how well the world matched up with Merton’s textbook, the tidal wave of money churned up by derivatives in free market economics carried most people along in its wake.9 People in the regulatory community found it hard to resist this intellectual juggernaut.

Nitpickers will point out that the economics Nobel Prize was not actually established by the inventor of dynamite but was rather an add-on created by the Swedish central bank in his memory. 7. Robert C. Merton, Continuous-Time Finance (Cambridge, MA: Blackwell, 1990), chap. 14. 8. After our meeting, Taleb wrote up his views as an article, “How the Ought Became the Is,” which I published in a Black-Scholes twenty-fifth anniversary supplement for the trade magazine Futures & Options World in July 1998. 9. See Nassim Taleb and Pablo Triana, “Bystanders to This Financial Crime Were Many,” Financial Times, December 8, 2008. 10. For example, see the report Improving Counterparty Risk Management Practices published in June 1999 by an industry working group chaired by Gerald Corrigan. 11. See the remarks by Goldman’s then head of firmwide risk, Bob Litzenberger, in Nicholas Dunbar, Inventing Money: The Story of Long-Term Capital Management and the Legends Behind It (Chichester: Wiley, 2000), 203. 12.

Motivated to find the balance between collective and individual greed, with little prompting by regulators, Goldman managed to get the governance right. If markets didn’t evolve and financial innovation didn’t take place, that might be the end of the story—a happy ending provided by VAR models. But this story does not have a happy ending. VAR quickly became dangerous not so much because of technical pitfalls like “black swans” or “fat tails,” but because it was used as an incentive rather than as a restraint. Suppose that a way could be found to stop scrabbling around as a middleman and earn big money instead by making bets—but without the risk. And suppose that the VAR system—the policing mechanism keeping the firm safe—said that the bet had low VAR and didn’t require much capital. Think for a moment about the relationship between traders and those who provide them with capital.


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The Bitcoin Standard: The Decentralized Alternative to Central Banking by Saifedean Ammous

Airbnb, altcoin, bank run, banks create money, bitcoin, Black Swan, blockchain, Bretton Woods, British Empire, business cycle, capital controls, central bank independence, conceptual framework, creative destruction, cryptocurrency, currency manipulation / currency intervention, currency peg, delayed gratification, disintermediation, distributed ledger, Ethereum, ethereum blockchain, fiat currency, fixed income, floating exchange rates, Fractional reserve banking, full employment, George Gilder, global reserve currency, high net worth, invention of the telegraph, Isaac Newton, iterative process, jimmy wales, Joseph Schumpeter, market bubble, market clearing, means of production, money: store of value / unit of account / medium of exchange, moral hazard, Network effects, Paul Samuelson, peer-to-peer, Peter Thiel, price mechanism, price stability, profit motive, QR code, ransomware, reserve currency, Richard Feynman, risk tolerance, Satoshi Nakamoto, secular stagnation, smart contracts, special drawing rights, Stanford marshmallow experiment, The Nature of the Firm, the payments system, too big to fail, transaction costs, Walter Mischel, zero-sum game

Keynes, “The End of Laissez‐Faire,” in Essays in Persuasion, pp. 272–295. 18 Murray Rothbard, “A Conversation with Murray Rothbard,” Austrian Economics Newsletter, vol. 11, no. 2 (Summer 1990). 19 John Kenneth Galbraith, The Great Crash 1929 (Boston, Ma: Houghton Mifflin Harcourt, 1997), p. 133. 20 If for some reason you haven't already, you really should read Nassim Nicholas Taleb's works on this: Fooled by Randomness, The Black Swan, Antifragility, and Skin in the Game. 21 For more on this topic, see James M. Buchanan and Gordon Tullock, The Calculus of Consent: Logical Foundations of Constitutional Democracy (1962). 22 Mark Skousen, “The Perseverance of Paul Samuelson's Economics,” Journal of Economic Perspectives, vol. 11, no. 2 (1997): 137–152. 23 David Levy and Sandra Peart, “Soviet Growth and American Textbooks: An Endogenous Past,” Journal of Economic Behavior & Organization, vol. 78, issues 1–2 (April 2011): 110–125. 24 Mark Skousen, “The Perseverance of Paul Samuelson's Economics,” Journal of Economic Perspectives, vol. 11, no. 2 (1997): 137–152. 25 Paul Krugman, “Secular Stagnation, Coalmines, Bubbles, and Larry Summers,” New York Times, November 16, 2003. 26 For a formal modeling of this statement, see D.

Wall Street Journal, 2017. Sutton, Antony. Wall Street and the Bolshevik Revolution, Crown Publishing Group, 1974. Szabo, Nick. 2001. Trusted Third Parties Are Security Holes. Available on NakamotoInstitute.org Szabo, Nick. Shelling Out: The Origins of Money. (2002). Available on NakamotoInstitute.org Taleb, Nassim Nicholas. Antifragile: How to Live in a World We Don't Understand. London: Allen Lane, 2012. _____. Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Random House, 2005. _____. The Black Swan: The Impact of the Highly Improbable. Random House, 2007. Thiel, Peter. From Zero to One: Notes on Start‐ups, or How to Build the Future. Crown Business, 2014. Zweig, Stefan. The World of Yesterday: Memoirs of a European. Pushkin Press, 2009. Online Resources bitcoin.org: The original domain used by Nakamoto to announce Bitcoin, share the white paper, and distribute the code.

Library of Congress Cataloging‐in‐Publication Data is Available: ISBN 9781119473862 (Hardcover) ISBN 9781119473893 (ePDF) ISBN 9781119473916 (ePub) Cover Design: Wiley Cover Images: REI stone © Danita Delimont/Getty Images; gold bars © Grassetto/Getty Images; QR code/Courtesy of Saifedean Ammous To my wife and daughter, who give me a reason to write. And to Satoshi Nakamoto, who gave me something worth writing about. About the Author Saifedean Ammous is a Professor of Economics at the Lebanese American University and member of the Center on Capitalism and Society at Columbia University. He holds a PhD in Sustainable Development from Columbia University. Foreword by Nassim Nicholas Taleb Let us follow the logic of things from the beginning. Or, rather, from the end: modern times. We are, as I am writing these lines, witnessing a complete riot against some class of experts, in domains that are too difficult for us to understand, such as macroeconomic reality, and in which not only is the expert not an expert, but he doesn't know it. That previous Federal Reserve bosses Greenspan and Bernanke, had little grasp of empirical reality is something we only discovered too late: one can macroBS longer than microBS, which is why we need to be careful of whom to endow with centralized macro decisions.


pages: 304 words: 80,965

What They Do With Your Money: How the Financial System Fails Us, and How to Fix It by Stephen Davis, Jon Lukomnik, David Pitt-Watson

activist fund / activist shareholder / activist investor, Admiral Zheng, banking crisis, Basel III, Bernie Madoff, Black Swan, buy and hold, centralized clearinghouse, clean water, computerized trading, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crowdsourcing, David Brooks, Dissolution of the Soviet Union, diversification, diversified portfolio, en.wikipedia.org, financial innovation, financial intermediation, fixed income, Flash crash, income inequality, index fund, information asymmetry, invisible hand, Kenneth Arrow, Kickstarter, light touch regulation, London Whale, Long Term Capital Management, moral hazard, Myron Scholes, Northern Rock, passive investing, performance metric, Ponzi scheme, post-work, principal–agent problem, rent-seeking, Ronald Coase, shareholder value, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, Steve Jobs, the market place, The Wealth of Nations by Adam Smith, transaction costs, Upton Sinclair, value at risk, WikiLeaks

For example, during the passage of the Dodd-Frank Act (the keystone regulation passed in the United States to regulate banks following the 2008 crisis), it was reported that six thousand lobbyists were employed to make sure it did not cut off lucrative revenue streams to the finance industry. See http//:thenation.com/article/174113/how-wall-street-defanged-dodd-frank. 48. See Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable, 2nd ed. (Random House, 2010), 284. 6 The Queen’s Question 1. “The Queen Asks Why No One Saw the Credit Crunch Coming,” The Telegraph, November 5, 2008. 2. Sky News, December 14, 2012, www.youtube.com/watch?v=wADO zwSTJGQ. 3. See Higher Education Statistics Agency, www.hesa.ac.uk/content/view/1897/239. 4. See Economic Departments, Institutes and Research Centres in the World, Edirc.repec.org. 5.

Coase, The Firm, the Market, and the Law, 15. 33. Andrew Scott, private interview for this book, October 2014. 34. En.wikipedia.org/wiki/There_are_known_knowns. 35. Some economists, notably Frank Knight at the University of Chicago, have written extensively about the dichotomy between predictable risk and uncertainty, which cannot be calculated. But many continue to focus only on risk. 36. Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (Random House, 2010), xxxix. 37. Andrew G. Haldane, “Tails of the Unexpected,” speech given at University of Edinburgh, June 8–9, 2012, p. 20, http://www.bankofengland.co.uk/publications/Documents/speeches/2012/speech582.pdf. 38. From Haldane, “Tails of the Unexpected.” The ratios are calculated on Table 2. Haldane compares an event four standard deviations from the mean, which under the predictions of a normal curve would have a probability of 0.003, with actual outcomes of events in the natural, social, and economic world.

There are too many possibilities, and too many uncertainties.35 The second point has to do with whether the normal curve and its variants describe the distribution of real world phenomena. Even if we set aside the problem of “unknown unknowns,” evidence suggests that economic phenomena are not normally distributed, because extreme events are much more likely than the Gaussian bell curve predicts. Nassim Taleb concludes that analyses based on bell curves tell you “close to nothing” because “they ignore large deviations, cannot handle them, yet make us confident that we have tamed uncertainty.”36 Andy Haldane, director of the Bank of England, opined that although “normality has been an accepted wisdom in economics and finance for a century or more … in real world systems, nothing could be less normal than normality.”37 Part of the problem is that Gauss never intended his distributions to be used in economics.


pages: 380 words: 118,675

The Everything Store: Jeff Bezos and the Age of Amazon by Brad Stone

airport security, Amazon Mechanical Turk, Amazon Web Services, bank run, Bernie Madoff, big-box store, Black Swan, book scanning, Brewster Kahle, buy and hold, call centre, centre right, Chuck Templeton: OpenTable:, Clayton Christensen, cloud computing, collapse of Lehman Brothers, crowdsourcing, cuban missile crisis, Danny Hillis, Douglas Hofstadter, Elon Musk, facts on the ground, game design, housing crisis, invention of movable type, inventory management, James Dyson, Jeff Bezos, John Markoff, Kevin Kelly, Kodak vs Instagram, late fees, loose coupling, low skilled workers, Maui Hawaii, Menlo Park, Network effects, new economy, optical character recognition, pets.com, Ponzi scheme, quantitative hedge fund, recommendation engine, Renaissance Technologies, RFID, Rodney Brooks, search inside the book, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, Skype, statistical arbitrage, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Thomas L Friedman, Tony Hsieh, Whole Earth Catalog, why are manhole covers round?, zero-sum game

For a moment, I experienced the same sweaty surge of panic every Amazon employee over the past two decades has felt when confronted with an unanticipated question from the hyperintelligent boss. The narrative fallacy, Bezos explained, was a term coined by Nassim Nicholas Taleb in his 2007 book The Black Swan to describe how humans are biologically inclined to turn complex realities into soothing but oversimplified stories. Taleb argued that the limitations of the human brain resulted in our species’ tendency to squeeze unrelated facts and events into cause-and-effect equations and then convert them into easily understandable narratives. These stories, Taleb wrote, shield humanity from the true randomness of the world, the chaos of human experience, and, to some extent, the unnerving element of luck that plays into all successes and failures. Bezos was suggesting that Amazon’s rise might be that sort of impossibly complex story.

The production philosophy pioneered by Toyota calls for a focus on those activities that create value for the customer and the systematic eradication of everything else. Data-Driven Marketing: The 15 Metrics Everyone in Marketing Should Know, by Mark Jeffery (2010). A guide to using data to measure everything from customer satisfaction to the effectiveness of marketing. Amazon employees must support all assertions with data, and if the data has a weakness, they must point it out or their colleagues will do it for them. The Black Swan: The Impact of the Highly Improbable, by Nassim Nicholas Taleb (2007). The scholar argues that people are wired to see patterns in chaos while remaining blind to unpredictable events, with massive consequences. Experimentation and empiricism trumps the easy and obvious narrative. Notes Prologue 1 Jeff Bezos, keynote address at Tepper School of Business graduation, Carnegie Mellon University, May 18, 2008. Part I Chapter 1: The House of Quants 1 Jeff Bezos, speech at Lake Forest College, February 26, 1998. 2 Mark Leibovich, The New Imperialists (New York: Prentice Hall, 2002), 84. 3 Rebecca Johnson, “MacKenzie Bezos: Writer, Mother of Four, and High-Profile Wife,” Vogue, February 20, 2013. 4 Eerily, here is how Bezos described the third-market opportunity to Investment Dealers’ Digest on November 15, 1993: “We wanted something to differentiate our product.

There was no easy explanation for how certain products were invented, such as Amazon Web Services, its pioneering cloud business that so many other Internet companies now use to run their operations. “When a company comes up with an idea, it’s a messy process. There’s no aha moment,” Bezos said. Reducing Amazon’s history to a simple narrative, he worried, could give the impression of clarity rather than the real thing. In Taleb’s book—which, incidentally, all Amazon senior executives had to read—the author stated that the way to avoid the narrative fallacy was to favor experimentation and clinical knowledge over storytelling and memory. Perhaps a more practical solution, at least for the aspiring author, is to acknowledge its potential influence and then plunge ahead anyway. And so I begin with a disclaimer. The idea for Amazon was conceived in 1994 on the fortieth floor of a midtown New York City skyscraper.


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The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale

Affordable Care Act / Obamacare, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, Chuck Templeton: OpenTable:, cloud computing, collateralized debt obligation, computerized markets, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, Debian, don't be evil, drone strike, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, financial innovation, financial thriller, fixed income, Flash crash, full employment, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, informal economy, information asymmetry, information retrieval, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, Marc Andreessen, Mark Zuckerberg, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, quantitative easing, race to the bottom, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, search engine result page, shareholder value, Silicon Valley, Snapchat, social intelligence, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, WikiLeaks, zero-sum game

Bankers were adopting complexly structured finance that hid their risk taking from the last backdrop of restraint: the market.152 Obligations would remain on balance sheets for some purposes, and off them for others. Byzantine agreements obscured who would be left holding the bag when a “credit event,” triggering massive payments, occurred.153 Derivatives slipped through numerous regulatory nets.154 The iconoclastic investor Nassim Taleb came to prominence by calling the financial crisis a “black swan,” a freakish event both unpredicted and unpredictable.155 But as more details emerge, it becomes apparent that it was less an unpredictable outcome of an unforeseen confluence of events than it was the natural consequence of a black box fi nance system. Even conscientious buyers of what turned out to be “toxic assets” couldn’t understand their true nature. Many resulted, at least in part, from outright obfuscation: direct efforts to hide how they had been created.

Alireza Gharagozlou, “Unregulable: Why Derivatives May Never Be Regulated,” Brooklyn Journal of Corporate, Financial and Commercial Law 4 (2010): 269–295 (exploring “various methods of regulating fi nancial derivative contracts, including (a) regulation by judicially created case law, (b) regulation as gambling, (c) regulation as insurance, (d) regulation as securities, (e) regulation via a clearinghouse and (f ) oversight by a super fi nancial regulator”). 155. Nassim Nicholas Taleb, Black Swan (New York: Random House, 2007). 156. Nomi Prins, Other People’s Money: The Corporate Mugging of America (New York: New Press, 2004). Nomi Prins, It Takes a Pillage: Behind the Bailouts, Bonuses and Backroom Deals from Washington to Wall Street (Hoboken, NJ: Wiley, 2009). 157. For example, Yves Smith describes a structure that catalyzed $533 in funding for subprime mortgages for every dollar invested in it.

There’s a noble tradition among social scientists of trying to clarify how power works: who gets what, when, where, and why.1 Our common life is explored in books like The Achieving Society, The Winner-Take-All Society, The Good Society, and The Decent Society. At their best, these works also tell us why such inquiry matters.2 But efforts like these are only as good as the information available. We cannot understand, or even investigate, a subject about which nothing is known. Amateur epistemologists have many names for this problem. “Unknown unknowns,” “black swans,” and “deep secrets” are popular catchphrases for our many areas of social blankness.3 There is even an emerging field of “agnotology” that studies the “structural production of ignorance, its diverse causes and conformations, whether brought about by neglect, forgetfulness, myopia, extinction, secrecy, or suppression.” 4 2 THE BLACK BOX SOCIETY Gaps in knowledge, putative and real, have powerful implications, as do the uses that are made of them.


pages: 669 words: 210,153

Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers by Timothy Ferriss

Airbnb, Alexander Shulgin, artificial general intelligence, asset allocation, Atul Gawande, augmented reality, back-to-the-land, Ben Horowitz, Bernie Madoff, Bertrand Russell: In Praise of Idleness, Black Swan, blue-collar work, Boris Johnson, Buckminster Fuller, business process, Cal Newport, call centre, Charles Lindbergh, Checklist Manifesto, cognitive bias, cognitive dissonance, Colonization of Mars, Columbine, commoditize, correlation does not imply causation, David Brooks, David Graeber, diversification, diversified portfolio, Donald Trump, effective altruism, Elon Musk, fault tolerance, fear of failure, Firefox, follow your passion, future of work, Google X / Alphabet X, Howard Zinn, Hugh Fearnley-Whittingstall, Jeff Bezos, job satisfaction, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Kickstarter, Lao Tzu, lateral thinking, life extension, lifelogging, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mason jar, Menlo Park, Mikhail Gorbachev, MITM: man-in-the-middle, Nelson Mandela, Nicholas Carr, optical character recognition, PageRank, passive income, pattern recognition, Paul Graham, peer-to-peer, Peter H. Diamandis: Planetary Resources, Peter Singer: altruism, Peter Thiel, phenotype, PIHKAL and TIHKAL, post scarcity, post-work, premature optimization, QWERTY keyboard, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, rent-seeking, Richard Feynman, risk tolerance, Ronald Reagan, selection bias, sharing economy, side project, Silicon Valley, skunkworks, Skype, Snapchat, social graph, software as a service, software is eating the world, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, superintelligent machines, Tesla Model S, The Wisdom of Crowds, Thomas L Friedman, Wall-E, Washington Consensus, Whole Earth Catalog, Y Combinator, zero-sum game

Phillips), The Hard Thing About Hard Things (Ben Horowitz), Zero to One (Peter Thiel), The Art of the Start 2.0 (Guy Kawasaki), the works of Nassim Nicholas Taleb Neistat, Casey: It’s Not How Good You Are, It’s How Good You Want to Be (Paul Arden), The Second World War (John Keegan), The Autobiography of Malcolm X (Malcolm X and Alex Haley) Nemer, Jason: The Prophet (Kahlil Gibran), Tao Te Ching (Lao Tzu) Norton, Edward: Wind, Sand and Stars (Antoine de Saint-Exupéry), Buddhism Without Beliefs (Stephen Batchelor), Shōgun (James Clavell), The Search for Modern China; The Death of Woman Wang (Jonathan Spence), “The Catastrophe of Success” (essay by Tennessee Williams), The Black Swan (Nassim Nicholas Taleb) Novak, B.J.: The Oxford Book of Aphorisms (John Gross), Daily Rituals: How Artists Work (Mason Currey), Easy Riders, Raging Bulls: How the Sex-Drugs-and-Rock ’N’ Roll Generation Saved Hollywood (Peter Biskind), The Big Book of New American Humor; The Big Book of Jewish Humor (William Novak and Moshe Waldoks) Ohanian, Alexis: Founders at Work: Stories of Startups’ Early Days (Jessica Livingston), Masters of Doom: How Two Guys Created an Empire and Transformed Pop Culture (David Kushner) Palmer, Amanda: Dropping Ashes on the Buddha: The Teachings of Zen Master Seung Sahn; Only Don’t Know: Selected Teaching Letters of Zen Master Seung Sahn (Seung Sahn), A Short History of Nearly Everything (Bill Bryson) Paul, Caroline: The Things They Carried (Tim O’Brien), The Dog Stars (Peter Heller) Polanco, Martin: The Journey Home (Radhanath Swami), Ibogaine Explained (Peter Frank), Tryptamine Palace: 5-MeO-DMT and the Sonoran Desert Toad (James Oroc) Poliquin, Charles: The ONE Thing (Gary Keller and Jay Papasan), 59 Seconds: Change Your Life in Under a Minute (Richard Wiseman), The Checklist Manifesto (Atul Gawande), Bad Science (Ben Goldacre), Life 101: Everything We Wish We Had Learned about Life in School—But Didn’t (Peter McWilliams) Popova, Maria: Still Writing (Dani Shapiro), On the Shortness of Life (Seneca), The Republic (Plato), On the Move: A Life (Oliver Sacks), The Journal of Henry David Thoreau, 1837–1861 (Henry David Thoreau), A Rap on Race (Margaret Mead and James Baldwin), On Science, Necessity and the Love of God: Essays (Simone Weil), Stumbling on Happiness (Daniel Gilbert), Desert Solitaire: A Season in the Wilderness (Edward Abbey), Gathering Moss (Robin Wall Kimmerer), The Essential Scratch & Sniff Guide to Becoming a Wine Expert (Richard Betts) Potts, Rolf: Leaves of Grass (Walt Whitman), Writing Tools: 50 Essential Strategies for Every Writer (Roy Peter Clark), To Show and to Tell: The Craft of Literary Nonfiction (Phillip Lopate), Screenplay: The Foundations of Screenwriting (Syd Field), Story (Robert McKee), Alien vs.

You should only gamble with what you’re very comfortable losing. If the prospective financial loss drives you to even mild desperation or depression, you shouldn’t do it. You have started and/or managed successful businesses in the past. You limit angel investment funds to 10 to 15% or less of your liquid assets. I subscribe to the Nassim Taleb “barbell” school of investment, which I implement as 90% in conservative asset classes like cash-like equivalents and the remaining 10% in speculative investments that can capitalize on positive “black swans.” Even if the above criteria are met, people overestimate their risk tolerance. Even if you have only $100 to invest, this is important to explore. In 2007, I had one wealth manager ask me, “What is your risk tolerance?” and I answered honestly: “I have no idea.” It threw him off. I then asked him for the average of his clients’ responses.

He did this to train himself to only be ashamed of those things that are truly worth being ashamed of. To do anything remotely interesting, you need to train yourself to handle—or even enjoy—criticism. I regularly and deliberately “embarrass” myself for superficial reasons, much like Cato. This is an example of “fear-rehearsing” (page 474). #8—“Living well is the best revenge.”—George Herbert During a tough period several years ago, Nassim Taleb of The Black Swan fame sent me the following aphorism, which was perfect timing and perfectly put: “Robustness is when you care more about the few who like your work than the multitude who hates it (artists); fragility is when you care more about the few who hate your work than the multitude who loves it (politicians).” Choose to be robust. “I really love the user-friendly quality of the word ‘fuck.’ ” * * * Margaret Cho Margaret Cho (TW: @margaretcho, margaretcho.com) is a polymath.


pages: 504 words: 126,835

The Innovation Illusion: How So Little Is Created by So Many Working So Hard by Fredrik Erixon, Bjorn Weigel

"Robert Solow", Airbnb, Albert Einstein, American ideology, asset allocation, autonomous vehicles, barriers to entry, Basel III, Bernie Madoff, bitcoin, Black Swan, blockchain, BRICs, Burning Man, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, Clayton Christensen, Colonization of Mars, commoditize, corporate governance, corporate social responsibility, creative destruction, crony capitalism, dark matter, David Graeber, David Ricardo: comparative advantage, discounted cash flows, distributed ledger, Donald Trump, Elon Musk, Erik Brynjolfsson, fear of failure, first square of the chessboard / second half of the chessboard, Francis Fukuyama: the end of history, George Gilder, global supply chain, global value chain, Google Glasses, Google X / Alphabet X, Gordon Gekko, high net worth, hiring and firing, Hyman Minsky, income inequality, income per capita, index fund, industrial robot, Internet of things, Jeff Bezos, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, joint-stock company, Joseph Schumpeter, Just-in-time delivery, Kevin Kelly, knowledge economy, laissez-faire capitalism, Lyft, manufacturing employment, Mark Zuckerberg, market design, Martin Wolf, mass affluent, means of production, Mont Pelerin Society, Network effects, new economy, offshore financial centre, pensions crisis, Peter Thiel, Potemkin village, price mechanism, principal–agent problem, Productivity paradox, QWERTY keyboard, RAND corporation, Ray Kurzweil, rent-seeking, risk tolerance, risk/return, Robert Gordon, Ronald Coase, Ronald Reagan, savings glut, Second Machine Age, secular stagnation, Silicon Valley, Silicon Valley startup, Skype, sovereign wealth fund, Steve Ballmer, Steve Jobs, Steve Wozniak, technological singularity, telemarketer, The Chicago School, The Future of Employment, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, transaction costs, transportation-network company, tulip mania, Tyler Cowen: Great Stagnation, uber lyft, University of East Anglia, unpaid internship, Vanguard fund, Yogi Berra

The low level of economic dynamism in the Western economy has prompted some observers to blame Western societies for a lack of inventiveness or a falling appetite for new knowledge and innovation. In their view, it is technology, not the economy, that fails. The idea of the planning machine connects with old thinking rooted in Descartes’ and Francis Bacon’s scientific civilization, with an unobstructed path from science to innovation, or from technology to the economy. Nassim Nicholas Taleb, the New York University professor and author of The Black Swan, calls it the “Soviet–Harvard illusion”: the superiority, or primacy, of scientific knowledge and the religious belief in rationalism as a way to understand and change society.19 Technological shifts are embraced for their revolutionary character or their unyielding ability to crush existing social and economic orders in their march through society. There is a beeline between technological and economic change.

(i)n17 savings aggregate (i) corporate (cash hoarding) (i), (ii), (iii), (iv), (v) retirement (i), (ii), (iii), (iv), (v), (vi), (vii) Schmidt, Eric (i) Schumpeter, Joseph (i), (ii), (iii), (iv) Schumpeterian innovation (i), (ii) “scientific civilization” thinking, and planning (i) scientific research (i) see also R&D; research Scrooge character (i) “second half of the chessboard” (Ray Kurzweil) (i) Second Machine Age, The (Brynjolfsson and McAfee) (i), (ii) Second World countries, and globalization (i) Seinfeld (TV series) Art Vandelay and “importer-exporter” conversation (i), (ii) “Art Vandelay logistics operation” (i) self-driving vehicles see driverless vehicles self-regulation (i) Sellers, Peter (i) Servan-Schreiber, Jean-Jacques, Le Défi américain (The American Challenge) (i) services and globalization (i) and market contestability (i), (ii) and second unbundling of production (i) see also online services “servicification” (or “servitization”) (i), (ii) SetPoint nerve stimulator (i) shale gas, and regulation in Europe (i) shareholders (i), (ii), (iii), (iv), (v), (vi), (vii) shares buybacks (i), (ii), (iii), (iv) share/stock structures (i) see also stock markets Shelley, Percy Bysshe, Prometheus Unbound (i) shipping containers (i) short-termism (i) Sidecar (i) SIFIs (systemically important financial institutions) (i) Silicon Valley (i), (ii), (iii) silo curse (i) Silvia, John (i) Simons, Bright (i) Simphal, Thibaud (i) Sinclair, Clive (i) Sinn, Hans-Werner, “bazaar economy” (i) size see corporate size skill deficiencies, and productivity (i) Skype (i), (ii) Slyngstad, Yngve (i) smartphones (i), (ii), (iii), (iv) Smith, Adam economy of specialization (i) labor and wealth (i) “man of system” (i) The Wealth of Nations (i), (ii) Smiths, The (rock band), “hang the DJ” lyric (i) social democratic vision (i) social regulation (i), (ii) socialism and bureaucracy (i) and community-generated content (i) corporate socialism (i), (ii) and Cybersyn project (i) and death of capitalism utopia (i) and labor vs. work (i) market socialism (i) and open source technology (i) socialist planning (i) and Swedish hybrid economy (i) Söderberg, Hjalmar (i) software technology, and regulation (i) Sombart, Werner (i) Sony (i), (ii), (iii), (iv), (v) sourdough production, history of (i) South Africa, taxi services and regulation (i) South Korea “Asian Tiger” (i) R&D spending (i) Sovereign Wealth Fund Institute (i) sovereign wealth funds (SWFs) (i), (ii), (iii) “Soviet–Harvard illusion” (Nassim Nicholas Taleb) (i) space flights, commercial (i) SpaceX (i) Spain biofuels regulation (i) and diffusion of innovations (i) and globalization (i) left-wing populism (i) lesser dependence on larger enterprises (i) pensions (i) public debt (i) taxi services and regulation (i) specialization and corporate control (i) and creative destruction (i) and deregulation (i) and firm boundaries (i), (ii), (iii), (iv), (v) and globalization (i), (ii), (iii), (iv), (v), (vi), (vii) and innovation (i), (ii), (iii), (iv) and organization (i) and sunk costs (i), (ii) vertical (i), (ii) speech codes, in universities (i) staff turnover rates, and economic dynamism (i) Stanford University (i), (ii) Star Trek (TV series) (i) start-ups (i), (ii), (iii), (iv), (v) Startup Genome Report (i) statistics see recorded data (national accounts) Statoil (i) Stein, Gertrude, “there is no there there” quote (i) Stern, Ariel Dora (i) stock markets changing role of (i) and corporate politics (i) post-financial crisis growth (i) and sovereign wealth funds (i) see also shareholders; shares stockholding periods (i), (ii), (iii) strategic management (i) strategic planning (i) strategy, and managerialism (i) Stratos pacemaker (i) subprime mortgage crisis (US) (i) see also financial crisis (2007) subsidies domestic companies (i) US firms (i) sunk costs (i), (ii), (iii), (iv) Sunstein, Cass (i) supply chains fragmentation of (i), (ii), (iii), (iv), (v), (vi), (vii) German-Central European supply chain (i), (ii) globalization of (i), (ii) and market concentration (i) marketization of (i) and multinationals (i) and Nokia (i) outsourcing of (i) and private standards (i) see also value chains Sweden corporate renewal levels (i) economic situation: 1970s–1980s (i); globalization and post-financial crisis (i) productivity and incomes (i) services and globalization (i) sourdough hotel (Stockholm) (i) state telecommunication monopoly and mobile technology (i) SWFs (sovereign wealth funds) (i), (ii), (iii) SWOT analyses (i) systemically important financial institutions (SIFIs) (i) Tabarrok, Alex (i) tablets (i), (ii) Taibbi, Matt, “Why Isn’t Wall Street in Jail?” (i) Taleb, Nassim Nicholas, “Soviet–Harvard illusion” (The Black Swan) (i) tax havens (i) taxes and debt vs. equity financing (i), (ii) and labor (i) and policy uncertainty (i) in Sweden (i) taxi services and driverless cars debate (i) and regulation (i) tech entrepreneurs (i) tech incubators (i) technofeudal society (i) technological platforms, and regulation (i) technological singularity (i) technological unemployment (i), (ii) technology and capitalism (i) dystopian visions of (i) and economy (i), (ii), (iii) and employment (i) and French dirigisme (i) and innovation success (i), (ii), (iii), (iv) and “scientific civilization” thinking (i) technology angst vs. technology frustration (i) technology blitz theory (i), (ii), (iii), (iv) and vertical specialization (i) see also artificial intelligence; automation; diffusion; innovation; New Machine Age thesis; robotics/robots technostructure (i), (ii), (iii), (iv) telecommunications and deregulation (i) and globalization (i) and investment (i) see also Ericsson telephone (i), (ii) see also mobile phones/technology; smartphones Teles, Steven (i) Teller, Astro (i) 1066 and All That (Sellar and Yeatman) (i) Tesla (i) Texas, Special/Permanent School Fund (i) TFP (total factor productivity) growth (i), (ii), (iii) Thiel, Peter (i), (ii) Thomson, George (i) Tiberius (i) Time magazine, “The Committee to Save the World” (i) TNT, attempted acquisition of by UPS (i) total factor productivity (TFP) growth (i), (ii), (iii) Toynbee, Arnold (i), (ii) trade interfirm vs. intrafirm trade (i) see also global trade; mercantilism; protectionism transaction costs (i), (ii), (iii), (iv), (v), (vi) transmission costs (i), (ii), (iii) transparency Linaburg Maduell Transparency Index (LMTI) (i) and regulation (i), (ii) and sovereign wealth funds (i), (ii) Transparency International (i) “triple helix” models (i) “triple revolution” (i) trucking industry (US), shortages of drivers (i) Trump, Donald (i), (ii) Tufts Center for the Study of Drug Development (i), (ii) Tullock, Gordon (i) Twitter, and Nobel Peace Prize (i) Uber (i), (ii), (iii) unbundling of production first (i) second (i), (ii), (iii), (iv) uncertainty and compliance officers (i), (ii) and entrepreneurship (i) and financial regulation (i) and globalist worldview (i), (ii) market uncertainty (i), (ii) policy uncertainty (i), (ii) and probabilistic approach (i), (ii) and risk (i), (ii) and strategy (i) see also predictability; regulatory complexity/uncertainty; volatility unemployment and decoupling (productivity/wages) thesis (i) and Great Recession (i) and New Machine Age hype (i) and productivity (i) technological unemployment (i), (ii) see also labor unicorns (firms) (i) United Kingdom (UK) “boom and bust” and Gordon Brown (i) business investment: declining trend (i); as a proportion of GDP (i) corporate net lending (i), (ii) corporate profit margins (1948–2014) (i), (ii) dependence on larger enterprises (i) EU Leave campaign and older generation (i) exports to China (i) financialization of real economy (i) and globalization (i), (ii), (iii) income inequality and generations (i) “Independent Review of UK Economic Statistics” (Charles Bean) (i) London Stock Exchange and sovereign wealth funds (i) managerialism (i) Middle Ages economy (i) pension deficits (i) pensioners vs. working-age households incomes (i) productivity and incomes (i) productivity puzzle (i) R&D spending (i) retirement savings (i) United States (US) academia and speech codes (i) American Financial Stability Oversight Council (i) banks: and compliance officers (i); and financial regulations (i) Blue Ribbon Commission (i) Burning Man festival (Nevada) (i) capital expenditure (capex) (i)n39 car industry: driverless cars (i); and environment-related regulations (i); and lean production (i) Code of Federal Regulations (i) Consumer Protection Act (i) corporate cash hoarding (i) corporate net lending (i), (ii) corporate profit margins (1948–2014) (i), (ii) corporate renewal levels (i) corporate retained earnings figures (i) corporations’ decline (1980s) (i) debt vs. equity (i) diffusion of innovations (i) dockers and containerization (i) Dodd–Frank Act (i), (ii), (iii), (iv) Energy Policy and Conservation Act (EPCA) (i) Federal Register (i) Federal Reserve (i) financial governance (1990s) (i) financialization of real economy (i) firm entry-and-exit rates (i), (ii), (iii) Food and Drug Administration (FDA) (i), (ii), (iii) GDP figures (i), (ii) and globalization (i), (ii), (iii) high-tech sector (i) Inc 500 ranking (i) incomes: and benefits (i); inequality and generations (i); inequality and productivity (i); and productivity (i), (ii), (iii), (iv), (v) information and communications technology: hardware investment as share of GDP (i), (ii); intensity and productivity (i); sector (i), (ii) investment: business investment declining trend (i), (ii); corporate borrowing and low investment levels (i); corporate investment and shareholders (i), (ii); institutional investors (i); private investment (i) labor: ATMs and teller jobs (i); farming occupation statistics (i); job creation and destruction trends (i), (ii), (iii); labor market flexibility, low rates of (i); occupational licenses (i), (ii); staff turnover rates (i); truck drivers, shortages of (i) market concentration (1997–2012) (i), (ii) Memphis International Airport and FedEx hub (i) mergers and acquisitions (i) New York Stock Exchange (i), (ii), (iii), (iv), (v) North American Free Trade Agreement (i) Organization Man (i) pessimism and capitalist decline (i) policy uncertainty (i), (ii) productivity: downward trend (i), (ii), (iii); via foreign operations (i)n46; and ICT intensity (i); and income inequality (i); and incomes (i), (ii), (iii), (iv), (v); total factor productivity (TFP) growth (i), (ii)n11; and un/employment (i) profit margins (i) public debt (i) public pensions (i) R&D spending (i), (ii), (iii) regulation/deregulation: air cargo services deregulation (i); car industry and environment-related regulations (i); Code of Federal Regulations (i); compliance officers and Dodd–Frank rules (i); drone aircraft rules (i); green building codes (i); index of regulatory freedom (i), (ii); index of regulatory trade barriers (i), (ii); medical devices (i); taxi services (i), (ii) retirement savings (i) robots, fear of (i) Silicon Valley (i), (ii), (iii) start-ups and entrepreneurship (i), (ii) stock market crash and modern portfolio theory (i) subprime mortgage crisis (i) subsidies to firms (i) Texas Special/Permanent School Fund (i) trade: and big business (i); index of regulatory trade barriers (i), (ii) Wall Street (i), (ii), (iii), (iv) universities, and erosion of dissent (i) University of Chicago (i) University of Oxford, Future of Humanity Institute (i) UPS, attempted acquisition of TNT (i) urbanization, and diffusion of innovations (i) value vs. numbers (i) value innovation (i) value chains fragmentation of (i), (ii), (iii) and German corporations (i) globalization of (i), (ii) and market concentration (i) marketization of (i) and outsourcing of supply chains (i) “slicing up” of (i), (ii) and specialization (i), (ii) see also supply chains Van Reenen, John (i) Vanguard Group (i) Vernon, John A.

At the sixty-fourth square, the pile of rise equaled the size of Mount Everest. 10.Nietzsche, Thus Spoke Zarathustra, 41. 11.Levy, Love and Sex with Robots. 12.Holley, “Apple Co-founder on Artificial Intelligence.” 13.Romm, “Americans Are More Afraid of Robots Than Death.” 14.Smith and Anderson, “AI, Robotics, and the Future of Jobs.” 15.This section on Stafford Beer and Project Cybersyn builds on Medina, Cybernetic Revolutionaries. 16.Medina, Cybernetic Revolutionaries, 25. 17.Morozov, “The Planning Machine.” 18.Huebner, “A Possible Declining Trend for Worldwide Innovation,” 985. 19.Taleb, Antifragile. 20.Kelly, “The New Socialism.” 21.Mason, Postcapitalism. 22.The Economist, “Caught in the Net.” 23.Gilder, Microcosm. 24.Carswell, The End of Politics and the Birth of iDemocracy. 25.Fukuyama, The End of History, 98–108. 26.Kaminsky, “Iran’s Twitter Revolution.” 27.Nixon, “Lack of Innovation Leaves EU Trailing.” 28.OECD, “Territorial Review: Stockholm, Sweden 2006.” 29.Legrain, European Spring, 367. 30.Gordon, “Secular Stagnation.” 31.Gage, “The Venture Capital Secret.” 32.Marmer et al., “Startup Genome Report Extra,” 10. 33.Schumpeter’s vision of capitalism is explained in Schumpeter, The Theory of Economic Development and, in a different way, in Schumpeter, Capitalism, Socialism, and Democracy. 34.For a discerning analysis of the similarities between Marx and Schumpeter, see Elliott, “Marx and Schumpeter on Capitalism’s Creative Destruction.” 35.Schumpeter, Capitalism, Socialism, and Democracy (1992), 61. 36.To avoid repetition in the book we will use terms like contestable innovation, big innovation, radical innovation, or game-changing innovation to describe the same phenomenon: innovation that contests markets. 37.Mokyr, “Long-Term Economic Growth and the History of Technology,” 4. 38.Broadberry et al., British Economic Growth. 39.Clark, A Farewell to Alms, 1. 40.Phelps, Mass Flourishing. 41.Our version of modern capitalism and its birth draws on several scholars such as Gregory Clark, David Landes, Joel Mokyr, and Edmund Phelps.


pages: 401 words: 93,256

Alchemy: The Dark Art and Curious Science of Creating Magic in Brands, Business, and Life by Rory Sutherland

3D printing, Alfred Russel Wallace, barriers to entry, basic income, Black Swan, butterfly effect, California gold rush, call centre, Captain Sullenberger Hudson, Cass Sunstein, cognitive dissonance, Daniel Kahneman / Amos Tversky, Dava Sobel, delayed gratification, Donald Trump, double helix, Downton Abbey, Elon Musk, Firefox, George Akerlof, gig economy, Google Chrome, Google X / Alphabet X, Grace Hopper, Hyperloop, Ignaz Semmelweis: hand washing, IKEA effect, information asymmetry, James Dyson, John Harrison: Longitude, loss aversion, low cost airline, Mason jar, Murray Gell-Mann, Peter Thiel, placebo effect, race to the bottom, Richard Feynman, Richard Thaler, Rory Sutherland, shareholder value, Silicon Valley, social intelligence, Steve Jobs, supply-chain management, the map is not the territory, The Market for Lemons, The Wealth of Nations by Adam Smith, ultimatum game, universal basic income, Upton Sinclair, US Airways Flight 1549, Veblen good

For instance, ostensibly right-wing people will engage – at a local level – in behaviour that is effectively socialist. A Pall Mall club in London is typically full of rich, right-wing people, yet everyone pays equal membership fees, even though they use the club in wildly different ways. Goldman Sachs, as the author and philosopher Nassim Nicholas Taleb points out, is surprisingly socialistic internally: people distribute their gains among a partnership. However, no one there proposes a profit share with JP Morgan; in one context people are happy to share and redistribute wealth, but in another, they definitely aren’t. Why is this? In his book Skin in the Game (2018), Taleb includes what might be the most interesting quotation on an individual’s politics I have ever read. Someone* explains how, depending on context, he has entirely different political preferences: ‘At the federal level I am a Libertarian.

After all, nothing I have ever seen in Wisconsin suggested that it was a state that would never vote for Donald Trump, and it has always had a strong streak of political eccentricity. The need to rely on data can also blind you to important facts that lie outside your model. It was surely relevant that Trump was filling sports halls wherever he campaigned, while Clinton was drawing sparse crowds. It’s important to remember that big data all comes from the same place – the past. A new campaigning style, a single rogue variable or a ‘black swan’ event can throw the most perfectly calibrated model into chaos. However, the losing sides in both these campaigns have never once considered that their reliance on logic might been the cause of their defeats, and the blame was pinned on anyone from ‘Russians’ to ‘Facebook’. Maybe they were blameworthy in part, but no one has spent enough time asking whether an overreliance on mathematical models of decision-making might be to blame for the fact that in each case the clear favourite blew it.

You would be more likely to survive and reproduce if you had a strong aversion to poo, and so almost all of us are descended from people who disliked it. What’s interesting is that we adopted the behaviour many thousands of years before we knew the reasons for it. There is a good reason why evolution worked this way. Instincts are heritable, whereas reasons have to be taught; what is important is how you behave, not knowing why you do. As Nassim Nicholas Taleb remarks, ‘There is no such thing as a rational or irrational belief – there is only rational or irrational behaviour.’ And the best way for evolution to encourage or prevent a behaviour is to attach an emotion to it. Sometimes the emotion is not appropriate – for instance, there is no reason for Brits to be afraid of spiders, since there are no poisonous spiders in the UK – but it’s still there, just in case.


pages: 624 words: 127,987

The Personal MBA: A World-Class Business Education in a Single Volume by Josh Kaufman

Albert Einstein, Atul Gawande, Black Swan, business cycle, business process, buy low sell high, capital asset pricing model, Checklist Manifesto, cognitive bias, correlation does not imply causation, Credit Default Swap, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, David Ricardo: comparative advantage, Dean Kamen, delayed gratification, discounted cash flows, Donald Knuth, double entry bookkeeping, Douglas Hofstadter, en.wikipedia.org, Frederick Winslow Taylor, George Santayana, Gödel, Escher, Bach, high net worth, hindsight bias, index card, inventory management, iterative process, job satisfaction, Johann Wolfgang von Goethe, Kevin Kelly, Kickstarter, Lao Tzu, lateral thinking, loose coupling, loss aversion, Marc Andreessen, market bubble, Network effects, Parkinson's law, Paul Buchheit, Paul Graham, place-making, premature optimization, Ralph Waldo Emerson, rent control, side project, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, subscription business, telemarketer, the scientific method, time value of money, Toyota Production System, tulip mania, Upton Sinclair, Vilfredo Pareto, Walter Mischel, Y Combinator, Yogi Berra

Unexpected or random events can occur suddenly, which can have major impacts on your goals and plans. In The Black Swan, Nassim Nicholas Taleb, a former hedge fund manager, describes the perils of Uncertainty. No matter how stable or predictable things seem, unpredictable “black swan events” can change everything in an instant. The term “black swan” was a common expression in sixteenth-century London for something that was impossible or didn’t exist—everyone knew that all swans were white. The problem with the term is what eighteenth-century philosopher David Hume called the “problem of induction”: until you see every swan that exists, you can never assume the statement “all swans are white” is true. All it takes is one black swan to completely invalidate the hypothesis, which happened when black swans were documented in Australia in 1697 by Dutch sea captain Willem de Vlamingh.

All it takes is one black swan to completely invalidate the hypothesis, which happened when black swans were documented in Australia in 1697 by Dutch sea captain Willem de Vlamingh. The moment before they happen, the probability of “black swan” events occurring is essentially zero. In the wake of a black swan event, the probability of its occurring is a moot point: the event changes the Environment in which the system operates, sometimes drastically changing Selection Tests without warning. You can’t know in advance if (or which) black swan events will occur: all you can do is be flexible, prepared, and Resilient (discussed later) enough to react appropriately if and when they do. Even the most detailed analysis with reams of historical data can’t save you from Uncertainty. The primary drawback of the financial models taught in most MBA programs is Uncertainty: your pro forma, (Net Present Value NPV ), or (Capital Asset Pricing Model CAPM) model is only as good as the quality of your predictions.

Psychologically, it’s very difficult to internalize that some things are random: there’s no rhyme or reason to many of the things that happen in the world. Because of our natural Pattern Matching abilities, we tend to see patterns where none exist and tend to attribute random Changes to skill if the Changes are good or misfortune if they’re bad. As a result, we’re Fooled by Randomness—the title of Nassim Nicholas Taleb’s first book. You will never develop your business to the point that everything is perfect and unchanging. Many business owners and managers share an unexamined belief that by moving a business from “good to great,” it’ll be “built to last,” continuing to outperform competitors for decades to come. It’s a pleasant dream, but measuring yourself against that yardstick is unrealistic—it requires an unchanging world.


pages: 385 words: 101,761

Creative Intelligence: Harnessing the Power to Create, Connect, and Inspire by Bruce Nussbaum

3D printing, Airbnb, Albert Einstein, Berlin Wall, Black Swan, Chuck Templeton: OpenTable:, clean water, collapse of Lehman Brothers, creative destruction, Credit Default Swap, crony capitalism, crowdsourcing, Danny Hillis, declining real wages, demographic dividend, disruptive innovation, Elon Musk, en.wikipedia.org, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, follow your passion, game design, housing crisis, Hyman Minsky, industrial robot, invisible hand, James Dyson, Jane Jacobs, Jeff Bezos, jimmy wales, John Gruber, John Markoff, Joseph Schumpeter, Kickstarter, lone genius, longitudinal study, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Martin Wolf, new economy, Paul Graham, Peter Thiel, QR code, race to the bottom, reshoring, Richard Florida, Ronald Reagan, shareholder value, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, six sigma, Skype, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, Tesla Model S, The Chicago School, The Design of Experiments, the High Line, The Myth of the Rational Market, thinkpad, Tim Cook: Apple, too big to fail, tulip mania, We are the 99%, Y Combinator, young professional, Zipcar

Ray Ball, “The Global Financial Crisis and the Efficient Market Hypothesis: What Have We Learned?” University of Chicago, Journal of Applied Corporate Finance, vol. 21, no. 4, 2009; Siegel, “Efficient Market Theory and the Crisis”; Roger Lowenstein, “Book Review: The Myth of the Rational Market by Justin Fox,” Washington Post, June 7, 2009, accessed September 13, 2012, http://www.washingtonpost.com/wp-dyn/ content/article/2009/06/05/AR2009060502053.html. 228 “black swans”: Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007). 228 By excluding uncertainty: Frank H. Knight, Risk, Uncertainty and Profit (New York: Sentry Press, 1921). 229 In the 1960s and 1970s, as EMT: John Cassidy, “The Minsky Moment,” New Yorker, February 4, 2008, accessed September 13, 2012, http://www.newyorker.com/talk/comment/2008/ 02/04/080204taco_talk_cassidy. 229 Charles Kindleberger’s: Charles P.

Equilibrium: markets tend toward balance and equilibrium; 4. Risk: managing risk efficiently is the key to maximizing profits; 5. Measurement: only what can be measured should be included in the model. Of course, what was missing from the efficient market theory was as important as what was included: uncertainty. Because the theory constituted extreme and unpredictable occurrences of the kind economist Nassim Nicholas Taleb called “black swans” in his 2007 book of that name, the Chicago economists viewed uncertainty as an “exogenous” variable. By excluding uncertainty and focusing on measurable risk (it was Chicago economist Frank Knight who introduced the distinction between risk and uncertainty), the efficient market theory model of economics assumed and reinforced a culture of control. This would have important consequences for creativity, innovation, and economic growth over the next decades.

It involves a certain amount of domain expertise—I’m certainly a better birder now than I was when I began fifteen years ago. But even if you’ve yet to amass experience in a particular field, you can still improve your chances of spotting the surprises you may not be expecting. For birders, it can mean going to strange and sometimes unsavory places. When I was in Singapore for a design conference, I went birding at a municipal waste treatment facility and found a number of birds—including one black swan. It was a rarity in Singapore and a good find. I was surprised, but not shocked. I was, after all, looking for what was not supposed to be there. Just as good detectives are trained to hear the dog that did not bark—so too are good scientists trained to look, and listen, for what’s not there. In 2012, Jeremy Feinberg, a Rutgers doctoral candidate, was doing fieldwork in the marshes and ponds surrounding New York City when he noticed something strange.


pages: 578 words: 168,350

Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies by Geoffrey West

Alfred Russel Wallace, Anton Chekhov, Benoit Mandelbrot, Black Swan, British Empire, butterfly effect, carbon footprint, Cesare Marchetti: Marchetti’s constant, clean water, complexity theory, computer age, conceptual framework, continuous integration, corporate social responsibility, correlation does not imply causation, creative destruction, dark matter, Deng Xiaoping, double helix, Edward Glaeser, endogenous growth, Ernest Rutherford, first square of the chessboard, first square of the chessboard / second half of the chessboard, Frank Gehry, Geoffrey West, Santa Fe Institute, Guggenheim Bilbao, housing crisis, Index librorum prohibitorum, invention of agriculture, invention of the telephone, Isaac Newton, Jane Jacobs, Jeff Bezos, Johann Wolfgang von Goethe, John von Neumann, Kenneth Arrow, laissez-faire capitalism, life extension, Mahatma Gandhi, mandelbrot fractal, Marchetti’s constant, Masdar, megacity, Murano, Venice glass, Murray Gell-Mann, New Urbanism, Peter Thiel, profit motive, publish or perish, Ray Kurzweil, Richard Feynman, Richard Florida, Silicon Valley, smart cities, Stephen Hawking, Steve Jobs, Stewart Brand, technological singularity, The Coming Technological Singularity, The Death and Life of Great American Cities, the scientific method, too big to fail, transaction costs, urban planning, urban renewal, Vernor Vinge, Vilfredo Pareto, Von Neumann architecture, Whole Earth Catalog, Whole Earth Review, wikimedia commons, working poor

Like long-term weather forecasting, this is a notoriously difficult challenge, and to be fair to economists we should recognize that they are pretty good at forecasting the relatively short term, provided the system remains stable. Traditional economic theory relies heavily on the economy remaining in an approximately equilibrium state. The serious challenge is to be able to predict outlying events, major transitions, critical points, and devastating economic hurricanes and tornadoes where their record has mostly been pretty dismal. Nassim Taleb, author of the best-selling, highly influential book The Black Swan, has been particularly harsh on economists despite, or maybe because of, having been trained in business and finance.5 He has held positions at several distinguished universities including New York University and Oxford and has focused on the importance of coming to terms with outlying events and developing a deeper understanding of risk. He has been brutally outspoken in his condemnation of classical economic thinking with hyperbolic comments such as: “Years ago, I noticed one thing about economics, and that is that economists didn’t get anything right.”

Coase, The Firm, the Market, and the Law (Chicago: University of Chicago Press, 1988). 3. See, for instance, J. H. Miller and S. E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton, NJ: Princeton University Press, 2007). 4. J. D. Farmer and D. Foley, “The Economy Needs Agent-Based Modeling,” Nature 460 (2009): 685–86. 5. N. N. Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007). 6. M.I.G. Daepp, et al., “The Mortality of Companies,” Journal of the Royal Society Interface, 12:20150120. 7. E. L. Kaplan and P. Meier, “Nonparametric Estimation from Incomplete Observations,” Journal of American Statistical Association 53 (1958): 457–81; R. Elandt-Johnson and N. Johnson, Survival Models and Data Analysis (New York: John Wiley & Sons, 1999). 8.

., 179 bacteria, 1, 79 metabolic rate of, 93, 94, 96 bacterial colonies, 220–22, 290–91, 291 Bank of Korea, 406 bankruptcies, 33, 396 survivorship curves, 396–97, 398 Barber, Benjamin, 262 Bartholomew, John, 330 basal metabolic rate, 18–19, 90–93, 160 Batty, Michael, 291, 294–95 Bavinger House, 259 beam experiment, 42 Beckham, David, 63 bell curve, 56, 314 Bergman, Ingmar, 178, 179–80 Bettencourt, Luis, 274–75, 341, 356, 364 Big Bang, 16, 198, 339, 429 big data, 57, 270, 325, 338, 439–48 Big Data Institute (BDI), 442–43 Big Picture, 1–33 cities and global sustainability, 28–32 companies and businesses, 32–33 energy, metabolism, and entropy, 12–15 exponentially expanding socioeconomic urbanized world, 8–10 growth from cells to whales, 25–28 introduction, overview, and summary, 1–8 matter of life and death, 10–12 scaling and complexity, 19–25 scaling and nonlinear behavior, 15–19 big-picture theory of cities, 6, 269–71, 325–26, 338 biological metabolic rate, 13, 373 biological networks, 103–5, 104, 111–18, 153–54, 284–85 biological time, 327 biology, 7, 10, 11, 13 mathematics and, 85–86, 87 physics and, 83–84, 105–11 biomechanical constraints, 122, 158–63 biophysics, 83–84 birth control, 227, 229 Black Swan, The (Taleb), 383 blood flow, 74, 118–20, 124–26, 128–29, 155 blood pressure, 51, 89, 125–26, 162 blue whales, 1, 18, 91, 119, 158, 159–60, 234 body functions, decline by age, 195, 197, 201, 202 body mass, 18–19 life spans scale, 195–96 metabolic rate of animals, 2, 2n, 3, 13, 18–19, 25–26, 91–92, 285–86 body-mass index (BMI), 55–56, 57–59 body temperature, 51, 173–78 extending life span and, 203–4 body weight and drug dosages, 53–55 Boltzmann, Ludwig, 109 Bombay, growth curve, 375 border paradox, 135, 136–40, 138, 152 Boston, 261, 278 movement in, 348–49, 349–50, 353–54 Boulding, Kenneth, 229 bounded growth, 31, 173, 391 Bragg, Lawrence, 437 Bragg, William, 437 brain matter, 93, 94, 96, 104 brain size and social groups, 308–9 branching, 151–52, 154, 155, 157 area-preserving, 120–22, 154, 157 branching ratio, 306–7 Brand, Stewart, 211–12 Brasilia, 257–58, 267, 268 Brenner, Sydney, 111, 443 bridges, 60–62, 298–300 British Classical Association, 86 British Meteorological Office, 132 broccoli, 126–27, 127 Brown, James, 105–7, 110 Brown, Jim, 174, 386 Brownsville, Texas, 358 Brunel, Isambard Kingdom, 63–68, 65, 70–71, 86, 177 Brunel, Marc, 64 Bryson, Bill, 266 budget, U.S., 233–34 Burundi, 9 business diversity, 363–71 business ecosystem, 249–50 businesses.


pages: 161 words: 44,488

The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology by William Mougayar

Airbnb, airport security, Albert Einstein, altcoin, Amazon Web Services, bitcoin, Black Swan, blockchain, business process, centralized clearinghouse, Clayton Christensen, cloud computing, cryptocurrency, disintermediation, distributed ledger, Edward Snowden, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, fixed income, global value chain, Innovator's Dilemma, Internet of things, Kevin Kelly, Kickstarter, market clearing, Network effects, new economy, peer-to-peer, peer-to-peer lending, prediction markets, pull request, QR code, ride hailing / ride sharing, Satoshi Nakamoto, sharing economy, smart contracts, social web, software as a service, too big to fail, Turing complete, web application

The Dodd-Frank’s3 mandatory central counterparty clearing provisions were a heavy-handed policy that actually amplified systemic risk, instead of reducing it. As a result, central counterparty clearinghouses have become a new class of “too big to fail” institutions, whereas, ironically, they were previously more widely distributed. In a 2012 New York Times article titled “Stabilization Will not Save Us,” Nassim Nicholas Taleb, author of Antifragile and The Black Swan, opined: “In decentralized systems, problems can be solved early and when they are small.”4 Indeed, not only was the Web hijacked with too many central choke points, regulators supposedly continue to centralize controls in order to lower risk, whereas the opposite should be done. IT’S NOT EASY BEING DECENTRALIZED Apple’s iTunes is a typical centralized marketplace. If it were decentralized, Apple would not fancy a 30% commission on sales.

Blockchain technology will permeate our economy, creating new players, threatening others, and forcing change on incumbent organizations that want to survive. NOTES 1. The Use of Knowledge in Society, F.A. Hayek, http://www.kysq.org/docs/Hayek_45.pdf, 1945. 2. Web We Want, https://webwewant.org. 3. Dodd–Frank Wall Street Reform and Consumer Protection Act, Wikipedia, https://en.wikipedia.org/wiki/Dodd%E2%80%93Frank_Wall_Street_Reform_and_Consumer_Protection_Act. 4. “Stablization Will not Save Us,” Nassim Nicholas Taleb, New York Times, http://www.nytimes.com/2012/12/24/opinion/stabilization-wont-save-us.html?_r=0. 5. Michael Spence, Wikipedia, https://en.wikipedia.org/wiki/Michael_Spence. 6. Carlota Perez, Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages, Elgar Online, 2002. EPILOGUE BLOCKCHAIN TECHNOLOGY WAS NOT CALLED FOR. It just happened. If you reacted initially, maybe you have a head start.

Bush, President global Google governance government Grid Singularity H healthcare I identity infrastructure innovation intermediary Internet IPFS xii, Ira Magaziner IT J James Champy Javascript J. Christopher Giancarlo John Hagel Juan Llanos xiv K KYC L land registry law La’ZooZ M marketplace marriage Michael Hammer Michael Spence Microsoft microtransactions money multisignature N narrative Nassim Nicholas Taleb Nicholas G. Carr Nick Dodson Nick Szabo O Open Assets open source oracle smart oracle Otonomos over-the-counter ownership P PayPal Peer-to-Peer P2P policy makers post-trade privacy private blockchain productivity programming 10, proof in a service proof of – authority existence ownership provenance receipt stake work R R3 CEV reengineering regulation repurchase market reputation Ricardian contracts Ripple risk Robert Sams S Satoshi Nakamoto security settlement smart contract smart property society standard startups supply chain swaps SWIFT T Tierion Tim Berners-Lee token TPS transactions TransActive Grid trust U Ukraine unbundling V VISA Vitalik Buterin W wallets warehouse receipts web3 work World Wide Web ADDITIONAL RESOURCES Executive Presentations by William Mougayar, Explaining the Impact of the Blockchain and Decentralization As a trained professional consultant and analyst, William starts by understanding the context and unique requirements of each audience he addresses.


pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Ben Horowitz, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Joi Ito, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, low earth orbit, Lyft, Marc Andreessen, Mark Zuckerberg, market design, means of production, minimum viable product, natural language processing, Netflix Prize, NetJets, Network effects, new economy, Oculus Rift, offshore financial centre, PageRank, pattern recognition, Paul Graham, paypal mafia, peer-to-peer, peer-to-peer model, Peter H. Diamandis: Planetary Resources, Peter Thiel, prediction markets, profit motive, publish or perish, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, speech recognition, stealth mode startup, Stephen Hawking,