Black Swan

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pages: 651 words: 180,162

Antifragile: Things That Gain From Disorder by Nassim Nicholas Taleb

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Air France Flight 447, Andrei Shleifer, banking crisis, Benoit Mandelbrot, Berlin Wall, Black Swan, credit crunch, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, discrete time, double entry bookkeeping, Emanuel Derman, epigenetics, financial independence, Flash crash, Gary Taubes, Gini coefficient, Henri Poincaré, high net worth, 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, knowledge economy, Lao Tzu, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, meta analysis, meta-analysis, microbiome, moral hazard, mouse model, Norbert Wiener, pattern recognition, 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, Silicon Valley, six sigma, spinning jenny, statistical model, Steve Jobs, Steven Pinker, Stewart Brand, stochastic process, stochastic volatility, The Great Moderation, The Wealth of Nations by Adam Smith, Thomas Malthus, too big to fail, transaction costs, urban planning, 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: 317 words: 100,414

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

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Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, availability heuristic, Black Swan, butterfly effect, cloud computing, cuban missile crisis, Daniel Kahneman / Amos Tversky, desegregation, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, index fund, Jane Jacobs, Jeff Bezos, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, obamacare, pattern recognition, performance metric, place-making, placebo effect, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Feynman, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, 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, 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

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Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Asian financial crisis, bank run, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, butterfly effect, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, collateralized debt obligation, collective bargaining, dark matter, Edward Lorenz: Chaos theory, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial innovation, 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, new economy, Paul Lévy, 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, V2 rocket, 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: 57 words: 11,522

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

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Benoit Mandelbrot, Black Swan, 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

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Albert Einstein, asset allocation, automated trading system, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy low sell high, capital asset pricing model, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Gordon Gekko, greed is good, Haight Ashbury, index fund, invention of the telegraph, invisible hand, Isaac Newton, job automation, John Nash: game theory, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, NetJets, new economy, offshore financial centre, Paul Lévy, Ponzi scheme, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, 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: 111 words: 1

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

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Antoine Gombaud: Chevalier de Méré, availability heuristic, backtesting, Benoit Mandelbrot, Black Swan, complexity theory, corporate governance, currency peg, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, endowment effect, equity premium, global village, hindsight bias, Long Term Capital Management, loss aversion, mandelbrot fractal, mental accounting, meta analysis, meta-analysis, quantitative trading / quantitative finance, QWERTY keyboard, random walk, Richard Feynman, Richard Feynman, road to serfdom, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, Steven Pinker, stochastic process, 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

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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

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affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, 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, happiness index / gross national happiness, high batting average, hindsight bias, illegal immigration, 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, 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, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, 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.

 

pages: 576 words: 105,655

Austerity: The History of a Dangerous Idea by Mark Blyth

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accounting loophole / creative accounting, balance sheet recession, bank run, banking crisis, Black Swan, Bretton Woods, capital controls, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, collateralized debt obligation, correlation does not imply causation, 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, interest rate swap, invisible hand, Irish property bubble, Joseph Schumpeter, Kenneth Rogoff, liquidationism / Banker’s doctrine / the Treasury view, Long Term Capital Management, market bubble, market clearing, Martin Wolf, moral hazard, mortgage debt, mortgage tax deduction, Occupy movement, offshore financial centre, paradox of thrift, 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

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.

 

pages: 300 words: 77,787

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

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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, invisible hand, Kenneth Rogoff, market bubble, passive investing, pattern recognition, prediction markets, risk tolerance, risk/return, Robert Shiller, Robert Shiller, 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: 444 words: 151,136

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

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Andy Kessler, asset allocation, backtesting, bank run, banking crisis, Berlin Wall, Bernie Madoff, Black Swan, Branko Milanovic, Bretton Woods, BRICs, business climate, capital asset pricing model, 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, laissez-faire capitalism, land reform, liquidity trap, Long Term Capital Management, McMansion, moral hazard, mortgage tax deduction, naked short selling, 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, 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: 227 words: 62,177

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

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American Society of Civil Engineers: Report Card, Andrew Wiles, Bernie Madoff, Black Swan, call centre, correlation does not imply causation, cross-subsidies, Daniel Kahneman / Amos Tversky, edge city, Emanuel Derman, facts on the ground, 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.

 

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

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Albert Einstein, Bernie Madoff, Black Swan, 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, 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

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: 270 words: 79,180

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

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Affordable Care Act / Obamacare, Airbnb, Al Roth, Black Swan, buy low sell high, Credit Default Swap, cross-subsidies, crowdsourcing, disintermediation, diversified portfolio, experimental economics, George Akerlof, Goldman Sachs: Vampire Squid, income inequality, index fund, Jean Tirole, Lean Startup, Lyft, Mark Zuckerberg, market microstructure, Martin Wolf, McMansion, Menlo Park, moral hazard, multi-sided market, Network effects, patent troll, Paul Graham, Peter Thiel, pez dispenser, ride hailing / ride sharing, 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, 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: 741 words: 179,454

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

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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, capital asset pricing model, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, 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, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Fall of the Berlin Wall, financial independence, financial innovation, 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, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, job automation, Johann Wolfgang von Goethe, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, Kevin Kelly, labour market flexibility, 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, merger arbitrage, Mikhail Gorbachev, Milgram experiment, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Naomi Klein, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, pets.com, 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 Feynman, Richard Thaler, 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, 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, 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 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

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: 381 words: 101,559

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

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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, 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, Kenneth Rogoff, labour mobility, laissez-faire capitalism, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, money: store of value / unit of account / medium of exchange, Network effects, New Journalism, Nixon shock, offshore financial centre, oil shock, open economy, paradox of thrift, 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

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: 376 words: 109,092

Paper Promises by Philip Coggan

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accounting loophole / creative accounting, balance sheet recession, bank run, banking crisis, barriers to entry, Berlin Wall, Bernie Madoff, Black Swan, Bretton Woods, British Empire, 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, joint-stock company, Kenneth Rogoff, labour market flexibility, Long Term Capital Management, manufacturing employment, market bubble, market clearing, Martin Wolf, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, 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 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

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: 246 words: 74,341

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

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accounting loophole / creative accounting, bank run, banking crisis, Bernie Madoff, Black Swan, capital controls, central bank independence, collateralized debt obligation, 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, 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: 447 words: 104,258

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

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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, 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: 502 words: 107,657

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

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Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, call centre, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, 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, 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, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra

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: 478 words: 126,416

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

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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, 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, diversification, diversified portfolio, Edward Lloyd's coffeehouse, Elon Musk, Eugene Fama: efficient market hypothesis, eurozone crisis, financial innovation, financial intermediation, 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, interest rate derivative, interest rate swap, invention of the wheel, Irish property bubble, Isaac Newton, London Whale, Long Term Capital Management, loose coupling, low cost carrier, M-Pesa, market design, millennium bug, mittelstand, moral hazard, mortgage debt, new economy, Nick Leeson, Northern Rock, obamacare, Occupy movement, offshore financial centre, oil shock, passive investing, 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, Richard Feynman, 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: 537 words: 144,318

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

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Albert Einstein, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business process, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, 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, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, invisible hand, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, market microstructure, moral hazard, 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, Sharpe ratio, short selling, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, The Great Moderation, time value of money, too big to fail, transaction costs, unbiased observer, value at risk, Vanguard fund, yield curve

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: 342 words: 94,762

Wait: The Art and Science of Delay by Frank Partnoy

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algorithmic trading, Atul Gawande, Bernie Madoff, Black Swan, blood diamonds, Cass Sunstein, Checklist Manifesto, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, 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, Isaac Newton, Long Term Capital Management, Menlo Park, mental accounting, meta analysis, meta-analysis, 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, 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.

 

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

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algorithmic trading, asset allocation, automated trading system, backtesting, Black Swan, Brownian motion, business continuity plan, compound rate of return, Elliott wave, endowment effect, fixed income, general-purpose programming language, index fund, 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, 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.

 

pages: 176 words: 55,819

The Start-Up of You by Reid Hoffman

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Airbnb, Andy Kessler, Black Swan, business intelligence, Cal Newport, Clayton Christensen, David Brooks, Donald Trump, en.wikipedia.org, fear of failure, follow your passion, future of work, game design, Jeff Bezos, job automation, late fees, Mark Zuckerberg, Menlo Park, out of africa, Paul Graham, 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: 483 words: 141,836

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

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Albert Einstein, algorithmic trading, Asian financial crisis, Atul Gawande, backtesting, Basel III, Benoit Mandelbrot, Bernie Madoff, Black Swan, capital asset pricing model, central bank independence, Checklist Manifesto, corporate governance, credit crunch, Credit Default Swap, disintermediation, distributed generation, diversification, diversified portfolio, 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: store of value / unit of account / medium of exchange, moral hazard, natural language processing, open economy, 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, The Myth of the Rational Market, 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.

 

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

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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, randomized controlled trial, risk-adjusted returns, Ronald Reagan, statistical model, The Signal and the Noise by Nate Silver, 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‑­a ccident-​­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/arti cles/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 221158 i-xiv 1-210 r4ga.indd  157 2/8/16  5:58:50 PM 158 Glossary 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 221158 i-xiv 1-210 r4ga.indd  158 2/8/16  5:58:50 PM Glossary 159 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 1⁄3, 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 2⁄3 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 221158 i-xiv 1-210 r4ga.indd  159 2/8/16  5:58:50 PM 160 Glossary 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) 221158 i-xiv 1-210 r4ga.indd  160 2/8/16  5:58:50 PM No t e s Preface 1.

 

pages: 296 words: 82,501

Stuffocation by James Wallman

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3D printing, Airbnb, back-to-the-land, Berlin Wall, big-box store, Black Swan, BRICs, carbon footprint, Cass Sunstein, clean water, collaborative consumption, crowdsourcing, David Brooks, Fall of the Berlin Wall, happiness index / gross national happiness, high net worth, income inequality, James Hargreaves, Joseph Schumpeter, Martin Wolf, McMansion, means of production, Nate Silver, Occupy movement, post-industrial society, Post-materialism, 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: 240 words: 73,209

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

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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, Isaac Newton, Long Term Capital Management, Mahatma Gandhi, mandelbrot fractal, NetJets, pattern recognition, pre–internet, random walk, Ronald Reagan, South Sea Bubble, Steve Jobs, winner-take-all economy, young professional

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: 459 words: 103,153

Adapt: Why Success Always Starts With Failure by Tim Harford

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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, credit crunch, Credit Default Swap, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dava Sobel, Deep Water Horizon, Deng Xiaoping, 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, Isaac Newton, Jane Jacobs, Jarndyce and Jarndyce, Jarndyce and Jarndyce, John Harrison: Longitude, knowledge worker, loose coupling, Martin Wolf, 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

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: 293 words: 81,183

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

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barriers to entry, 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, experimental subject, follow your passion, food miles, immigration reform, income inequality, index fund, Isaac Newton, job automation, job satisfaction, labour mobility, Lean Startup, M-Pesa, 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 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.

 

pages: 416 words: 106,582

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

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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, Isaac Newton, Jaron Lanier, John von Neumann, Kevin Kelly, mandelbrot fractal, market design, Mars Rover, Marshall McLuhan, microbiome, Murray Gell-Mann, Nicholas Carr, open economy, place-making, placebo effect, pre–internet, QWERTY keyboard, random walk, randomized controlled trial, rent control, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Schrödinger's Cat, security theater, Silicon Valley, stem cell, Steve Jobs, Steven Pinker, Stewart Brand, the scientific method, Thorstein Veblen, Turing complete, Turing machine, Walter Mischel, Whole Earth Catalog

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.)

 

pages: 349 words: 134,041

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

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accounting loophole / creative accounting, Albert Einstein, Asian financial crisis, asset-backed security, Black Swan, Black-Scholes formula, Bretton Woods, BRICs, Brownian motion, business process, buy low sell high, call centre, capital asset pricing model, collateralized debt obligation, complexity theory, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, currency peg, disintermediation, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, 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, locking in a profit, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Marshall McLuhan, mass affluent, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, mutually assured destruction, 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, risk-adjusted returns, risk/return, shareholder value, short selling, South Sea Bubble, statistical model, technology bubble, the medium is the message, 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!

 

pages: 94 words: 26,453

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

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3D printing, Black Swan, British Empire, Buckminster Fuller, Clayton Christensen, crowdsourcing, deliberate practice, fear of failure, Filter Bubble, future of work, Google Glasses, Isaac Newton, James Dyson, Jaron Lanier, Jeff Bezos, job automation, Lean Startup, low skilled workers, Mark Zuckerberg, move fast and break things, Paul Erdős, Paul Graham, recommendation engine, rising living standards, Robert Shiller, Robert Shiller, Silicon Valley, Silicon Valley startup, skunkworks, 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: 265 words: 74,000

The Numerati by Stephen Baker

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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, 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

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Ada Lovelace, autonomous vehicles, Baxter: Rethink Robotics, Black Swan, call centre, capital asset pricing model, computer age, corporate governance, deskilling, en.wikipedia.org, Freestyle chess, future of work, Google Glasses, Grace Hopper, industrial robot, interchangeable parts, job automation, knowledge worker, low skilled workers, meta analysis, meta-analysis, Narrative Science, new economy, rising living standards, self-driving car, sentiment analysis, Silicon Valley, Skype, 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.

 

pages: 654 words: 191,864

Thinking, Fast and Slow by Daniel Kahneman

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Albert Einstein, Atul Gawande, availability heuristic, 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, hindsight bias, index card, job satisfaction, John von Neumann, libertarian paternalism, loss aversion, medical residency, mental accounting, meta analysis, meta-analysis, nudge unit, pattern recognition, pre–internet, price anchoring, quantitative trading / quantitative finance, random walk, Richard Thaler, risk tolerance, Ronald Reagan, The Chicago School, The Wisdom of Crowds, 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.

 

pages: 396 words: 117,149

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

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3D printing, Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, 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 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, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight

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.

 

pages: 292 words: 81,699

More Joel on Software by Joel Spolsky

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barriers to entry, Black Swan, Build a better mousetrap, business process, call centre, Danny Hillis, failed state, Firefox, George Gilder, low cost carrier, Mars Rover, Network effects, Paul Graham, performance metric, place-making, price discrimination, prisoner's dilemma, Ray Oldenburg, 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: 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

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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, Bretton Woods, 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, 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, invisible hand, Joseph Schumpeter, Kenneth Rogoff, labour market flexibility, labour mobility, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, margin call, market bubble, market clearing, market fragmentation, Martin Wolf, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, new economy, North Sea oil, Northern Rock, open economy, paradox of thrift, price stability, private sector deleveraging, purchasing power parity, pushing on a string, quantitative easing, Real Time Gross Settlement, regulatory arbitrage, reserve currency, Richard Feynman, 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

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.

 

pages: 304 words: 82,395

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

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23andMe, Affordable Care Act / Obamacare, airport security, AltaVista, 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, Internet of things, invention of the printing press, Jeff Bezos, 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, performance metric, Peter Thiel, Post-materialism, 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, 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: 1,088 words: 228,743

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

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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, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, 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, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, 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, New Journalism, oil shock, p-value, passive investing, 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, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, 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

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: 364 words: 99,613

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

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back-to-the-land, Bernie Sanders, Black Swan, Bretton Woods, BRICs, British Empire, call centre, centre right, cognitive dissonance, collateralized debt obligation, collective bargaining, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, David Brooks, David Ricardo: comparative advantage, 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, lake wobegon effect, Long Term Capital Management, market fundamentalism, Martin Wolf, McMansion, medical malpractice, mortgage debt, Naomi Klein, new economy, oil shock, 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: 355 words: 92,571

Capitalism: Money, Morals and Markets by John Plender

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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, Capital in the Twenty-First Century by Thomas Piketty, central bank independence, collapse of Lehman Brothers, collective bargaining, computer age, Corn Laws, corporate governance, 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, Gordon Gekko, greed is good, Hyman Minsky, income inequality, inflation targeting, invention of the wheel, invisible hand, Isaac Newton, James Watt: steam engine, Johann Wolfgang von Goethe, John Maynard Keynes: Economic Possibilities for our Grandchildren, joint-stock company, Joseph Schumpeter, labour market flexibility, London Interbank Offered Rate, London Whale, Long Term Capital Management, manufacturing employment, Mark Zuckerberg, market bubble, market fundamentalism, means of production, Menlo Park, moral hazard, moveable type in China, Nick Leeson, Northern Rock, Occupy movement, offshore financial centre, paradox of thrift, 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, We are the 99%, Wolfgang Streeck

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: 484 words: 136,735

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

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bank run, banking crisis, Benoit Mandelbrot, Berlin Wall, Black Swan, bonus culture, Bretton Woods, BRICs, Carmen Reinhart, cognitive dissonance, collapse of Lehman Brothers, Corn Laws, correlation does not imply causation, 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, invisible hand, Isaac Newton, Joseph Schumpeter, Kenneth Rogoff, laissez-faire capitalism, Long Term Capital Management, mandelbrot fractal, market design, market fundamentalism, Martin Wolf, moral hazard, mortgage debt, new economy, Northern Rock, offshore financial centre, oil shock, paradox of thrift, 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 Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, Washington Consensus

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.”

 

pages: 823 words: 220,581

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

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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, butterfly effect, capital asset pricing model, cellular automata, central bank independence, citizen journalism, clockwork universe, collective bargaining, complexity theory, correlation coefficient, 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, Fractional reserve banking, full employment, Henri Poincaré, housing crisis, Hyman Minsky, income inequality, invisible hand, iterative process, John von Neumann, 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, open economy, 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

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.

 

pages: 168 words: 50,647

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

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Airbnb, barriers to entry, Black Swan, call centre, cloud computing, 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, 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, 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.

 

pages: 274 words: 75,846

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

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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, Netflix Prize, new economy, PageRank, paypal mafia, Peter Thiel, recommendation engine, RFID, 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: 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

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Admiral Zheng, banking crisis, Basel III, Bernie Madoff, Black Swan, centralized clearinghouse, clean water, 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, Flash crash, income inequality, index fund, invisible hand, London Whale, Long Term Capital Management, moral hazard, Northern Rock, passive investing, performance metric, Ponzi scheme, 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: 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

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asset-backed security, bank run, banking crisis, Basel III, Black Swan, Black-Scholes formula, bonus culture, capital asset pricing model, Carmen Reinhart, Cass Sunstein, collateralized debt obligation, 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, Kenneth Rogoff, Long Term Capital Management, margin call, market bubble, Nick Leeson, Northern Rock, offshore financial centre, price mechanism, regulatory arbitrage, rent-seeking, Richard Thaler, risk tolerance, risk/return, Ronald Reagan, shareholder value, short selling, statistical model, The Chicago School, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, yield curve

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.

 

pages: 410 words: 114,005

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

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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, credit crunch, 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, Joseph Schumpeter, Lean Startup, meta analysis, meta-analysis, minimum viable product, quantitative easing, randomized controlled trial, 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, 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: 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

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Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, Black Swan, 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, Eugene Fama: efficient market hypothesis, forensic accounting, hindsight bias, 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, 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: 422 words: 113,830

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

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algorithmic trading, asset-backed security, bank run, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, collateralized debt obligation, computer age, 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, Monroe Doctrine, moral hazard, mortgage debt, new economy, oil shale / tar sands, oil shock, peak oil, Plutocrats, plutocrats, Ponzi scheme, profit maximization, Renaissance Technologies, reserve currency, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, 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.

 

pages: 695 words: 194,693

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

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Albert Einstein, Andrei Shleifer, asset allocation, asset-backed security, banking crisis, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bretton Woods, Brownian motion, 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: store of value / unit of account / medium of exchange, moral hazard, 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, 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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Creative Intelligence: Harnessing the Power to Create, Connect, and Inspire by Bruce Nussbaum

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3D printing, Airbnb, Albert Einstein, Berlin Wall, Black Swan, clean water, collapse of Lehman Brothers, Credit Default Swap, crony capitalism, crowdsourcing, Danny Hillis, declining real wages, demographic dividend, 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, Joseph Schumpeter, Kickstarter, lone genius, manufacturing employment, Mark Zuckerberg, Martin Wolf, new economy, Paul Graham, Peter Thiel, 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: 726 words: 172,988

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

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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, 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, George Akerlof, Growth in a Time of Debt, income inequality, invisible hand, Jean Tirole, joint-stock company, joint-stock limited liability company, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, margin call, Martin Wolf, moral hazard, mortgage debt, mortgage tax deduction, Nick Leeson, Northern Rock, open economy, peer-to-peer lending, regulatory arbitrage, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, 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.

 

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

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Airbnb, artificial general intelligence, asset allocation, Atul Gawande, augmented reality, back-to-the-land, Bernie Madoff, Bertrand Russell: In Praise of Idleness, Black Swan, blue-collar work, Buckminster Fuller, business process, Cal Newport, call centre, Checklist Manifesto, cognitive bias, cognitive dissonance, Colonization of Mars, Columbine, 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, Kevin Kelly, Kickstarter, Lao Tzu, life extension, Mahatma Gandhi, Mark Zuckerberg, Mason jar, Menlo Park, Mikhail Gorbachev, Nicholas Carr, optical character recognition, PageRank, passive income, pattern recognition, Paul Graham, Peter H. Diamandis: Planetary Resources, Peter Singer: altruism, Peter Thiel, phenotype, post scarcity, premature optimization, QWERTY keyboard, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, rent-seeking, Richard Feynman, Richard Feynman, risk tolerance, Ronald Reagan, 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

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: 624 words: 127,987

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

Amazon: amazon.comamazon.co.ukamazon.deamazon.fr

Albert Einstein, Atul Gawande, Black Swan, 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 Ricardo: comparative advantage, Dean Kamen, delayed gratification, discounted cash flows, double entry bookkeeping, Douglas Hofstadter, en.wikipedia.org, Frederick Winslow Taylor, Gödel, Escher, Bach, high net worth, hindsight bias, index card, inventory management, iterative process, job satisfaction, Johann Wolfgang von Goethe, Kevin Kelly, Lao Tzu, loose coupling, loss aversion, 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, 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: 320 words: 87,853

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

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Affordable Care Act / Obamacare, algorithmic trading, Amazon Mechanical Turk, asset-backed security, Atul Gawande, bank run, barriers to entry, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, cloud computing, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, Debian, don't be evil, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, financial innovation, Flash crash, full employment, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, informal economy, 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, Mark Zuckerberg, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, precariat, profit maximization, profit motive, quantitative easing, race to the bottom, recommendation engine, regulatory arbitrage, risk-adjusted returns, search engine result page, shareholder value, Silicon Valley, Snapchat, 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

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: 380 words: 118,675

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

Amazon: amazon.comamazon.co.ukamazon.deamazon.fr

3D printing, airport security, AltaVista, Amazon Mechanical Turk, Amazon Web Services, bank run, Bernie Madoff, big-box store, Black Swan, book scanning, Brewster Kahle, call centre, centre right, 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, 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?

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.

 

pages: 161 words: 44,488

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

Amazon: amazon.comamazon.co.ukamazon.deamazon.fr

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 blockchain, fault tolerance, fiat currency, global value chain, Innovator's Dilemma, Internet of things, Kevin Kelly, Kickstarter, market clearing, Network effects, new economy, peer-to-peer lending, prediction markets, pull request, 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

Amazon: amazon.comamazon.co.ukamazon.deamazon.fr

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, ethereum blockchain, Galaxy Zoo, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, Hyperloop, industrial robot, Innovator's Dilemma, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loose coupling, loss aversion, Lyft, Mark Zuckerberg, market design, means of production, minimum viable product, natural language processing, Netflix Prize, Network effects, new economy, Oculus Rift, offshore financial centre, p-value, PageRank, pattern recognition, Paul Graham, 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, Steve Jobs, subscription business, supply-chain management, TaskRabbit, telepresence, telepresence robot, Tony Hsieh, transaction costs, Tyler Cowen: Great Stagnation, urban planning, WikiLeaks, winner-take-all economy, X Prize, Y Combinator

The Penguin Press HC. Sinek, S. (2009). Start with Why: How Great Leaders Inspire Everyone to Take Action. Portfolio Hardcover. Solis, B. (2013). What’s the Future of Business: Changing the Way Businesses Create Experiences. Wiley. Spear, S. J. (2010). The High-Velocity Edge: How Market Leaders Leverage Operational Excellence to Beat the Competition. Mcgraw-Hill. Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House. Taleb, N. N. (2012). Antifragile: Things That Gain from Disorder. Random House. Thiel, P. & Masters, B. (2014). Zero to One: Notes on Startups or How to Build the Future. Crown Business. Tracy, B. (2010). How the Best Leaders Lead: Proven Secrets to Getting the Most Out of Yourself and Others. AMACOM. Wadhwa, V., & Chideya, F. (2014). Innovating Women: The Changing Face of Technology.

As these and hundreds more examples suggest, we are moving toward a world in which everything will be measured and anything can be knowable, both in the world around us and within our bodies. Only enterprises that plan for this new reality will have a chance at long-term success. Now that we have finished describing the characteristics of ExOs and their implications, we can look at the how an ExO maps onto other constructs. The following table compares ExO Attributes with Joi Ito’s MIT Media Lab Principles and the heuristics in Nassim Taleb’s Anti-Fragile theory. Joi Ito (MIT Medialab) Nassim Taleb (Anti-Fragile Theory) MTP Pull over push Compasses over maps Focus on the long term, not just the financials and short term Staff on Demand Resilience over strength Stay small and flexible Community & Crowd Systems (ecosystems) over objects Resilience over strength Build in options Stay small and flexible Algorithms - Build in stressors > Simplify and Automate Heuristics (skin in the game, orthogonal) Leased Assets Resilience over strength Reduce dependency and IT; stay small and flexible Invest in R&D Invest in data and social infrastructure Engagement (IC, gamify) Pull over push Build in options Heuristics: skin in the game Interfaces - Simplify and Automate Overcome cognitive biases Dashboard Learning over financial Simplify and Automate Short feedback loops Rewards only after project completion Experimentation Practice over theory Risk over safety Learning over education Diversify Build in hacking and stressors by yourself (fail fast and often; Netflix case w/ Chaos Monkey), especially in good times Build in options Risk over safety (not risk insensitivity) Avoid too much focus on efficiency, control and optimization Autonomy Emergence over authority Disobedience over compliance Decentralization Do not overregulate Challenge senior management Compartmentalize Share ownership within ExO on the edges (skin in the game) Social Technologies Emergence (peer-to-peer learning) over authority Build in stressors How Exponential is Your Organization?

Inevitably, even more time and money is spent adapting the product to fit the customer, a process that once again takes too long as the market moves on. In the end, of course, the product fails. In sum, NPD has become a process in which thinking and doing are separated for a long time period and where data-driven and behavioral customer feedback is delivered too late in the development process. As Nassim Taleb explains, “Knowledge gives you a little bit of an edge, but tinkering (trial and error) is the equivalent of 1,000 IQ points. It is tinkering that allowed the Industrial Revolution.” By comparison, consider the same scenario using the Lean Startup method: The company first researches the needs of the customer, then conducts an experiment to see if a proposed product matches those needs. By relying on quantitative and qualitative data, a company forms a conclusion based on a series of well-considered questions: Does a product fit the need of the customer?

 

pages: 306 words: 85,836

When to Rob a Bank: ...And 131 More Warped Suggestions and Well-Intended Rants by Steven D. Levitt, Stephen J. Dubner

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Affordable Care Act / Obamacare, airport security, augmented reality, barriers to entry, Bernie Madoff, Black Swan, Broken windows theory, Captain Sullenberger Hudson, Daniel Kahneman / Amos Tversky, deliberate practice, feminist movement, food miles, George Akerlof, invisible hand, loss aversion, mental accounting, Netflix Prize, obamacare, oil shale / tar sands, peak oil, pre–internet, price anchoring, price discrimination, principal–agent problem, profit maximization, Richard Thaler, security theater, Ted Kaczynski, the built environment, The Chicago School, the High Line, Thorstein Veblen, transaction costs

The participants are: Arthur Brooks, who teaches business and government at Syracuse and is the author of Who Really Cares: The Surprising Truth About Compassionate Conservatism; Tyler Cowen, an economist at George Mason who writes books and maintains the Marginal Revolution blog; Mark Cuban, the multifaceted entrepreneur and Dallas Mavericks owner; Barbara Ehrenreich, author of the low-rent classic Nickel and Dimed and many other works; and Nassim Nicholas Taleb, the noted flâneur and author of The Black Swan and Fooled by Randomness. Here is the question we put to each of them: You are walking down the street in New York City with ten dollars of disposable income in your pocket. You come to a corner with a hot-dog vendor on one side and a beggar on the other. The beggar looks like he’s been drinking; the hot-dog vendor looks like an upstanding citizen. How, if at all, do you distribute the ten dollars in your pocket, and why?

A given person might fear a terrorist attack and mad cow disease more than anything in the world, whereas in fact she’d be better off fearing a heart attack (and therefore taking care of herself) or salmonella (and therefore washing her cutting board thoroughly). Why do we fear the unknown more than the known? That’s a larger question than I can answer here (not that I’m capable anyway), but it probably has to do with the heuristics—the shortcut guesses—our brains use to solve problems, and the fact that these heuristics rely on the information already stored in our memories. And what gets stored away? Anomalies—the big, rare, “black swan” events that are so dramatic, so unpredictable, and perhaps world-changing, that they imprint themselves on our memories and con us into thinking of them as typical, or at least likely, whereas in fact they are extraordinarily rare. Which brings us back to Bruce Pardo and Atif Irfan. The people who didn’t seem to fear Pardo were friends and relatives. The people who did fear Irfan were strangers.

And before anyone virtuously offers him a hot dog, they should reflect on the possibility that the beggar is a vegetarian or only eats kosher or halal meat. So if the beggar approaches me and puts out his hand, and if I only have a ten-dollar bill, I have to give it to him. It’s none of my business whether he plans to spend it on infant formula for his starving baby or a pint of Thunderbird. NASSIM NICHOLAS TALEB This question is invalid and answers to it would not provide useful information. Let me explain: When I recently had drinks and cheese with Stephen Dubner (I ate 100 percent of the cheese), he asked me why economics bothers me so much as a discipline, to the point of causing allergic reactions when I encounter some academic economists. Indeed, my allergy can be physical: recently, on a British Airways flight between London and Zurich, I found myself seated across the aisle from an Ivy League international economist dressed in a blue blazer and reading the Financial Times.

 

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals by David Aronson

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Albert Einstein, Andrew Wiles, asset allocation, availability heuristic, backtesting, Black Swan, capital asset pricing model, cognitive dissonance, compound rate of return, Daniel Kahneman / Amos Tversky, distributed generation, Elliott wave, en.wikipedia.org, feminist movement, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, mental accounting, meta analysis, meta-analysis, p-value, pattern recognition, Ponzi scheme, price anchoring, price stability, quantitative trading / quantitative finance, Ralph Nelson Elliott, random walk, retrograde motion, revision control, risk tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Robert Shiller, Sharpe ratio, short selling, statistical model, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

Popper’s logic of falsification can be seen as a protection against the confirmation bias that infects informal inference (see Chapter 2). One final example may help clarify the power of falsifying evidence and the weakness of confirmatory evidence. It is the famous problem of the black swan posed by philosopher John Stuart Mill (1806–1873). Suppose we wish to ascertain the truth of the proposition: ‘All swans are white.’ Mill said, and Popper concurred, that no matter how many white swans have been observed—that is, no matter how voluminous the confirmatory evidence—the proposition’s truth is never proven. A black swan may lurk just around the next corner. This is the limitation of induction that so upset Hume. However, by merely observing a single non-white swan, one may declare with certitude that the proposition is false. The conditional syllogism below shows that the falsity of the proposition All swans are white is based on the valid deductive form falsification of the consequent.

Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance 25 (1970), 383–417, as cited by A. Shleifer in Inefficient Markets: An Introduction to Behavioral Finance (Oxford, UK: Oxford University Press, 2000), 1. A. Shleifer, Inefficient Markets: An Introduction to Behavioral Finance (Oxford, UK: Oxford University Press, 2000), 1. N.N. Taleb, Fooled by Randomness: The Hidden Role of Chance in the Markets and in Life (New York: Texere, 2001). A money manager with no skill whatsoever has a 0.5 probability of beating the market in any given time period (e.g., 1 year). In this experiment, Taleb assumed a universe of money managers with a 0.50 probability of beating the market in a given year. Actually there are three forms of EMH—strong, semistrong, and weak. The strong form contends that no information, including inside information, can be used to beat the market on a risk-adjusted basis.

If the 136 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS drives on copper-bracelet days are less than 25 yards better, evidence refuting the claim would be in hand. Limitations of Popper’s Method. As important as Popper’s method of falsification is to modern science, it has been criticized on a number of grounds. Critics assert that Popper’s contention that hypotheses can be definitively falsified overstates matters. Although the observation of a black swan can neatly and logically falsify the universal generalization that all swans are white, the hypotheses of real science are far more complex30 and probabilistic (nonuniversal). They are complex in the sense that a newly proposed hypothesis rests on numerous auxiliary hypotheses that are assumed to be true. Thus, if a prediction deduced from the new hypothesis is later falsified, it is not clear whether the new hypothesis was in error or one of the many auxiliary hypotheses was incorrect.

 

pages: 261 words: 86,905

How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester

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asset allocation, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamonds, Bretton Woods, BRICs, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, financial innovation, Flash crash, forward guidance, Gini coefficient, global reserve currency, high net worth, High speed trading, hindsight bias, income inequality, inflation targeting, interest rate swap, Isaac Newton, Jaron Lanier, joint-stock company, joint-stock limited liability company, Kodak vs Instagram, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, Plutocrats, plutocrats, Ponzi scheme, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Richard Feynman, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, South Sea Bubble, sovereign wealth fund, Steve Jobs, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, trickle-down economics, Washington Consensus, working poor, yield curve

.† Black-Scholes The name of the formula that made it possible to create prices in the derivatives markets; before the equation was discovered (or invented, depending on your view of what mathematics does), uncertainties about how probabilities changed made it impossible to create accurate prices for an option over time. Black-Scholes gave a way of mathematically modeling the price of the options, and led to a huge boom in the global market for derivatives. The equation is named after the two men who created it, Fischer Black and Myron Scholes. Black Swan A term coined by the philospher-investor Naseem Nicholas Taleb for an event so rare it doesn’t fit in normal models of statistical probability. As a result, institutions such as banks are grievously unprepared for this kind of very rare event. It is possible that humans are hardwired not to have a good intuitive understanding of these kinds of risks. An example would be Earth being hit by an asteroid big enough to cause global disaster, something NASA says happens every 500,000 years or so.22 That puts the odds of its happening in a typical 80-year life at one in 6,250—which is uncomfortably high.

(Allen Lane: London, 2013), p. xii. 47See www.gartner.com/technology/research/methodologies/hype-cycle.jsp. 48See cdn.budgetresponsibility.independent.gov.uk/2013-FSR_OBR_web.pdf. 49John Maynard Keynes, The Economic Consequences of the Peace (London: Macmillan, 1919), p. 118. 50See www.forbes.com/sites/luisakroll/2011/04/22/just-how-rich-is-queen-elizabeth-and-her-family/ and www.guardian.co.uk/artanddesign/2006/apr/20/art.monarchy. 51Here’s the actual napkin: web.archive.org/web/20110503200219/http://www.polyconomics.com/gallery/Napkin003.jpg. 52See www.cdc.gov/nchs/data/hus/hus12.pdf#017. 53See media.bloomberg.com/bb/avfile/rJ5Q_k_NsIk8. 54Available at www.marxists.org/archive/marx/works/1852/18th-brumaire/. 55The study, called “When Choice Is Demotivating,” is available at www.columbia.edu/~ss957/articles/Choice_is_Demotivating.pdf. 56See www.theguardian.com/commentisfree/2013/jul/30/obama-grand-bargain-speech-middle-class. 57At www.un.org/millenniumgoals/poverty.shtml. 58See www.businessinsider.com/most-miserable-countries-in-the-world-2013-2?op=1. 59Graham, The Intelligent Investor, p. 108. 60Nassim Nicholas Taleb, http://www.bloomberg.com/news/2010-10-08/taleb-says-crisis-makes-nobel-panel-liable-for-legitimizing-economists.html. 61See www.oecd-berlin.de/charts/PIAAC/. 62See www.ft.com/cms/s/0/cb2bfb08-0e06-11e0-86e9-00144feabdc0.html#axzz2acmNJdUy. 63Burton Malkiel, A Random Walk Down Wall Street (New York: Norton, 2007), p. 119. 64Joseph Stiglitz, “A Tax System Stacked against the 99 Percent,” New York Times, 14 April 2013, available at opinionator.blogs.nytimes.com/2013/04/14/a-tax-system-stacked-against-the-99-percent/?

The 2013 prize was an absolute classic in this respect. It was awarded both to the person who created the theory of efficient markets, Eugene Fama, and the man who has mounted the most sustained empirical critique of the theory, Robert Schiller. It’s like awarding a prize both to Galileo, for saying that Earth isn’t the center of the universe, and to Pope Paul V, for saying that it is. Nassim Nicholas Taleb, a particularly trenchant critic of the prize, has argued that investors who lost money in the credit crunch should sue the prize for giving credibility to mistaken mathematical theories of how things should be priced. “I want to make the Nobel accountable. . . . Citizens should sue if they lost their job or business owing to the breakdown in the financial system.” 60 This is a bit like Richard Dawkins’s idea that astrologers should be sued for fraud, in that it’s unlikely to happen but fun to think about.

 

pages: 467 words: 154,960

Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel

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Albert Einstein, asset allocation, Atul Gawande, backtesting, Bernie Madoff, Black Swan, buy low sell high, capital asset pricing model, Clayton Christensen, commodity trading advisor, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, fiat currency, fixed income, game design, hindsight bias, housing crisis, index fund, Isaac Newton, John Nash: game theory, linear programming, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, mental accounting, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Renaissance Technologies, Richard Feynman, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, systematic trading, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, volatility arbitrage, William of Occam

Abraham Trading Company Annual Performance Breakdown Yearly Statistics Year Return Drawdown 2008 28.77% 2007 19.20% –7.24% 2006 8.93% –9.03% 2005 –10.95% –26.80% (continues) 347 Much of what happens in history comes from “Black Swan dynamics,” very large, sudden, and totally unpredictable “outliers”… Our track record in predicting those events is dismal; yet by some mechanism called the hindsight bias, we think that we understand them… Why are we so bad at understanding this type of uncertainty? It is now the scientific consensus that our risk-avoidance mechanism is not mediated by the cognitive modules of our brain, but rather by the emotional ones. This may have made us fit for the Pleistocene era. “Our risk machinery is designed to run away from tigers; it is not designed for the information-laden modern world.” Nassim Nicholas Taleb, Learning to Expect the Unexpected, Edge.org 348 Trend Following (Updated Edition): Learn to Make Millions in Up or Down Markets Yearly Statistics Year Return Drawdown 2004 15.38% –12.25% 2003 74.66% –14.71% 2002 21.51% –11.81% 2001 19.50% –14.11% 2000 13.54% –17.00% 1999 4.76% –15.17% 1998 4.39% –14.34% 1997 10.88% –12.05% 1996 –0.42% –19.69% 1995 6.12% –21.02% 1994 24.22% –10.99% 1993 34.29% –10.50% 1992 –10.50% –26.55% 1991 24.39% –27.01% 1990 89.95% –7.90% 1989 17.81% –31.96% 1988 142.04% –22.12% Month by Month Returns Month by Month Returns Year 1988 1989 1990 Jan 4.17 Feb Mar Apr May Jun Jul Aug –2.59 –8.78 –12.35 32.34 71.99 –2.82 3.45 –8.05 –12.64 13.91 –20.08 38.65 –4.4 16.08 –13.84 Sep Oct Nov Dec –1.98 8.01 17.83 4.51 –7.75 –14.40 10.30 39.52 3.65 1.81 9.45 12.90 –7.90 2.49 20.08 18.54 8.57 –0.36 0.31 –0.09 1991 –15.94 1.30 2.43 –13.70 2.94 2.11 –1.52 –6.33 11.61 16.61 –2.09 33.75 1992 –12.60 –6.00 –5.47 0.31 –5.71 6.58 16.52 1.92 –0.34 –3.31 4.65 –4.54 6.10 4.57 9.24 4.88 –1.22 6.60 –5.28 1.16 –6.59 3.71 12.83 1993 –4.21 349 Appendix B • Performance Guide Month by Month Returns Year Jan Feb Mar Apr 1994 –1.45 –4.16 2.87 –8.39 1995 –7.91 1.24 6.63 1996 –6.85 –13.78 May Jun Jul Aug Sep Oct Nov Dec 15.01 1.47 0.98 –7.38 5.05 5.43 14.24 1.06 4.73 8.22 0.11 –8.75 –5.34 –1.84 –6.67 –0.19 19.11 9.66 14.27 –9.41 1.52 –6.30 –3.34 6.03 16.84 2.45 –6.41 4.95 –5.37 2.10 7.46 –3.33 –11.39 0.94 4.67 1997 5.28 9.15 –1.50 –5.16 –1.32 0.38 4.11 –8.08 1998 –0.90 4.09 –4.45 –4.45 2.61 –2.34 –0.83 23.24 1999 –11.56 13.35 –9.43 7.52 –6.09 –0.68 –0.83 3.12 0.99 –9.57 13.64 8.41 2000 8.02 –9.05 –4.16 5.48 –2.58 –2.19 –5.26 11.76 –4.53 9.51 8.58 –0.18 2001 2.28 2.99 15.17 –10.20 5.13 4.47 –2.58 4.89 9.28 4.13 –13.68 –0.50 2002 –1.73 1.33 –6.62 4.99 1.51 7.75 –3.97 9.86 3.29 –10.19 –1.80 18.41 2003 24.18 13.18 –4.73 2.02 5.59 –7.06 –4.86 –3.54 7.02 22.09 –0.03 8.69 2004 0.47 8.38 0.88 –6.22 2.53 1.37 6.74 –12.25 7.84 4.32 2.79 –0.51 2005 –5.48 –8.95 –1.00 –10.04 1.93 6.66 –12.16 15.74 –5.79 –5.98 14.15 3.96 2006 2.56 –1.53 5.71 2.75 –1.70 –2.32 –5.26 2.72 –1.51 4.08 2.23 1.41 2007 –1.08 –4.00 –2.32 6.50 4.96 3.66 –2.54 –3.73 5.20 4.32 1.16 6.47 2008 6.44 6.57 –0.21 0.34 –0.94 2.04 –4.19 0.08 5.55 4.73 2.02 3.72E E=estimated Campbell & Company, Inc.

Houdek, Robert (Bucky) Isaacson, Christian Jund, MaryAnn Kiely, Eddie Kwong, Pete Kyle, Eric Laing, Elina Manevich, Bill Mann, Jon Markman, Michael Martin, John Mauldin, Timothy M. McCann, Lizzie McLoughlin, James Montier, Georgia Nakou, Peter Navarro, Gail Osten, Michael Panzner, Bob Pardo, Baron Robertson, Jim Rogers, Murray Ruggiero, Michael Seneadza, Takaaki Sera, Tom Shanks, Howard Simons, Barry Sims, Aaron Smith, Michael Stephani, Richard Straus, Nassim Nicholas Taleb, Stephen Taub, Ken Tower, Ken Tropin, Tomoko Uchiyama, Thomas Vician, Jr., Robert Webb, Kate Welling, Gabriel Wisdom, Brent Wood, and Patrick L. Young for all of their efforts and support. And thank you to the following publications and writers who generously allowed me to quote from their work: Sol Waksman and Barclay Managed Futures Report, Futures Magazine, Managed Account Reports, Michael Rulle of Graham Capital Management, and Technical Analysis of Stocks and Commodities Magazine.

It’s not that they’re stupid; it’s not speculative frenzy; they’re just using these markets for a completely different purpose.” 121 This page intentionally left blank Big Events, Crashes, and Panics 4 “I have noticed that everyone who ever told me that the markets are efficient is poor.” —Larry Hite, Mint Investment Management Company “Rare events are always unexpected, otherwise they would not occur.” —Nassim Taleb1 To comprehend trend following’s true impact, you have to look at trend followers’ performance data—the returns they generate. Their performance data makes clear that they were the winners in the biggest trading events, bubbles, and crashes of the last 30 years. This chapter outlines high profile times where trend followers won huge profits in the zero-sum game. An investment in DUNN acts as a hedge against unpredictable market crises.

 

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The Irrational Economist: Making Decisions in a Dangerous World by Erwann Michel-Kerjan, Paul Slovic

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Andrei Shleifer, availability heuristic, bank run, Black Swan, Cass Sunstein, clean water, cognitive dissonance, collateralized debt obligation, complexity theory, conceptual framework, corporate social responsibility, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-subsidies, Daniel Kahneman / Amos Tversky, endowment effect, experimental economics, financial innovation, Fractional reserve banking, George Akerlof, hindsight bias, incomplete markets, invisible hand, Isaac Newton, iterative process, Loma Prieta earthquake, London Interbank Offered Rate, market bubble, market clearing, moral hazard, mortgage debt, placebo effect, price discrimination, price stability, RAND corporation, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, statistical model, stochastic process, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, ultimatum game, University of East Anglia, urban planning

RECOMMENDED READING Hogarth, R. M., and N. Karelaia (2007). “Heuristic and Linear Models of Judgment: Matching Rules and Environments.” Psychological Review 114, no. 3: 733-758. Makridakis, S., R. M. Hogarth, and A. Gaba (2009). Dance with Chance: Making Luck Work for You. Oxford: Oneworld Publications. Savage, L. J. (1954). The Foundations of Statistics. New York: John Wiley & Sons. Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. New York: Random House. Winter, S. G., G. Cattani, and A. Dorsch (2007). “The Value of Moderate Obsession: Insights from a New Model of Organizational Search.” Organization Science 18, no. 3: 403-419. 4 The More Who Die, the Less We Care PAUL SLOVIC A defining element of catastrophes is the magnitude of their harmful consequences.

The reasons Marsh and Merton gave for thinking that any market inefficiencies will be “arbitraged away” by smart money have a number of limitations (Shleifer and Vishny, 1997; Barberis and Thaler, 2003). 7 These ads are displayed and discussed in Mullainathan and Shleifer (2005). 8 Allen et al. (2002). 9 Higgins (2005). Chapter 3 Hogarth: Subways, Coconuts, and Foggy Minefields 1 Taleb (2007) refers to coconuts with large consequences as “black swans.” 2 For a related approach, see the interesting work by Winter, Cattani, and Dorsch (2007). 3 See, for example, Hogarth and Karelaia (2007). Chapter 4 Slovic: The More Who Die, the Less We Care 1 Portions of this chapter appeared earlier in P. Slovic, “If I Look at the Mass I Will Never Act: Psychic Numbing and Genocide.” Judgment and Decision Making 2 (2007): 79-95.

 

pages: 379 words: 109,612

Is the Internet Changing the Way You Think?: The Net's Impact on Our Minds and Future by John Brockman

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A Declaration of the Independence of Cyberspace, Albert Einstein, AltaVista, Amazon Mechanical Turk, Asperger Syndrome, availability heuristic, Benoit Mandelbrot, biofilm, Black Swan, British Empire, conceptual framework, corporate governance, Danny Hillis, Douglas Engelbart, Emanuel Derman, epigenetics, Flynn Effect, Frank Gehry, Google Earth, hive mind, Howard Rheingold, index card, information retrieval, Internet Archive, invention of writing, Jane Jacobs, Jaron Lanier, Kevin Kelly, lone genius, loss aversion, mandelbrot fractal, Marshall McLuhan, Menlo Park, meta analysis, meta-analysis, New Journalism, Nicholas Carr, out of africa, Ponzi scheme, pre–internet, Richard Feynman, Richard Feynman, Rodney Brooks, Ronald Reagan, Schrödinger's Cat, Search for Extraterrestrial Intelligence, SETI@home, Silicon Valley, Skype, slashdot, smart grid, social graph, social software, social web, Stephen Hawking, Steve Wozniak, Steven Pinker, Stewart Brand, Ted Nelson, telepresence, the medium is the message, the scientific method, The Wealth of Nations by Adam Smith, theory of mind, trade route, upwardly mobile, Vernor Vinge, Whole Earth Catalog, X Prize

S is for Salon of the twenty-first century The Internet has made me think more about whom I would like to introduce to whom, and about whether to cyberintroduce people or introduce them in person through actual salons for the twenty-first century (see the Brutally Early Club). The Degradation of Predictability—and Knowledge Nassim N. Taleb Distinguished Professor of Risk Engineering, New York University–Polytechnic Institute; principal, Universa Investments; author, The Black Swan I used to think the problem of information is that it turns Homo sapiens into fools—we gain disproportionately in confidence, particularly in domains where information is wrapped in a high degree of noise (say, epidemiology, genetics, economics, etc.). So we end up thinking we know more than we do, which, in economic life, causes foolish risk taking.

When I started trading, I went on a news diet and I saw things with more clarity. I also saw how people built too many theories based on sterile news, fooled by the randomness effect. But things are a lot worse. Now I think that, in addition, the supply and spread of information turns the world into Extremistan (a world I describe as one in which random variables are dominated by extremes, with Black Swans playing a large role in them). The Internet, by spreading information, causes an increase in interdependence, the exacerbation of fads (bestsellers like Harry Potter and runs on banks become planetary). Such a world is more “complex,” more moody, much less predictable. So consider the explosive situation: More information (particularly thanks to the Internet) causes more confidence and illusions of knowledge while degrading predictability.

Tipler We Have Become Hunter-Gatherers of Images and Information: Lee Smolin The Human Texture of Information: Jon Kleinberg Not at All: Steven Pinker This Is Your Brain on Internet: Terrence Sejnowski The Sculpting of Human Thought: Donald Hoffman What Kind of a Dumb Question Is That?: Andy Clark Public Dreaming: Thomas Metzinger The Age of (Quantum) Information?: Anton Zeilinger Edge, A to Z (Pars Pro Toto): Hans Ulrich Obrist The Degradation of Predictability—and Knowledge: Nassim N. Taleb Calling You on Your Crap: Sean Carroll How I Think About How I Think: Lera Boroditsky I Am Not Exactly a Thinking Person— I Am a Poet: Jonas Mekas Kayaks Versus Canoes: George Dyson The Upload Has Begun: Sam Harris Hell if I Know: Gregory Paul What I Notice: Brian Eno It’s Not What You Know, It’s What You Can Find Out: Marissa Mayer When I’m on the Net, I Start to Think: Ai Weiwei The Internet Has Become Boring: Andrian Kreye The Dumb Butler: Joshua Greene Finding Stuff Remains a Challenge: Philip Campbell Attention, Crap Detection, and Network Awareness: Howard Rheingold Information Metabolism: Esther Dyson Ctrl + Click to Follow Link: George Church Replacing Experience with Facsimile: Eric Fischl and April Gornik Outsourcing the Mind: Gerd Gigerenzer A Prehistorian’s Perspective: Timothy Taylor The Fourth Phase of Homo sapiens: Scott Atran Transience Is Now Permanence: Douglas Coupland A Return to the Scarlet-Letter Savanna: Jesse Bering Take Love: Helen Fisher Internet Mating Strategies: David M.

 

pages: 361 words: 81,068

The Internet Is Not the Answer by Andrew Keen

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3D printing, A Declaration of the Independence of Cyberspace, Airbnb, AltaVista, Andrew Keen, augmented reality, Bay Area Rapid Transit, Berlin Wall, bitcoin, Black Swan, Burning Man, Cass Sunstein, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, cuban missile crisis, David Brooks, disintermediation, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, full employment, future of work, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kickstarter, Kodak vs Instagram, Lean Startup, libertarian paternalism, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, move fast and break things, Nate Silver, Network effects, new economy, Nicholas Carr, nonsequential writing, Norbert Wiener, Occupy movement, packet switching, PageRank, Paul Graham, Peter Thiel, Plutocrats, plutocrats, Potemkin village, precariat, pre–internet, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, TaskRabbit, Ted Nelson, telemarketer, the medium is the message, Thomas L Friedman, Tyler Cowen: Great Stagnation, Uber for X, urban planning, Vannevar Bush, Whole Earth Catalog, WikiLeaks, winner-take-all economy, working poor, Y Combinator

“How do you plan to handle the narrative fallacy?” the Internet entrepreneur asked, leaning forward on his elbows and staring in his bug-eyed way at Stone.9 There was a nervous silence as Stone looked at Bezos blankly. The “narrative fallacy,” Bezos explained to Stone, is the tendency, particularly of authors, “to turn complex realities” into “easily understandable narratives.” As a fan of Nassim Nicholas Taleb’s The Black Swan, a book that introduced the concept, Jeff Bezos believes that the world—like that map on the wall of Ericsson’s Stockholm office—is so random and chaotic that it can’t be easily summarized (except, of course, as being randomly chaotic). The history of Amazon is too complicated and fortuitous to be squeezed into an understandable narrative, Bezos was warning Stone. And he would, no doubt, argue that the history of the Internet, in which he and his Everything Store have played such a central role since Amazon.com went live on July 16, 1995, is equally complex and incomprehensible.

The poachers are now the gamekeepers. There is Mark Zuckerberg’s Harvard roommate, Chris Hughes, a cofounder of Facebook, who bought the venerable New Republic magazine in 2012. Then there’s Amazon right-libertarian CEO Jeff Bezos, who acquired the equally venerable Washington Post newspaper in 2013, no doubt giving all its reporters a required reading list including The Innovator’s Dilemma and The Black Swan. Meanwhile, multibillionaire eBay founder and chairman Pierre Omidyar has set up his own new Internet publishing empire, First Look Media, and used his massive wealth to hire superstar investigative journalists like Glenn Greenwald and Matt Taibbi to peddle Omidyar’s own left-libertarian agenda. For the last quarter of a century, we’ve been told ad nauseam by tenured professors of journalism like New York University’s Jay Rosen that the Internet’s destruction of old media is a good thing because it democratizes the information economy.

 

pages: 836 words: 158,284

The 4-Hour Body: An Uncommon Guide to Rapid Fat-Loss, Incredible Sex, and Becoming Superhuman by Timothy Ferriss

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23andMe, airport security, Albert Einstein, Black Swan, Buckminster Fuller, carbon footprint, cognitive dissonance, Columbine, correlation does not imply causation, Dean Kamen, game design, Gary Taubes, index card, Kevin Kelly, knowledge economy, life extension, Mahatma Gandhi, microbiome, p-value, Parkinson's law, Paul Buchheit, placebo effect, Productivity paradox, publish or perish, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Richard Feynman, Silicon Valley, Silicon Valley startup, Skype, stem cell, Steve Jobs, Thorstein Veblen, wage slave, William of Occam

Critics of small trials or self-experimentation often miss this. If something appears to produce a 300% change, you don’t need that many people to show significance, assuming you’re controlling variables. • It is not kosher to combine p-values from multiple experiments to make something more or less believable. That’s another trick of bad scientists and mistake of uninformed journalists. TOOLS AND TRICKS The Black Swan by Nassim Taleb (www.fourhourbody.com/blackswan) Taleb, also author of the bestseller Fooled by Randomness, is the reigning king when it comes to explaining how we fool ourselves and how we can limit the damage. Our instinct to underestimate the occurrence of some events, while overestimating others, is a principal cause of enormous pain. This book should be required reading. The Corporation, DVD (www.fourhourbody.com/corporation) This is a disturbing documentary about the American corporation and its relentless pursuit of profit at the expense of our culture.

Does this make him guilty of skewing data to serve corporate interests? No. But it should make you look closely at the studies themselves before accepting the headlines and making behavioral changes in your life. The Goal of this Chapter vs. the Goal of this Book Understanding how to act under conditions of incomplete information is the highest and most urgent human pursuit. —Nassim Taleb, The Black Swan Are the experiments in this book bulletproof? Far from it. All studies are flawed in some respect, often for legitimate cost or ethical considerations. I use myself as a single subject, and (with a few exceptions) I neither randomize nor create a control. Some scientists will, no doubt, have a field day picking these self-experiments apart. This doesn’t bother me, and it shouldn’t bother you.

There are, of course, some outstanding companies with solid R&D and uncompromising ethics, but they are few and far between. 4. I have absolutely no financial interest in any of the supplements I recommend in this book. If you purchase any supplement from a link in this book, an affiliate commission is sent directly to the nonprofit DonorsChoose.org, which helps public schools in the United States. 5. Philosopher Nassim N. Taleb noted an important difference between language and biology that I’d like to underscore: the former is largely known and the latter is largely unknown. Thus, our 2.5% is not 2.5% of a perfect finite body of knowledge, but the most empirically valuable 2.5% of what we know now. FUNDAMENTALS— FIRST AND FOREMOST THE MINIMUM EFFECTIVE DOSE From Microwaves to Fat-Loss Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.

 

pages: 598 words: 134,339

Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Bruce Schneier

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23andMe, Airbnb, airport security, AltaVista, Anne Wojcicki, augmented reality, Benjamin Mako Hill, Black Swan, Brewster Kahle, Brian Krebs, call centre, Cass Sunstein, Chelsea Manning, citizen journalism, cloud computing, congestion charging, disintermediation, Edward Snowden, experimental subject, failed state, fault tolerance, Ferguson, Missouri, Filter Bubble, Firefox, friendly fire, Google Chrome, Google Glasses, hindsight bias, informal economy, Internet Archive, Internet of things, Jacob Appelbaum, Jaron Lanier, Julian Assange, Kevin Kelly, license plate recognition, linked data, Lyft, Mark Zuckerberg, Nash equilibrium, Nate Silver, national security letter, Network effects, Occupy movement, payday loans, pre–internet, price discrimination, profit motive, race to the bottom, RAND corporation, recommendation engine, RFID, self-driving car, Silicon Valley, Skype, smart cities, smart grid, Snapchat, social graph, software as a service, South China Sea, stealth mode startup, Steven Levy, Stuxnet, TaskRabbit, telemarketer, Tim Cook: Apple, transaction costs, Uber and Lyft, urban planning, WikiLeaks, zero day

That doesn’t stop us: When we look back at an event and see all the evidence, we often believe we should have connected the dots. There’s a name for that: hindsight bias. The useful bits of data are obvious after the fact, but were only a few items in a sea of millions of irrelevant data bits beforehand. And those data bits could have been assembled to point in a million different directions. the “narrative fallacy”: Nassim Nicholas Taleb (2007), “The narrative fallacy,” in The Black Swan: The Impact of the Highly Improbable, Random House, chap. 6, http://www.fooledbyrandomness.com. The TSA’s no-fly list: Associated Press (2 Feb 2012), “U.S. no-fly list doubles in one year,” USA Today, http://usatoday30.usatoday.com/news/washington/story/2012-02-02/no-fly-list/52926968/1. the watch list: Eric Schmitt and Michael S. Schmidt (24 Apr 2013), “2 U.S. agencies added Boston bomb suspect to watch list,” New York Times, https://www.nytimes.com/2013/04/25/us/tamerlan-tsarnaev-bomb-suspect-was-on-watch-lists.html.

However, this is a terribly misleading metaphor. Connecting the dots in a coloring book is easy, because they’re all numbered and visible. In real life, the dots can only be recognized after the fact. That doesn’t stop us from demanding to know why the authorities couldn’t connect the dots. The warning signs left by the Fort Hood shooter, the Boston Marathon bombers, and the Isla Vista shooter look obvious in hindsight. Nassim Taleb, an expert on risk engineering, calls this tendency the “narrative fallacy.” Humans are natural storytellers, and the world of stories is much more tidy, predictable, and coherent than reality. Millions of people behave strangely enough to attract the FBI’s notice, and almost all of them are harmless. The TSA’s no-fly list has over 20,000 people on it. The Terrorist Identities Datamart Environment, also known as the watch list, has 680,000, 40% of whom have “no recognized terrorist group affiliation.”

You can think of the difference between tactical and strategic oversight as the difference between doing things right and doing the right things. Both are required. Neither kind of oversight works without accountability. Those entrusted with power can’t be free to abuse it with impunity; there must be penalties for abuse. Oversight without accountability means that nothing changes, as we’ve learned again and again. Or, as risk analyst Nassim Taleb points out, organizations are less likely to abuse their power when people have skin in the game. It’s easy to say “transparency, oversight, and accountability,” but much harder to make those principles work in practice. Still, we have to try—and I’ll get to how to do that in the next chapter. These three things give us the confidence to trust powerful institutions. If we’re going to give them power over us, we need reassurance that they will act in our interests and not abuse that power.

 

pages: 437 words: 132,041

Alex's Adventures in Numberland by Alex Bellos

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Andrew Wiles, Antoine Gombaud: Chevalier de Méré, Black Swan, Black-Scholes formula, Claude Shannon: information theory, computer age, Daniel Kahneman / Amos Tversky, family office, forensic accounting, game design, Georg Cantor, Henri Poincaré, Isaac Newton, pattern recognition, Paul Erdős, probability theory / Blaise Pascal / Pierre de Fermat, random walk, Richard Feynman, Richard Feynman, SETI@home, Steve Jobs, The Bell Curve by Richard Herrnstein and Charles Murray, traveling salesman

These curves describe distributions in which extreme events are more likely than if the distribution were normal. For instance, if the variation in the price of a share were fat-tailed, it would mean there was more of a chance of a dramatic drop, or hike, in price than if the variation were normally distributed. For this reason, it can sometimes be reckless to assume a bell curve over a fat-tailed curve. The economist Nassim Nicholas Taleb’s position in his bestselling book The Black Swan is that we have tended to underestimate the size and importance of the tails in distribution curves. He argues that the bell curve is a historically defective model because it cannot anticipate the occurrence of, or predict the impact of, very rare, extreme events – such as a major scientific discovery like the invention of the internet, or of a terrorist attack like 9/11.

., Extreme Measures, Bloomsbury, London, 2004 Cline Cohen, P., A Calculating People: The Spread of Numeracy in Early America, University of Chicago Press, IL, 1982 Cohen, I. B., The Triumph of Numbers, W. W. Norton, New York, 2005 Edwards, A.W.F., Pascal’s Arithmetical Triangle, Johns Hopkins University Press, Baltimore, MD, 1987 Kuper S., and Szymanski S., Why England Lose, HarperCollins, London, 2009 Taleb, N.N., The Black Swan, Penguin, London, 2007 CHAPTER ELEVEN While it is still an open question whether the universe is flat, spherical or hyperbolic, the universe is certainly pretty flat; if its curvature does indeed deviate from zero, it does so only very slightly. An irony of testing the universe for its curvature, however, is that it can never be conclusively proved that the universe is flat since there will always be measurement error.

 

pages: 573 words: 163,302

Year's Best SF 15 by David G. Hartwell; Kathryn Cramer

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air freight, Black Swan, experimental subject, Georg Cantor, gravity well, job automation, Kuiper Belt, phenotype, semantic web

He is an American and citizen of the world, drawn to events and especially people tipping the present over into the future. His short fiction, now as likely to be fantasy as SF, is one of the finest bodies of work in the genre over the last three decades. “Black Swan” originally appeared in Italian as “Cigno Nero” in the Spring 2009 issue of ROBOT magazine. It was subsequently published in Interzone, which in spite of its small circulation continues to be a major venue for SF and fantasy. The title refers to the concept behind Nassim Nicholas Taleb’s book The Black Swan: The Impact of the Highly Improbable. The ethical journalist protects a confidential source. So I protected ‘Massimo Montaldo,’ although I knew that wasn’t his name. Massimo shambled through the tall glass doors, dropped his valise with a thump, and sat across the table.

Surely that was a great loss, but how could anybody guess the extent of that loss? A stroke of genius is a black swan, beyond prediction, beyond expectation. If a black swan never arrives, how on Earth could its absence be guessed? The chasm between Massimo’s version of Italy and my Italy was invisible—yet all encompassing. It was exactly like the stark difference between the man I was now, and the man I’d been one short hour ago. A black swan can never be predicted, expected, or categorized. A black swan, when it arrives, cannot even be recognized as a black swan. When the black swan assaults us, with the wingbeats of some rapist Jupiter, then we must rewrite history. Maybe a newsman writes a news story, which is history’s first draft. Yet the news never shouts that history has black swans. The news never tells us that our universe is contingent, that our fate hinges on changes too huge for us to comprehend, or too small for us to see.

Then they were aloft, all six of them, dragons returning to the sky where they had been born. Ling Yun spared not a glance backwards, but sang a quiet little melody to herself as they headed for the stars. Black Swan BRUCE STERLING Bruce Sterling (www.wired.com/beyond_the_beyond/) lives usually in exotic places in Europe, from which he continues his lifelong habit of cultural commentary. He reports that he “is dividing his atemporal time-zones among Austin, Turin, and Belgrade, and his alternate global identities as Bruce Sterling, Bruno Argento, and Boris Srebro.” “Black Swan” is one of those “Bruno Argento” efforts, written in Torino and originally published in Italy. His most recent novel is SF, The Caryatids (2009). His short fiction is collected in Ascendancies: The Best of Bruce Sterling (2007).

 

pages: 274 words: 66,721

Double Entry: How the Merchants of Venice Shaped the Modern World - and How Their Invention Could Make or Break the Planet by Jane Gleeson-White

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Affordable Care Act / Obamacare, Bernie Madoff, Black Swan, British Empire, carbon footprint, corporate governance, credit crunch, double entry bookkeeping, full employment, Gordon Gekko, income inequality, invention of movable type, invention of writing, Islamic Golden Age, Johann Wolfgang von Goethe, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, means of production, Naomi Klein, Ponzi scheme, shareholder value, Silicon Valley, Simon Kuznets, spice trade, spinning jenny, The Wealth of Nations by Adam Smith, Thomas Malthus, trade route, traveling salesman, upwardly mobile

Today these numbers are manipulated by the powerful wizards of Wall Street, the mathematical geniuses who run the quantitative equity (or ‘quant’) funds, which rely on complex and sophisticated mathematical algorithms to find the glitches and anomalies, the hidden patterns in the markets, the infinitesimal stock movements and trends which yield their fortunes. Nassim Nicholas Taleb, the author of the bestselling book on improbability, The Black Swan, himself a former quant nerd, says of the quant nerds: ‘Most are idiot savants brought to industrial proportions.’ The elite funds run by these ‘idiot savants’ are called ‘black-box’ funds: ‘opaque to outsiders, the boxes contain investment magic understood by only the wizards who conjured it up’. As the Washington Post’s Frank Ahrens says, the allure of a unifying, perfect mathematical formula with which to generate a fortune from financial markets is powerful.

Crusades 16, 17, 18 currencies 100 da Gama, Vasco 29 da Pisa, Leonardo see Fibonacci da Vinci, Leonardo 7, 27, 32, 47, 60, 65, 80–2, 84, 87–8 Dafforne, Richard 120–3 Dandolo, Enrico 52 Dark Ages dark arts 35, 83 Darwin, Charles 139, 165 Das Kapital (Marx) 165 Dasgupta, Sir Partha 231–2, 237, 238, 239 Datini, Francesco 23–6, 52, 96 de’ Barbari, Jacopo 79 de’ Belfolci, Folco 34, 44 De divina proportione (Pacioli) 66, 82, 84, 85–6 De ludo scacchorum (Pacioli) 87–8 De pictura (Alberti) 60, 117 De quinque corporibus (Piero) 66 De viribus quantitatis (Pacioli) 83 Dean, Graeme W. 203 debit and credit entries 13, 55, 93–4, 100 difficulties 101–2, 122–3 The Decline of the West (Spengler) 167 Defoe, Daniel 127–8 della Francesca, Piero 7, 32, 34, 44–5, 46, 47 mathematical treatises 45, 66, 75 perspective painting 60, 64, 76–7 della Rovere, Giuliano 59 Deloitte, William 145 Deloitte Touche Tohmatsu 217 demand management 185 democracy 15 depreciation 148, 149, 231 Der moderne Kapitalismus (Sombart) 161–2, 171 derivatives market 198, 200 Descartes, René 40 d’Este, Isabella 83, 84, 88 dividends 144, 146, 147, 148, 149, 202 Doge’s Palace 50, 56 Domenici, Pete V. 191 domestic accounts 15–16 double-entry bookkeeping 8, 115, 120, 166 Badoer’s system 55 and capitalism 159–60, 161–75 and decision-making 126–7 earliest surviving 20–1 to improve the mind 125 link with rhetoric 172–3 in modern era 135–6, 249 origins 6–7, 16, 21–2 Pacioli’s definition 92–3 six essential features 20–1 texts on 117, 136 use by Datini 24, 26 Venetian 55, 67, 97–100, 123–7, 131 see also Particularis de computis et scripturis du Pont, Irénée 156 ducats 50, 55 Dürer, Albrecht 79–80 earnings per share (EPS) 219 earth see planet Earth Earth Summit 2012 248–9 East India Company 142 Ebbers, Bernie 213 eco-accounting 249 economic growth 192–3, 225, 227, 233, 242, 245, 248 economics 185 political economy 171 ecosystems 239–40, 247 education 245 Euclid’s Elements 37–8 quadrivium 36, 38, 43 trivium 38, 43 Egypt 35, 36 Eisenstein, Elizabeth 116–17 Elements (Euclid) 37–8, 39, 67, 68, 84 Elgin Marbles 15 Engels, Friedrich 162, 164, 165 England 116, 121, 131, 133, 147 Enron 3, 173, 194–9, 201, 207, 212–13, 214–16, 222–3 environmental accounting 233–8, 245, 247 environmental damage 222–3, 224–5, 232–3, 240, 241–2, 248 equity 21, 243 Erasmus of Rotterdam 68, 84–5 Erlich, Everett 235 Ernst & Young 209, 210, 216, 217 Espeland, Wendy Nelson 172–3 Euclid, Elements 37–8, 39, 67, 68, 75, 84 Eugenius IV, Pope 34 Europe 17, 20, 21, 22–3, 40, 116, 156, 188 accounting associations 153 currencies 25 medieval 26, 70–1 universities 30, 40, 42 vernacular languages 41 European Environment Agency 247 Evans, John H. 173–5 exchange rates 55 externalities 236 factory system 136–7, 138, 139–41, 165, 166 Farolfi ledger 20–1 Fastow, Andrew 213 Fells, J.M. 140–1 Fibonacci 18–19, 75 Fibonacci numbers 19–20 Liber abaci 19–20, 22, 39–41, 63, 66, 67, 75 Financial Accounting Standards Board (US) 206, 213 financial information 203–6 financial statements 5, 143, 144, 146, 200, 205, 214, 215 Fitoussi, Jean-Paul 243–4 Florence 6, 17, 34, 61, 64, 84 abbaco schools 41 bank ledger 20 expansion of commerce 21 Flugel, Thomas 127 Fondaco dei Tedeschi 56 Ford Motor Company 250–1 forests 240, 241 Forster, E.M. 154–5 Forster, Nathaniel 137 France 147 Franciscans 62, 65, 88, 89 Frankfurt Book Fair 95 Frederick II 95 Freiburg 27 Friedman, Milton 221 fund transfers 54 G20 249 Galileo 116, 166 Geijsbeek, John B. 157–8 General Electric 204 The General Theory of Employment (Keynes) 177–8, 179, 183, 185–6 Genoa 6, 17 geometry 36, 37, 38, 63, 73, 75, 81 Germany 56, 68, 183 Gertner, Jon 244 Giovanni, Enrico 244 Giovanni Farolfi & Co. 20 Glitnir 5 Global Biodiversity Outlook 3 (Sukhdev) global financial crisis (2008) 3, 5, 197, 215, 242, 243–4 globalisation, of finance 206–7, 219, 221 Goethe, Johann W. von 128–31 golden ratio 66, 86 Goodwin, Sir Fred 197 governmental accounting 120 grammar 38, 43 Great Depression 177, 178, 179, 180, 227 Greece, ancient 15 mathematics 34–5, 37–8, 61 philosophers 37 green accounting 244 Green Economy Report (Sukhdev) 248–9 Greenspan, Alan 227–8 Gross Domestic Product (GDP) 3, 180–2, 225, 227–30, 232–3, 235, 237–8, 242–3, 246 alternatives to 243–7, 249 failings of 246 Gross National Product (GNP) 1–3, 181, 190, 231 Groves, Eddy 208–9 Guidobaldo, Duke of Urbino 66, 72, 79, 92 Gutenberg, Johann 68, 77 Hagen, Everett 186 Hamilton, Alexander 22 Hammurabi’s Code 14 Haq, Mahbub ul 245 Henry VIII 25 Herodotus 36 HIH 208, 209, 213, 215 Hindu–Arabic arithmetic 34, 41, 62, 67 Hindu–Arabic numerals 18–19, 21, 26–7, 38, 44, 52, 71, 75 Hoenig, Chris 246–7 honeybee pollination 237 Hoover, Herbert 177 Hopwood, Anthony housework, unpaid 229 How to Pay for the War (Keynes) 182–3 Hudson, George 142–3 human capital 231, 248 Human Development Index 245 Humanism (Florence) 43–4, 59–60, 68 Huxley, Aldous 32–3 income measurement 218–19, 226 income statements 5, 202, 203, 219 in ancient Rome 16–17 see also profit and loss accounts India 29, 238 trade/double entry 22 Indonesia 240 industrial revolution 131, 133, 139, 200, 226 inflation 182, 183 information processing 203 Institute of Accountants and Bookkeepers of New York (IABNY) 156, 157 Institute of Chartered Accountants in England and Wales (ICAEW) 153, 205–6 Insull, Samuel 202, 214 Insull Utility Investments 201–2 interest payments 25, 54, 96 international accounting 189, 207 International Accounting Standards Board 207, 214 International Monetary Fund 187 internet 204 inventory 97–9, 101 Islam 22, 39 Italy 6, 7, 16, 19, 28, 167–8 mathematics 34–42, 62 Jerusalem 17 joint stock companies 133, 136, 142, 147, 148 Joint Stock Companies Act 1844 144, 149 Jones, Edward T. 133–6 Jones’ English System of Book-keeping 133–6 journal 99, 100, 101, 103, 118, 203 Julius II, Pope 59 Kennedy, Robert F. 1–3, 229–30, 246 Keynes, John Maynard 8, 176, 177–80, 182–7, 190, 250 Klein, Naomi 221, 233 KPMG 210, 214, 217 Kreuger, Ivar 201 Kreuger & Toll 201 Kublai Khan 18 Kuznets, Simon 2, 177, 180–1, 189, 229 Lanchester, John 4, 198 Landefeld, Steven 228 Landsbanki 5 Latin 35, 41, 63, 71, 72, 73, 74, 116, 220 Lawrence, D.H. 154–5 Lay, Kenneth 195, 196, 197, 212–13, 214 ledgers 20, 93, 99–100, 103–4, 118, 203 14th century 24, 93 Badoer’s 52, 55 balancing 111 closing accounts 111–13 Farolfi 20–1 Lee, G.A. 20–1 Lee, Thomas A. 203 Lehman Brothers 5, 216 Liber abaci (Fibonacci) 19–20, 22, 39–41, 63, 66, 67, 75 limited liability 147–8, 149 Littleton, A.C. 17, 140, 146, 147, 158–9 Liverpool and Manchester Railway 141 Lives of the Most Eminent Painters (Vasari) 46 Living Planet Survey 241–2 Lloyds-HBOS 5 London and North Western Railway 141 Louis XII 82 Machiavelli 30 Mackinnon, Nick 79–80 Madoff, Bernie 142 Madonna and Child with Saints 47 magic 35, 40, 83, 220 Mair, John 118, 125, 130 Malatesta family 33–4, 43 Malthus, Thomas 171 Manchester cotton mill (Engels) 165 Mandela, Nelson 221 Mantua 83, 84 manufacturing 136–41 manuscripts 61, 70, 77 Manutius, Aldus 84 Manzoni, Domenico 118–19 maritime insurance 53 Mark the Evangelist 51–2 marketplace, 15th century 95 markets, impact on politics 221, 228 mark-to-model 213 Marshall Plan 188 Marx, Karl 162, 163–5, 171 mass production 138 mathematics 7, 22, 28, 47, 89–90 ancient Greek 34–5, 37–8, 63 Arab 18–19, 63 and art 85–6 Hindu 39–40 in Italy 34–42 and magic 35, 40, 220 medieval European 63, 251–2 taught as astrology 29–30, 42 universal application 73, 116–17 see also arithmetic Mattessich, Richard 12–13, 186 Maurice, Prince of Orange 120 Maxwell Communications 207 McDonald’s 224 Meade, James 183–4 measurement 23, 218–19 Medici of Florence 26, 64, 80, 168, 171 Mehmed II 57 Mellis, John 121 memorandum (waste book) 99, 101, 118 entering transactions 105–7, 118, 122 merchandise 104 merchant bankers 21, 26, 69 merchants 10, 23, 35, 41 Arab 18–19, 25 Indian 22 Italian 40, 42 Phoenician 36 Venetian 18, 27, 55–6, 69, 94–5, 149 Mesopotamia 12, 13, 14 metaphysics 36–7 Middle Ages 60, 251–2 Milan 30, 34, 47, 61, 80–3 Millennium Ecosystem Assessment 239 Monsanto 222 Monteage, Stephen 124, 126 Morgan, John Pierpont 156 multiplication 74, 75–6 music 36, 38 Naples 50, 61–2 Napoleonic War 145 national accounts 175, 179–88, 190–3, 226–7, 230, 242, 244 natural capital 230–1, 235–9 navigation charts 23 Neighborhood Tree Survey (NY) 241, 244 Netherlands 119, 120 New Deal (Roosevelt) 177, 202 New York Light Company 155 New York Stock Exchange 155, 176, 201 New Zealand 153, 230 Nicholas V, Pope 61 No Royal Road: Luca Pacioli and his times (Taylor) 46–7 Nordhaus, William D. 180, 191, 227 numbers 37, 218, 219–20, 249 Obama, Barack 215, 246 O’Grady, Oswald 208 Oldcastle, Hugh 121, 124 Olmert, Michael 168 One.Tel 208, 209, 213, 215 Organisation for Economic Co-operation and Development (OECD) 190, 242 Organisation for European Economic Co-operation (OEEC) 188 Ormerod, Paul 244 Ottoman Empire 29, 34, 50, 51, 56, 57, 116 Pacioli, Luca 7, 8, 27–8, 34, 35, 161, 219 abbaco mathematics 40, 41 as academic 65, 80, 84, 89 astrologer 42 birth 30 bookkeeping treatise see Particularis de computis and Piero della Francesca 45–6, 47–8 education 43–8 encyclopaedia see Summa de arithmetica on Euclid 84–5 games/tricks 83–4 itinerant teacher 61–6 last years 88–90 and Leonardo da Vinci 80–2, 84 in Milan 80–3 portrait 47, 79–80 and the printing press 66–72 remembered in Sansepolcro 31–2 in Rome 58–61 in Venice 49–58 Paganini, Paganino de 67–8, 71–2, 78, 85 painting 60, 64, 81 Pakistan 224, 245 Paris 23, 50 Particularis de computis et scripturis (Pacioli) 29–30, 78, 90–114, 117–18, 121 and capitalism 163 foundation of modern accounting 30, 75, 131, 157–9, 166 profit calculation 146–7 partnerships 108–9, 147 Patel, Raj 222, , 224 Patient Protection and Affordable Care Act 2010 (US) 246 patronage 59, 67, 70, 72 Paul II, Pope 59 Payen, Jean-Baptiste 139–40 Peking 18 Perspectiva (Witelo) 64 perspective 23, 42, 45, 60, 64, 76–7, 80, 82 Perugia 62–3, 64, 65 Petty, William 180 phi 86 Philip VI 23 philosophy 37, 40 Phoenicians 36 pi 36 Piazza San Marco 56 Pinto cars 250–1 Pisa 6, 17 Pitcher Partners 209, 210, 211–12 plagiarism 63 planet Earth 8–9, 248 accounting for 254 effects of cost-benefit approach 175 health of 224–5, 239 Plato 37 Platonic solids 45, 79, 86 Pliny the Elder 16 pollution 244 Polly Peck International 207 Polo, Marco 18 Ponzi scheme 142 Postlethwayt, Malachy 124 poverty 237, 246, 248, 249 Prato 23–4 Price, Samuel 145 Price Waterhouse 201, 207 PricewaterhouseCoopers 217 principlism 173–4 printing 29, 45, 60, 63, 66–72, 77–8, 90, 115–17 profit 21, 24, 97, 102–3, 127, 146–8, 159, 161, 167, 169 profit and loss accounts 55, 109–11, 112, 166 Pythagoras 35, 36–7 quadrivium 36, 38, 43 quant nerds 220 railways 141–3, 231 Ramsay, Ian 211 Ratdolt, Erhard 68, 116 record-keeping 15 Reformation 33 regulation 206–14, 215 Reid Murray Holdings 207–8 religion 24, 96, 116, 124–5, 220 see also Christian Church; Islam Renaissance 7, 8, 23, 26, 36, 59, 80, 86, 89, 168 art 6, 7, 44, 60, 86 Resurrection (Piero) 32, 33 retained-earnings statements 5, 219 rhetoric 172–3 Rialto 50, 55, 108 Ricardo, David 171 Rich, Jodee 213 Rinieri Fini & Brothers 20 Ripoli Press 70 Robert of Chester 39 Rockefeller, John D. 156 Roman numerals 19, 26–7, 38, 40, 71, 116 Romantic poets, English 131, 154 Rome 58–61, 64, 89 ancient 15–16 Rompiasi family 57, 58, 97 Roosevelt, Franklin D. 177, 178, 181, 202, 214, 215 Rose, Paul L. 71 Ross, Philip 209–10, 211 Rothschild banks 133 Royal Bank of Scotland 173, 197–8 royal estate management 16–17 Rule of Three 38, 41 Russia 153–4 salt 51 Samuelson, Paul A. 191, 227 Sansepolcro 30–4, 43–4, 48, 65, 77, 88–9, 168 Sanuto, Marco 66, 72 Sarbanes, Paul 191 Sarbanes-Oxley Act 2002 212, 215 Sarkozy, Nicolas 242–3, 245 satellite accounts 234–5 scandals/fraud 194–203, 206, 207–12, 215, 225 Schmandt-Besserat, Denise 11–12 Schumpeter, Joseph 169–70 science 35, 37, 40, 42, 67, 76, 116, 166–7 Scotland 27, 147, 150, 153 Scott, Sir Walter 150–1 Scuola di Rialto 58 Second World War 32, 181–5, 187, 227 Securities and Exchange Commission (US) 202–3, 213, 214 Sen, Amartya 243–4, 245 Sforza, Ludovico 80, 81–2, 85, 86, 168 Sikka, Prem 216, 217 Silberman, Mark 213 Simons, James 220 Sistine Chapel 65 Sixtus IV, Pope 59 Skidelsky, Robert 178, 182, 187 Skilling, Jeffrey K. 196, 197, 212, 214 Smith, Adam 171 social sciences 171, 175 socialism 171 Society of Accountants, Edinburgh 152 Sombart, Werner 161–2, 164, 165–6, 166–8, 169, 170, 171–2, 173 Spain 22, 39 Spengler, Oswald 167 Sri Lanka 232–3, 240 State of the USA 246 Stevin, Simon 120, 121, 166, 169 Stiglitz, Joseph 243–4 Stiglitz-Sen-Fitoussi Commission 243–4 stock markets 143 stocktaking 166 Stone, Sir Richard 183–5, 188–9, 190 sub-prime mortgages Sukhdev, Pavan Summa de arithmetica (Pacioli) 57, 61, 62–3, 64, 72–7, 80, 82 printing 66–8, 71–2 publication 32, 77–9 sustainability 232, 243, 249 System of Integrated Environmental and Economic Accounting (UN) 234 System of National Accounts (UN) 189–90, 247 tabulae rationum 16 Taleb, Nassim N. 220 tariffs 63 Tartaglia, Nicholas 76 Taylor, R. Emmett 47 T-column 99 Thomson, Charles Poulett 143 Tiber Valley 30, 31, 33 Timaeus (Plato) 37 time-and-motion studies 157 Toepfer, Klaus 238–9 Tolstoy, Leo 154 town clocks 23 trade 26, 53, 95 transaction analysis 123 transactions 105–7, 118, 122 in manufacturing accounts 140 Treviso Arithmetic 68 trial balances 123–4, 204 trivium 38, 43 unemployment 179, 182, 183, 187 United Kingdom 144, 145, 155, 201 GDP (2008) 3 United Kingdom (Cont.)

 

pages: 471 words: 124,585

The Ascent of Money: A Financial History of the World by Niall Ferguson

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Admiral Zheng, Andrei Shleifer, Asian financial crisis, asset allocation, asset-backed security, Atahualpa, bank run, banking crisis, banks create money, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, BRICs, British Empire, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, collateralized debt obligation, colonial exploitation, Corn Laws, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, deglobalization, diversification, diversified portfolio, double entry bookkeeping, Edmond Halley, Edward Glaeser, Edward Lloyd's coffeehouse, financial innovation, financial intermediation, fixed income, floating exchange rates, Fractional reserve banking, Francisco Pizarro, full employment, German hyperinflation, Hernando de Soto, high net worth, hindsight bias, Home mortgage interest deduction, Hyman Minsky, income inequality, interest rate swap, Isaac Newton, iterative process, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, labour mobility, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, means of production, Mikhail Gorbachev, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, mortgage tax deduction, Naomi Klein, Nick Leeson, Northern Rock, pension reform, price anchoring, price stability, principal–agent problem, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, quantitative hedge fund, RAND corporation, random walk, rent control, rent-seeking, reserve currency, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, seigniorage, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spice trade, structural adjustment programs, technology bubble, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, too big to fail, transaction costs, value at risk, Washington Consensus, Yom Kippur War

Afterword: The Descent of Money 1 For some fascinating insights into the limits of globalization, see Pankaj Ghemawat, Redefining Global Strategy: Crossing Borders in a World Where Differences Still Matter (Boston, 2007). 2 Frederic Mishkin, Weissman Center Distinguished Lecture, Baruch College, New York (12 October 2006). 3 Larry Neal, ‘A Shocking View of Economic History’, Journal of Economic History, 60, 2 (2000), pp. 317-34. 4 Robert J. Barro and José F. Ursúa, ‘Macroeconomic Crises since 1870’, Brookings Papers on Economic Activity (forthcoming). See also Robert J. Barro, ‘Rare Disasters and Asset Markets in the Twentieth Century’, Harvard University Working Paper (4 December 2005). 5 Nassim Nicholas Taleb, Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets (2nd edn., New York, 2005) 6 Idem, The Black Swan: The Impact of the Highly Improbable (London, 2007). 7 Georges Soros, The New Paradigm for Financial Markets: The Credit Crash of 2008 and What It Means, (New York, 2008), pp. 91 ff. 8 See Frank H. Knight, Risk, Uncertainty and Profit (Boston, 1921). 9 John Maynard Keynes, ‘The General Theory of Employment’, Economic Journal, 51, 2 (1937), p. 214. 10 Daniel Kahneman and Amos Tversky, ‘Prospect Theory: An Analysis of Decision under Risk’, Econometrica, 47, 2 (March 1979), p. 273. 11 Eliezer Yudkowsky, ‘Cognitive Biases Potentially Affecting Judgment of Global Risks’, in Nick Bostrom and Milan Cirkovic (eds.), Global Catastrophic Risks (Oxford University Press, 2008), pp. 91-119.

On the contrary, financial history is a roller-coaster ride of ups and downs, bubbles and busts, manias and panics, shocks and crashes.3 One recent study of the available data for gross domestic product and consumption since 1870 has identified 148 crises in which a country experienced a cumulative decline in GDP of at least 10 per cent and eighty-seven crises in which consumption suffered a fall of comparable magnitude, implying a probability of financial disaster of around 3.6 per cent per year.4 Even today, despite the unprecedented sophistication of our institutions and instruments, Planet Finance remains as vulnerable as ever to crises. It seems that, for all our ingenuity, we are doomed to be ‘fooled by randomness’5 and surprised by ‘black swans’.6 It may even be that we are living through the deflation of a multi-decade ‘super bubble’.7 There are three fundamental reasons for this. The first is that so much about the future - or, rather, futures, since there is never a singular future - lies in the realm of uncertainty, as opposed to calculable risk. As Frank Knight argued in 1921, ‘Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated . . .

Beveridge Report 204-5 biases 316 bill brokers 299 bill-discounting banks 53 billets d’état 139 bills, commercial 54 bills of exchange (cambium per literas) 43-4 Birmingham & Midland 56 Bismarck, Otto von 202 Black, Fisher 320-22 black box see Black-Scholes model ‘Black’ days 164 black (or grey) economic zones 275 ‘Black Mondays’: 1929: 158 1987 see financial crises black people see African-American people Black-Scholes model (black box) 320-4 Blackstone 337 ‘black swans’ 342 ‘Black Thursday’ 158 Blain, Spencer H., Jr. 256-8 Blankfein, Lloyd 1-2 Bleichroeder (Arnhold & S.) 315 Bloch, Ivan 297 Bloomfield, Arthur 305 Blunt, John 155-6 BNP Paribas 272 Bolivia 2 Bolsheviks 107 bonds and bond markets 64 benefits of 3 bond insurance companies 347 boom 332 bundled mortgages see securitization collateral for 94 compared with mortgages (spread) 241-2 compared with stock markets 124-5 cotton-backed 94-6 crises and defaults 73 definitions 65-9 emerging market bonds see emerging markets face value (par) 73 future of 115-16 government see government bonds history 65-7 importance and power of 67-9 inflation and 105 insurance companies and 198 interest rates 67 liquidity 71 and mortgage rates 68 and pensions 67 perpetual bonds 76 Right- and Left-wing critics of 89-90 Rothschilds and 80-91 and savings institutions 116 and taxes 68 vulnerability of 99 war and 69-75 widening access to 100 bonds and bond markets - cont. and First World War 297 Bonn Consensus 312 bookkeeping 44-5 Borges, Jorge Luis 111 borrowing see credit; debt Boston 266 Botticelli, Sandro 42 ‘bottomry’ 185 Brady, Nicholas 165 Brailsford, Henry Noel 298 Brazil 18. see also BRICs Bretton Woods 305-8 Bretton Woods II 334 Briand, Aristide 159 BRICs (Brazil, Russia, India, China: Big Rapidly Industrializing Countries) 284 Britain: and American Civil War 94-5 banknotes 27 banks and industrialization 48-9 business failures 349 colonies see British Empire compared with France 141 compared with Japan 209-11 cost of living 26 cotton industry 94-6 East Indies trade 134; see also East India Company economy 210-11 finances for Napoleonic wars 80-84 financial ignorance 11-12 financial sector’s contribution to GDP 5 fiscal system 75 foreign investment 287 foreign investment in 76 Glorious Revolution 75-6 house prices and property ownership 10.

 

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How to Build a Billion Dollar App: Discover the Secrets of the Most Successful Entrepreneurs of Our Time by George Berkowski

Amazon: amazon.comamazon.co.ukamazon.deamazon.fr

Airbnb, Amazon Web Services, barriers to entry, Black Swan, business intelligence, call centre, crowdsourcing, en.wikipedia.org, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, iterative process, Jeff Bezos, Jony Ive, Kickstarter, knowledge worker, Lean Startup, loose coupling, Mark Zuckerberg, minimum viable product, move fast and break things, Network effects, Oculus Rift, Paul Graham, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software as a service, software is eating the world, Steve Jobs, Steven Levy, Y Combinator

Great, disruptive entrepreneurs need to understand the capabilities of the technology available to them, the necessity of building new platforms, how to integrate virality into their products and, perhaps most importantly, the power of timing. Don’t get me wrong: there is almost always an element of luck involved (and often significant opportunity cost). But being an entrepreneur is not for the conservative. Nicholas Nassim Taleb (author of The Black Swan) would question the viability of betting on low-probability, high-impact events, what he calls black-swan events, but that is the business of entrepreneurs: manufacturing opportunities that are rare and complex and ultimately yield huge returns. So let’s take a deep dive into the key disruptions delivered by our billion-dollar apps, and expose the critical factors that you need to take into account. Why hating advertising pays more Jan Koum and Brian Acton both hate advertising.

Index Note: page numbers in bold refer to illustrations, page numbers in italics refer to information contained in tables. 99designs.com 111 500 Startups accelerator 136, 160 Accel Partners 3, 158, 261, 304, 321, 336, 383 accelerators 136, 159–60, 160 accountants 164, 316 accounting software 164 acquisition (of users) costs 148–9, 184, 236–7, 275–9, 282 and Facebook 271, 272, 273–4 for five hundred-million-dollar apps 327, 341–3 for hundred-million-dollar apps 252, 259, 266, 267–74, 275–84, 295–307 and incentive-based networks 270–1 international 295–307 for million dollar apps 136–7, 139, 140–51, 148–9, 153 and mobile social media channels 271–3, 272 and mobile user-acquisition channels 269–70 strategy 222–31 for ten-million-dollar apps 211–12, 213, 222–31, 236–7, 248–9 and traditional channels 268–9 and ‘viral’ growth 225, 278, 279–84 zero-user-acquisition cost 278 acquisitions 414–25 buying sustained growth 417–18 by non-tech corporations 418–20 initial public offerings 420–2 Waze 415–16 activation (user) 136, 137, 139, 153–4, 211–12, 213 Acton, Brian 54, 394 addiction, smartphone 30–1 Adler, Micah 269 administrators 409 AdMob 414–15 advertising 43 business model 67, 89–90 costs 140 and Facebook 271, 272, 273–4 mobile 148–9, 268–70, 272–3, 272 mobile social media 272–3, 272 mobile user-acquisition channels 269–70 outdoor 264 shunning of 42, 54–6 video ads 273 aesthetics 131 after product–market fit (APMF) 180 agencies 195–7, 264, 343 ‘agile coaches’ see scrum masters agile software development 192–3, 299, 315, 357, 377 Ahonen, Tomi 45 ‘aiming high’ 40–1 Airbnb 160, 301 alarm features 48 Albion 111 alerts 293 Alexa.com 146 Alibaba 227 ‘ALT tags’ 147 Amazon 7, 29, 131, 164, 227, 276, 366, 374–5, 401, 406 Amazon Web Services 374 American Express 347 Amobee 149 analytics 134–5, 149, 199, 205, 210, 212, 217–21, 294 and cohort analysis 287–8 Flurry 135, 149, 220 function 217–18 Google Analytics 135, 219–20, 345 limitations 284 Localytics 135, 221 and marketing 263 mistakes involving 218–19 Mixpanel.com tool 135, 217–18, 220–1, 287, 290–1, 345 Andreessen, Marc 180, 418–19 Andreessen Horowitz 72, 80, 180, 321, 383, 385, 418–19 Android (mobile operating system) 6, 23–4, 38, 415 advertising 274 audience size 119 beta testing 202 building apps for 116–22 and international apps 296 in Japan 306 scaling development and engineering 357–8 time spent on 26 and WhatsApp 55 Angel Capital Association 162 angel investors 154, 155–6, 323 AngelList 99, 131, 155, 159, 233 Angry Birds (game) 6, 42, 47, 57–8, 87, 89, 97 and application programming interface 36 delivering delight 207 design 131 funding 321 game in game 348–9 international growth 297–9 platform 117, 118 product extension 356 virality 282 annual offsites 379 annual revenue per user (ARPU) 215, 219, 232, 236 anonymity 43, 56–7 anti-poaching clauses 247 antidilution rights 245 API see application programming interface app descriptions 143 app development billion-dollar app 8, 389–425 CEO advice 406–13 getting acquired 414–25 people 395–405 process 390–1 five-hundred-million-dollar app 325–87 funding 328, 383–7 hiring staff 334–6, 337–40 killer product expansion 350–63 process 326–8 scaling 326, 330–6, 331–2 scaling marketing 341–9 scaling people 364–72, 377–9 scaling process 373–82 scaling product development 357–63 hundred-million-dollar app 251–324 international growth 295–307 process 252–4 product-market fit 255–6 retention of users 286–94 revenue engines 257–66, 275–85 user acquisition 267–74 million-dollar app 81–171 app Version 0.1 123–35 coding 133–4 design 129–33 feedback 127, 134–5 funding 152–60, 161–71, 176, 235–49 identity of the business 106–14 lean companies 115–22 metrics 136–9, 139 process 82–4 startup process 85–105 testing 126–8 user acquisition 140–51 ten-million-dollar app 173–249 growth engine 222–31, 235–49 metrics 211–21 new and improved Version 1.0 198–210 process 174–6 product–market fit 180–97 revenue engine 232–4 venture capital 235–49 app stores 22, 27–8, 33–4 see also Apple App Store; Google Play app-store optimisation (ASO) 142, 225 AppAnnie 205 Apple 19, 20, 31–2, 393 application programming interface 35–6 designers 129 Facetime app 46 iWatch 38–9 profit per employee 402–3 revenue per employee 401 visual voicemail 50 Worldwide Developers Conference (WWDC) 313 see also iPad; iPhone Apple App Store 22, 27, 32–3, 75, 88, 89, 117, 226 finding apps in 140, 141, 142–5 international apps 297–9 making submissions to 152–3 and profit per employee 403 ratings plus comments 204–5 Apple Enterprise Distribution 201–2 application programming interface (API) 35–6, 185, 360, 374 ARPU see annual revenue per user articles of incorporation 169 ASO see app-store optimisation Atari 20 Atomico 3, 261, 321, 383 attribution 227–31 for referrals 230–1 average transaction value (ATV) 214–15, 219, 232, 236, 387 Avis 95 backlinking to yourself 146 ‘bad leavers’ 247 Balsamiq.com 128 Banana Republic 352 bank accounts 164 banking 156–7 Bardin, Noam 43 Barr, Tom 338 Barra, Hugo 120, 306 Baseline Ventures 72 Baudu 226 beauty 131 BeeJiveIM 33 before product–market fit (BPMF) 180 ‘below the fold’ 143 Beluga Linguistics 297 Benchmark 75 benefits 398–400 beta testing 201–4 Betfair 358 Bezos, Jeff 366, 374 Bible apps 45 billion 9–10 Billion-Dollar Club 5 billionaires 9 Bing 226 ‘black-swan’ events 54 BlackBerry 23 Blank, Steve 257 Blogger 41 blood sugar monitoring devices 38 board seats 242, 243–4 board-member election consent 169 Bolt Peters 363 Booking.com 320 Bootstrap 145 Botha, Roelof 76, 77, 80 Box 7, 90, 276, 396–7, 411 brains 10 brainstorming 108 branding 111–13, 143, 263–4 Braun 129 Bregman, Jay xiii, 14–16, 95, 124, 209, 303 bridge loans 323 Brin, Sergey 366 Bring Your Own Infrastructure (BYOI) 17–18 Brougher, Francoise 340 Brown, Donald 44 Brown, Reggie 104–5 Bubble Witch 421 Buffet, Warren 4 build-measure-learn cycle 116 Burbn.com 72–4, 80 business advisors/coaches 103 business analysts 343 business culture 395–8 business goal setting 310–11 business models 67, 83, 87, 88–91, 175, 253, 259, 327, 351–2, 391, 400, 423–4 business success, engines of 183–4, 423–4 Business Wire 150 CAC see Customer Acquisition Cost Cagan, Marty 314 calendars 49 calorie measurement sensors 38 Cambridge Computer Scientists 160 camera feature 48 Camera+ app 48 Candy Crush Saga 6, 47, 87, 89, 131, 278–81, 318, 349, 421–2 card-readers 41–2 cash flow 164 CEOs see Chief Executive Officers CFOs see Chief Financial Officers channels incentive-based networks 270–1 mobile social-media 271–3, 272 mobile user-acquisition 269–70 source attribution 227–31 testing 224–7 traditional 268–9 viral 280–2 charging phones 49–50 Chartboost 149 chauffer hire see Uber app check-ins, location-based 72, 74 Chief Executive Officers (CEOs) 309, 380 advice from 406–13 and the long haul 68 and product centricity 185–6 role 337 Chief Financial Officers (CFOs) 316 Chief Operations Officers (COOs) 309, 326, 337–40, 380 Chief Technology Officers (CTOs) 186–7, 195 Chillingo 298 China 24–5, 146, 226, 306–7 Cisco 402 Clash of Clans (game) 6, 28, 36, 47, 87, 89, 97, 118, 227, 348–9, 398 Clements, Dave 120 Climate Corporation 412, 419 clock features 47 cloud-based software 67, 90 Clover 419 coding 133–4 cofounders 85, 91–105, 188, 191 chemistry 92–3 complementary skills 93 finding 96–9 level of control 94 passion 93–4 red flags 102–3 successful matches 104–5 testing out 100–2 cohort analysis 237, 287–8 Color.com (social photo-sharing) app 113, 255 colour schemes 111 Commodore 20 communication open 412–13 team 194 with users 208–9 Companiesmadesimple.com 163–4 computers 20–1, 29 conferences 97–8, 202, 312–13 confidentiality provisions 244 connectedness 30 ConnectU 105 consumer audience apps 233–4 content, fresh 147 contracts 165–6 convertible loans 163 Cook, Daren 112 cookies 228–9 Coors 348 COOs see Chief Operations Officers Cost Per Acquisition (CPA) 148–9 Cost Per Download (CPD) 148 Costolo, Dick 77–8, 79–80 costs, and user acquisition 148–9, 184, 236–7, 275–9, 282 Crash Bandicoot 33 crawlers 146–7 Cray-1 supercomputer 20 CRM see customer-relationship management CrunchBase 238 CTOs see Chief Technology Officers Customer Acquisition Cost (CAC) 148–9, 184, 236–7, 275–9 customer lifecycle 212–14 customer segments 346–7 customer-centric approach 344 customer-relationship management (CRM) 290–4, 343 customer-support 208–9 Cutright, Alyssa 369 daily active users (DAUs) 142 D’Angelo, Adam 75–6 data 284–5, 345–7 data engineers 284 dating, online 14, 87–8, 101–2, 263 decision making 379–82, 407–8 defining apps 31–4 delegation 407 delight, delivery 205–7 design 82, 129–33, 206–7 responsive 144 designers 132, 189–91, 363, 376 developer meetups 97 developers see engineers/developers development see app development; software development development agencies 196 ‘development sprint’ 192 Devine, Rory 358–9 Digital Sky Technologies 385 directors of finance 316–17 Distimo 205 DLD 97 Doerr, John 164, 310 Doll, Evan 42–3, 105 domain names 109–10 international 146 protection 145–6 Domainnamesoup.com 109 Dorsey, Jack 41, 58, 72, 75–7, 79–80, 104, 112, 215–16, 305, 312, 412–13 ‘double-trigger’ vesting 247 DoubleClick 414 Dow Jones VentureSource 64 down rounds 322–3 downloads, driving 150–1 drag along rights 245 Dribbble.com 132 Dropbox 7, 90, 131, 276 CEO 407, 410–11 funding 160 scaling 336 staff 399 Dunbar, Robin 364–5 Dunbar number 365 e-commerce/marketplace 28–9, 67, 89, 213–14 Chinese 306 Flipboard and 351–2 and revenue engines 232, 233–4, 276 social media generated 271–2 and user retention 288, 289 eBay 7, 28–9, 131, 180, 276 economic models 275 economies of scale 331–2, 331–2 eCourier 15, 95 education 68–9 edX 69 Ek, Daniel 357 Ellis, Sean 182 emails 291–3 emotion effects of smartphones on 29–30, 30 inspiring 223–4 employees see staff employment contracts 246–7 engagement 236, 278, 283 engineering VPs 337, 358–9 engineers/developers 190–1, 194–5, 361–2, 362, 370, 375–7, 405 enterprise 90, 233–4 Entrepreneur First programme 160 entrepreneurs 3–5, 7–8, 65, 262, 393–4, 409, 424 Ericsson 21 Etsy 107, 109, 110, 358 Euclid Analytics 149 Evernote 7, 90, 131, 399 ExactTarget 291 excitement 30 executive assistants 367 Exitround 419 experience 67–8, 264, 397 Fab.com 352 Facebook 7, 10, 26, 32, 48, 76, 226, 394, 422 and acquisition of users 271, 272, 273–4 acquisitions 416–18, 417 agile culture 375 alerts 293 and application programming interface 36 board 180 and business identity 114 and Candy Crush 280–1 Chief Executive Officer 406 cofounders 100–1 and Color 255–6 design 131, 206, 363 Developer Garage 97 driving downloads on 151 and e-commerce decisions 271, 272 and FreeMyApps.com 271 funding 419 and getting your app found 147 and the ‘hacker way’ 375 initial public offering 420–1 and Instagram 29, 51, 76–80, 90, 117 name 110 ‘No-Meeting Wednesday’ 376 product development 187 profit per employee 403 revenue per employee 401 scaling 336 and Snapchat 57 staff 339, 362, 363, 398, 401, 403 and virality 281 WhatsApp purchase 42, 54–6, 416–17, 417 zero-user-acquisition cost 278 and Zynga 279, 281 Facetime app 46 fanatical users 294 feedback 86, 127, 134–5, 182, 192–3, 198–201, 256, 396 loops 204, 211 qualitative 199 quantitative 199 see also analytics Feld, Brad 170, 241 Fenwick and West 168 Fiksu 264, 269–70 finance, VP of 317–18 finding apps 140–8, 148–9 FireEye 90 First Data 419 first impressions 107–10 Fitbit 38 fitness bracelets 38 flat rounds 322–3 Flipboard 6, 29, 42–3, 49, 51, 89–90 and application programming interface 36 Catalogs 351–2 cofounders 105 design 131, 207 funding 164 growth 351–2 platform choice 119 product innovation 351–2 user notifications 292 virality 281 zero-user-acquisition cost 278 Flurry 135, 149, 220 Fontana, Ash 233 Forbes magazine 40 Ford Motors 419 Founder Institute, The 168 founder vesting 166–7, 244 Foursquare 419 France Telecom 13 franchising 354 FreeMyApps.com 270–1 Friedberg, David 412 Froyo (Android mobile software) 7 Fujii, Kiyotaka 304 full service agencies 195–6 functionality 25–6, 45–50, 131 funding 72, 75–6, 84, 87–8, 152–60, 161–71, 179 accelerators 159–60 angel investors 154, 155–6, 323 for billion-dollar apps 391 convertible loans 163 core documents 169–70 for five-hundred-million-dollar apps 328, 383–7 founder vesting 166–7 for hundred-million-dollar apps 254, 258, 316–17, 318–24 incubators 159–60 legal aspects 163–4 and revenue engines 233–4 Series A 234, 238–40, 238, 240, 241, 242–6, 255, 319–21, 385 Series B 238, 241, 253, 260, 284, 319–21, 322, 384 Series C 384 signing a deal 167–8 for ten-million-dollar apps 152–60, 161–71, 176, 235–49 venture capital 72, 75, 156–8, 165–6, 235–49, 261–2, 383–5, 385, 418–19 game in game 348–9 gaming 42, 47, 318, 355 business model 67, 89 and revenue engines 232, 278–9 and user retention 288, 289 see also specific games Gandhi, Sameer 336 Gartner 271 Gates, Bill 4 general managers (GMs) 300–3 Gladwell, Malcolm 424 Glassdoor 361–2 Global Positioning System (GPS) 23 Gmail 72 GMs see general managers goal setting 40–1, 310–11 Goldberg, Dave 397 Goldman Sachs 385 ‘good leavers’ 247 Google 7, 19, 23, 27, 72, 88, 164, 226 acquisitions 43, 414–16, 418 application programming interface 35–6 beta testing 202 Chief Executive Officer 406–8 developer meetups 97 finding your app on 144, 147 Hangouts app 46 meetings 381–2 mission 404, 408–9 and the OKR framework 310 profit per employee 403, 405 revenue per employee 401, 405 scaling 332 and Snapchat 57 and source attribution 228–9 staff 339, 340, 361–2, 366, 401, 403, 404–5, 412 Thank God It’s Friday (TGIF) meetings 311–12 transparency 413 value 78 Waze app purchase 43 and WhatsApp 56 zero-user-acquisition cost 278 see also Android (mobile operating system) Google Ad Mob 149 Google AdSense 149 Google Analytics 135, 219–20, 345 Google Glass 38–9, 405 Google I/O conference 313 Google Maps 33, 35, 414, 416 Google Now 37 Google Play 88, 89, 117, 120, 226 and beta testing 202 finding apps in 141–5 profit per employee 403 ratings plus comments 204–5 Google Reader 72 Google Ventures 384 Google X 405 Google+ and business identity 114 and virality 281 Google.org 339 GPS see Global Positioning System Graham, Paul 184–5, 211 Graphical User Interface (GUI) 20 Greylock 321, 383 Gross, Bill 406–7, 409–10 Groupon 7, 51–2, 227, 344–5, 419 Grove, Andy 310 growth 267, 308–17 buying sustained 417–18 engines 184, 210, 222–31, 259, 265 and five-hundred-million-dollar apps 329–36 and Friday update meetings 311–12 and goal setting 310–11 and hiring staff 308–9, 411–12 and product and development teams 313–14 and staff conferences 312–13 targets 234, 260 see also acquisition (of users); international growth; scaling Growth Hackers 182 GUI see Graphical User Interface hackathons 99 Haig, Patrick 143 Hailo app xiii–xiv, 5, 36, 89, 386 big data 284–5 branding 112–13 cofounders 94–6 customer segments 346–7 customer-support 208–9 design 131, 132, 133, 206–7 development 123–7, 153–4 Friday update meetings 311 funding 162, 242 goal setting 310 growth 296–7, 299, 302–4, 308–11, 313, 315–17, 329–30, 334–6 hiring staff 308–9, 334–6, 338, 366–7 idea for 14–18 international growth 296, 297, 299, 302–4 market research 182 marketing 263, 264, 268, 270, 273, 341, 347–8 meetings 381 metrics 137–9, 216 name 107 organisational culture 396 platform choice 117, 120, 121 premises xiii–xiv, 177–8, 329–30, 371–2, 386 product development 189, 191, 196 retention 293–4 revenue engine 276 scaling development and engineering 357 scaling people 365–7 scaling process 377 team 258 testing 177–8, 201–4 and user emotionality 224 virality 280, 282 Hangouts app 46 Harris Interactive 31 HasOffers 149 Hay Day 47, 97 head of data 342 Heads Up Display (HUD) 38 heart rate measurement devices 37–8 Hed, Niklas 42 hiring staff 308–9, 334–6, 337–40, 365–70 history of apps 31–2 HMS President xiii–xiv, 177–9, 329, 371, 386 HockeyApp 202 HootSuite 151 Houston, Drew 407, 410–11 HP 180, 402 HTC smartphone 121 HUD see Heads Up Display human universals 44–5 Humedica 419 hyperlinks 147 hypertext markup language (HTML) 147 I/O conference 2013 202 IAd mobile advertising platform 149 IBM 20, 402 icons 143 ideas see ‘thinking big’ identity of the business 86 branding 111–13 identity crises 106–14 names 106–11 websites 113–14 image descriptions 147 in Mobi 149 in-app purchases 28 incentive-based networks 270–1 incorporation 163–4, 179 incubators 159–60 Index Ventures 3, 261 initial public offerings (IPOs) 64, 67–9, 78, 80, 246, 420–2 innovation 404–5 Instagram 6, 29, 48, 51, 67, 71–80, 88–90, 114, 117, 226, 278, 340, 417–18 cofounders 73–4 design 131 funding 75–6, 77–8 X-Pro II 75 zero-user-acquisition cost 278 instant messaging 46 Instantdomainsearch.com 109 integrators 410 Intel 310 intellectual property 165–6, 244, 247 international growth 295–307 Angry Birds 297–9 Hailo 296, 297, 299, 302–4 language tools 297 Square 295, 299, 304–6 strings files 296 Uber 299–302 International Space Station 13 Internet bubble 13 investment see funding iOS software (Apple operating system) 7, 23–4, 46, 75, 104 advertising 274 audience size 119 building apps for 116–22 and international apps 296 scaling development and engineering 357–8 time spent on 26 iPad 42–3, 118–20, 351 iPhone 6, 19, 22–3, 32, 38–9, 183, 351 advertising on 274 camera 48 designing apps for 117–18, 120 finding apps with 145 games 42, 47, 58 and Instagram 74–6 in Japan 306 and Square 104, 306 and Uber 301 user spend 117 and WhatsApp app 54–5 iPod 22 IPOs see initial public offerings Isaacson, Walter 32 iTunes app 22, 47, 88, 143 iTunes U app 69 Ive, Jony 129 iZettle 304 Jackson, Eric 40 Jain, Ankit 142 Japan 227, 304–6 Jawbone Up 38 Jelly Bean (Android mobile software) 7 Jobs, Steve 4, 22, 32, 323, 393, 425 journalists 150–1 Jun, Lei 306 Kalanick, Travis 299–300, 384, 422 Kayak 336 Keret, Samuel 43 Keyhole Inc. 414 keywords 143, 146 Kidd, Greg 104 King.com 349, 421–2 see also Candy Crush Saga KISSmetrics 291 KitKat (Android mobile software) 7 Klein Perkins Caulfield Byers (KPCB) 158, 261, 321, 383 Kontagent 135 Koolen, Kees 320, 339 Korea 30 Koum, Jan 42, 54, 55–6, 154, 321, 394, 416 Kreiger, Mike 73–6 language tools 297 Launchrock.com 113–14, 145, 202 Lawee, David 415 lawyers 103, 169, 170, 242 leadership 410–11 see also Chief Executive Officers; managers lean companies 69, 115–22, 154, 257, 320–1 Lee, Bob 340 legalities 163–70, 242–7, 301 letting go 406–7 Levie, Aaron 396–7, 411 Levinson, Art 32 LeWeb 97 Libin, Phil 399 licensing 356 life experience 67–8, 264 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291, 342 Line app 46, 226 Lingo24 297 LinkedIn 97, 226, 406, 408–9 links 147 liquidation preference 242, 243, 245 non-participating 245 Livio 419 loans, convertible 163 Localytics 135, 221 locations 69 logos 111–14 LTV see lifetime value luck 412 Luckey, Palmer 39 LVMH 304 Lyons, Carl 263 Maiden 95 makers 375–7 see also designers; engineers/developers managers 189–90, 300–3, 375–7, 405 MapMyFitness 419 market research 115, 127, 182 marketing data 345–7 and Facebook 271, 272, 273–4 and incentive-based networks 270–1 marketing engineering team 344–5 and mobile social media channels 271–3, 272 and mobile user-acquisition channels 269–70 partner marketing 347–8 scaling 341–9 teams 262–6, 337, 342 and traditional channels 268–9 VPs 262–6, 337, 342 marketplace see e-commerce/marketplace MasterCard 347–8 Matrix Partners 283 McClure, Dave 136, 160, 211, 234 McCue, Mike 42–3, 105, 351 McKelvey, Jim 41, 104 ‘me-too’ products 181 Medium 41 Meebo 73 meetings 379–82, 412–13 annual offsite 379 daily check-ins 381 disruptive nature 376–7 Friday update 311–12 meaningful 381–2 monthly strategic 380 quarterly 380 weekly tactical 380 Meetup.com 98–9 Mendelsen, Jason 170 messaging platforms 226 time spent on 46 and user retention 288, 289 metrics 136–9, 139, 211–21 activation 136, 137, 139, 153–4, 211–12, 213 annual revenue per user (ARPU) 215, 219, 232, 236 average transaction value (ATV) 214–15, 219, 232, 236, 387 consensual 215–16 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291, 342 and product-market fit 209–10 referral 137, 138, 139, 153, 154, 211–12, 213, 230–1 revenue 137, 138, 139, 154, 211–12, 213, 214–15, 219, 291 transparency regarding 312 see also acquisition (of users); retention (of users) mice 20 Microsoft application programming interface 35–6 revenue per employee 401 Windows 20, 22, 24 Millennial Media 149 minimum viable product (MVP) 123, 153 MirCorp 13–14 mission 261, 404, 408–9 Mitchell, Jason 51 Mitsui Sumitomo Bank 305 Mixpanel.com tool 135, 217–18, 220–1, 287, 290–1, 345 MMS see Multimedia Messaging Service Mobile Almanac 45 Mobile App Tracking 230, 231 mobile technology, rise of 19–39 MoMo app 306 Monsanto 419 moonshots 404–5 Moore, Jonathan 200 MoPub 149 Moqups.com 128 Mosaic 180 Motorola 21 Moz.com 143 Mullins, Jacob 419 Multimedia Messaging Service (MMS) 47 Murphy, Bobby 43, 104–5, 152–3 music player apps 47 MVP see Metrics into Action; minimum viable product names 106–11, 142 NameStation.com 108 Nanigans 273–4 National Venture Capital Association 64 native apps 33–4 NDA see Non Disclosure Agreement negotiation 265 Net Promoter Score (NPS) 206, 209 net-adding users 206 Netflix 400 Netscape 164, 180 New Enterprise Associates 385 New York Times news app 32–3, 256 news and alerts feature 48–9 Nextstop 72 Nguyen, Bill 255–6 NHN 227 Nike Fuelband 38 Nintendo Game Boy 47 Nokia 21, 35–6 Non Disclosure Agreement (NDA) 165 noncompetition/non-solicitation provision 244, 247 notifications 291–4 NPS see Net Promoter Score Oculus VR 39 OKR (‘objectives and key results’) framework 310–11, 380 OmniGraffle 128 open-source software 23, 34–5, 185 OpenCourseWare 68–9 operating systems 20–4 see also Android; iOS software operations VPs 337 org charts 258, 309 organisational culture 395–8 O’Tierney, Tristan 104 outsourcing 194–7 ownership and founder vesting 166–7 and funding 155, 156, 161–3, 318 oxygen saturation measurement devices 37–8 Paananen, Ilkka 118–19, 397–8 Page, Larry 4, 23, 382, 404, 407–8 Palantir 90 Palihapitiya, Chamath 187 Pandora 7, 47, 67, 131, 410 pay-before-you-download model 28 pay-per-download (PPD) 225 Payleven 304 payment systems 7, 33–4, 227, 304, 305 see also Square app PayPal 7, 227, 304, 305 Pepsi 196 Perka 419 perks 398–400 perseverance 67, 394, 410 personal computers (PCs) 29 perspiration measurement devices 38 Pet Rescue Saga 349, 421 Petrov, Alex 369 phablets 7 Pham, Peter 255 PhoneSaber 33 Photoshop 128 PIN technology 305 Pincus, Mark 311 Pinterest app 48, 226 and business identity 114 and e-commerce decisions 271, 272 and getting your app found 147 name 107 and virality 281 Pishevar, Shervin 300 pivoting 73–4 population, global 9–10 portfolio companies 261–2 PowerPoint 128 PPD see pay-per-download preferential return 243 premises 370–2 preparation 412 press kits 148, 150 press releases 150 Preuss, Dom 98 privacy issues 43, 56–7 private vehicle hire see Uber pro-rata rights 242, 243 producers 409 product chunks 360 product development scaling 357–63 scope 199 team building for 188–91 and team location 193–4 and vision 186–8, 191 see also app development; testing product expansion 350–63 product extension 354 product managers 189–90, 405 product-centricity 185–6, 314, 360 product-market fit 9, 180–97, 235–6, 248, 256–7 measurement 209–10, 212, 286–8 profit 267, 320, 342 profit margin 258–9, 318, 321 profit per employee 402–4, 403, 405 profitability 260, 277, 400 Project Loon 405 proms 12 proto.io tool 133 prototype apps 86, 174 app Version 0.1 123–35, 174 new and improved Version 1.0 198–210 rapid-design prototyping 132–3 PRWeb 150 PSP 47 psychological effects of smartphones 29–30, 30 pttrns.com 131 public-relations agencies 343 publicity 150–1, 225, 313 putting metrics into action 138–9 Puzzles and Dragons 47, 131 QlikView 221, 284–5 QQ 307 quality assurance (QA) 190–1, 196 Quora 76 QZone 307 Rabois, Keith 368, 369 Rakuten 227 Rams, Dieter 129 rapid-design prototyping 132–3 ratings plus comments 204–5 Red Bull 223 redemption codes 230 referrals (user) 137, 138, 139, 153, 154, 211–12, 213 attribution for referrals 230–1 referral codes 230 religious apps 45 remuneration 361–2, 362, 363 Renault 13 restated certification 169 retention (of users) 136–9, 153, 154 for five hundred-million-dollar apps 327, 341–3 for hundred-million-dollar apps 286–94, 288–9 measurement 286–8 for ten-million-dollar apps 206, 211–12, 213, 278 revenue 137–8, 139, 154, 211–12, 213, 214–15, 219, 236, 239–40, 267, 291, 331–2, 341–2, 354 revenue engines 184, 210, 232–4, 257–66, 265, 275–85 revenue per employee 400–2, 402, 405 revenue streams 27–9 Ries, Eric, The Lean Startup 115–16 Rockefeller, John D. 9 Rocket Internet 304 Rolando 33 Rosenberg, Jonathan 413 Rovio 58, 97, 118, 297–9, 318, 320–1, 336, 354, 409 see also Angry Birds Rowghani, Ali 77 Rubin, Andy 23 Runa 419 SaaS see software as a service Sacca, Chris 75–6 sacrifice 86–7 Safari Web browser 32 salaries 361–2, 362, 363 sales VPs 337 Salesforce 291 Samsung 23 Galaxy Gear smartwatch 38 smartphones 121 Sandberg, Sheryl 4, 100–1, 339, 397 SAP 304 scaling 259, 308, 312, 323–4, 326, 330–6, 331–2, 384–5 decision making 379–81 international growth 295–307 marketing 341–9 and organisational culture 396–8 people 338–9, 364–72 premature 334–5 process 373–82 product development and engineering 357–63 and product innovation 350–6 reasons for 333–4 skill set for 335–6 Schmidt, Eric 120 scope 199 screenshots 131, 144, 206 scrum masters (‘agile coaches’) 315, 359, 360 search functions 49 organic 141–2, 141, 145 search-engine optimisation (SEO) 142, 145–8, 225 Sedo.com 109 Seed Fund 136 Seedcamp 160 Sega Game Gear 47 segmentation 220, 287, 290, 346–7 self-empowered squads/units 360 SEO see search-engine optimisation Sequoia Capital 76, 77–80, 158, 255, 321, 383, 385 Series A funding 234, 238–40, 238, 240, 241, 242–6, 255, 261, 262, 319–21, 385 Series B funding 238, 241, 253, 260, 319–21, 322, 384 Series C funding 384 Series Seed documents 168 Sesar, Steven 263 sex, smartphone use during 31 Shabtai, Ehud 43 shares 156, 166–8, 244 ‘sharing big’ 51–2, 52 Shinar, Amir 43 Shopzilla 263 Short Message Service (SMS) 21, 46–7 Silicon Valley 71–4, 77, 79, 99, 162, 168, 180, 184, 255, 340, 361, 411, 422 Sina 227 sitemaps 146–7 skills sets complementary 93 diverse 409–10 for scaling 335–6 Skok, David 283 Skype app 7, 46, 111, 200–1, 226, 357, 419 Sleep Cycle app 48 Smartling 297 smartwatches 7, 38–9 SMS see Short Message Service Snapchat app 6, 43, 46, 56–7, 88, 89, 223, 226, 416, 418 cofounders 104–5 design 131 funding 152–3, 307, 320 name 107 platform 117 staff 340 valuations 333 virality 280, 283 zero-user-acquisition cost 278 social magazines 42–3 see also Flipboard social media 48 driving downloads through 151 and getting your app found 147 mobile channels 271–3, 272 and user retention 288, 289 Sofa 363 SoftBank 227 software development agile 192–3, 299, 315, 357, 377 outsourcing 194–5 see also app development software as a service (SaaS) 67, 90, 208, 214, 233, 276–7 Somerset House 329–30, 371 Sony 21, 47 SoundCloud 358 source attribution 227–31 space tourism 13–14 speech-to-text technology 50 speed 20 Spiegel, Evan 43, 56–7, 104–5, 152–3 Spinvox 50 Splunk 90 Spotify app 47, 357–8 SQL 284 Square app 6, 41–2, 58–9, 87, 89, 333, 350 branding 112 Chief Executive Officer 412–13 cofounders 104 design 131, 363 funding 320–1 international growth 295, 299, 304–6 marketing 348 metrics 215–16 name 107, 110 product–market fit 183 revenue engine 276 scaling people 367–8 scaling product innovation 352–3 staff 340, 367–8 transparency 312 virality 282 Square Cash 353 Square Market 353 Square Register 350, 352–3 Square Wallet 348, 350, 353 Squareup.com 144 staff at billion-dollar app scale 395–405, 423 attracting the best 91 benefits 398–400 conferences 312–13 conflict 334, 378 employee agreements 244 employee legals 246–7 employee option pool 244 employee-feedback systems 378 firing 370, 378 hiring 308–9, 334–6, 411–12 induction programmes 370 investment in 360 mistakes 369–70, 411–12 and premises 370–2 profit per employee 402–4, 403 revenue per employee 400–2, 402 reviews 370 scaling people 364–72, 377–9 scrum masters 315, 359, 360 training programmes 370 see also cofounders; specific job roles; teams Staples 419 Starbucks 338, 348 startup weekends 98 startups, technology difficulties of building 63–80 failure 63–5, 73–4 identity 106–14 lean 115–22, 154 process 82–4, 85–105 secrets of success 66–9 step sensors 38 stock markets 420–1 straplines 111 strings files 296 Stripe 160 style 111 subscriptions 90 success, engines of 183–4, 423–4 SumUp 304 Supercell 28, 47, 97, 118–19, 318, 336, 397–8, 401, 403 see also Clash of Clans; Hay Day SurveyMonkey 397 surveys 206, 209 synapses 10 Systrom, Kevin 71–80 tablets 7 Tableau Software 90 Taleb, Nicholas Nassim 54 Tamir, Diana 51 Tap Tap Revolution (game) 42 Target 419 taxation 164 taxi hailing apps see Hailo app TaxiLight 16 team builders 264 team building 188–91 teams 82, 174, 252, 390 complementary people 409–10 for five-hundred-million-dollar apps 326, 342–5, 357–63, 374, 386 growth 313–14, 326, 342–4 for hundred-million-dollar apps 258–61 located in one place 193–4 marketing 262–6, 342–4 marketing engineering 344–5 product development and engineering 357–63 ‘two-pizza’ 374 TechCrunch Disrupt 97, 99 technology conferences 97–8, 202, 312–13 Techstars 159, 160, 168 Tencent 307 Tencent QQ 226 term sheets 168, 169, 170, 243–4 testing 126–8, 177–8, 187–8, 192–3, 199–201 beta 201–4 channels 224–7 text messaging 21 unlimited packages 42 see also Short Message Service ‘thinking big’ 40–59, 82, 85 big problem solutions 41–3 disruptive ideas 53–9 human universals 44–5 sharing big 51–2, 52 smartphones uses 45–50 Thoughtworks 196 time, spent checking smartphones 25–6, 26, 45–50 Tito, Dennis 13 tone of voice 111 top-down approaches 311 traction 233, 252 traffic information apps 43 traffic trackers 146 translation 296–7 transparency 311–12, 412–13 Trilogy 13 Tumblr 110, 226, 399, 418 Twitter 41, 48, 54, 72, 226, 394 acquisitions 418 and application programming interface 36 and Bootstrap 145 and business identity 114 delivering delight 206 and e-commerce decisions 272 and FreeMyApps.com 271 funding 419, 421 and getting your app found 147 initial public offering 421 and Instagram 51, 76–7, 79–80 name 110 and virality 281 ‘two-pizza’ teams 374 Uber 6, 36, 87, 89, 333, 350 and attribution for referrals 231 design 131 funding 320, 384, 422 international growth 295, 299–302 name 107, 110 revenue engine 276 revenue per employee 401 scaling product innovation 355–6 staff 339, 399 user notifications 292 virality 280 Under Armour 419 Union Square Ventures (USV) 3, 158, 242, 261, 262, 288, 321, 323, 377, 383 unique propositions 198 UnitedHealth Group 419 URLs 110 ‘user experience’ (UX) experts 190 user journeys 127–8, 213–14 user notifications 291–4 user stories 193 users 83, 175, 252, 327, 390 activation 136, 137, 139, 153–4, 211–12, 213 annual revenue per user (ARPU) 215, 219, 232, 236 communication with 208–9 definition 137 emotional response of 223–4 fanatical 294 finding apps 140–8 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291 metrics 136–9 net-adding of 206 ratings plus comments 204–5 referrals 137, 138, 139, 153, 154, 211–12, 213, 230–1 target 83, 115, 127 wants 180–97 see also acquisition (of users); retention (of users) Usertesting.com 200–1 USV see Union Square Ventures valuations 83, 161–3, 175, 237–8, 238, 253, 318, 319, 322, 327, 333, 391 venture capital 72, 75, 156–8, 165–6, 235–49, 261–2, 383–5, 385, 418–19 Viber app 6, 46, 1341 video calls 46, 47 viral coefficient 282–4 ‘viral’ growth 225, 278, 279–84 Communication virality 281 and cycle time 283–4 incentivised virality 280–1 inherent virality 280 measurement 282–4 social-network virality 281 word-of-mouth virality 281–2 virtual reality 39 vision 261, 393–4, 408–9, 414, 415 voice calls 46–7 voice-over-Internet protocol (VOIP) 46 voicemail 50 Wall Street Journal 43, 55 warranties 246 Waze app 6, 43, 97 acquisition 415–16 design 131 name 107 zero-user-acquisition cost 278 web browsing 49 Web Summit 97 websites 113–14, 144–8 WebTranslateIt (WTI) 297 WeChat app 46, 226, 306 Weibo 48 Weiner, Jeff 408–9 Wellington Partners 4 Weskamp, Marcos 207 Westergren, Tim 410 WhatsApp 6, 42, 46, 54–6, 87, 90, 226, 394 acquisition 42, 54–6, 416, 416–17, 417 cofounders 96 design 131, 144 funding 154, 320–1 platform 117–18 valuations 333 virality 280 White, Emily 340 Williams, Evan 41, 65 Williams, Rich 344 Wilson, Fred 110, 242, 288, 323, 377 Windows (Microsoft) 20–1, 22, 24, 24 Winklevoss twins 105 wireframes 127–8 Woolley, Caspar 15–16, 95, 124, 338 WooMe.com 14, 87–8, 101–2, 263 Workday 90 world population 9–10 Worldwide Developers Conference (WWDC) 313 wowing people 8–9 WTI see WebTranslateIt Xiaomi 306 Y Combinator 159–60, 184–5, 211, 407, 410–11 Yahoo!

Index Note: page numbers in bold refer to illustrations, page numbers in italics refer to information contained in tables. 99designs.com 111 500 Startups accelerator 136, 160 Accel Partners 3, 158, 261, 304, 321, 336, 383 accelerators 136, 159–60, 160 accountants 164, 316 accounting software 164 acquisition (of users) costs 148–9, 184, 236–7, 275–9, 282 and Facebook 271, 272, 273–4 for five hundred-million-dollar apps 327, 341–3 for hundred-million-dollar apps 252, 259, 266, 267–74, 275–84, 295–307 and incentive-based networks 270–1 international 295–307 for million dollar apps 136–7, 139, 140–51, 148–9, 153 and mobile social media channels 271–3, 272 and mobile user-acquisition channels 269–70 strategy 222–31 for ten-million-dollar apps 211–12, 213, 222–31, 236–7, 248–9 and traditional channels 268–9 and ‘viral’ growth 225, 278, 279–84 zero-user-acquisition cost 278 acquisitions 414–25 buying sustained growth 417–18 by non-tech corporations 418–20 initial public offerings 420–2 Waze 415–16 activation (user) 136, 137, 139, 153–4, 211–12, 213 Acton, Brian 54, 394 addiction, smartphone 30–1 Adler, Micah 269 administrators 409 AdMob 414–15 advertising 43 business model 67, 89–90 costs 140 and Facebook 271, 272, 273–4 mobile 148–9, 268–70, 272–3, 272 mobile social media 272–3, 272 mobile user-acquisition channels 269–70 outdoor 264 shunning of 42, 54–6 video ads 273 aesthetics 131 after product–market fit (APMF) 180 agencies 195–7, 264, 343 ‘agile coaches’ see scrum masters agile software development 192–3, 299, 315, 357, 377 Ahonen, Tomi 45 ‘aiming high’ 40–1 Airbnb 160, 301 alarm features 48 Albion 111 alerts 293 Alexa.com 146 Alibaba 227 ‘ALT tags’ 147 Amazon 7, 29, 131, 164, 227, 276, 366, 374–5, 401, 406 Amazon Web Services 374 American Express 347 Amobee 149 analytics 134–5, 149, 199, 205, 210, 212, 217–21, 294 and cohort analysis 287–8 Flurry 135, 149, 220 function 217–18 Google Analytics 135, 219–20, 345 limitations 284 Localytics 135, 221 and marketing 263 mistakes involving 218–19 Mixpanel.com tool 135, 217–18, 220–1, 287, 290–1, 345 Andreessen, Marc 180, 418–19 Andreessen Horowitz 72, 80, 180, 321, 383, 385, 418–19 Android (mobile operating system) 6, 23–4, 38, 415 advertising 274 audience size 119 beta testing 202 building apps for 116–22 and international apps 296 in Japan 306 scaling development and engineering 357–8 time spent on 26 and WhatsApp 55 Angel Capital Association 162 angel investors 154, 155–6, 323 AngelList 99, 131, 155, 159, 233 Angry Birds (game) 6, 42, 47, 57–8, 87, 89, 97 and application programming interface 36 delivering delight 207 design 131 funding 321 game in game 348–9 international growth 297–9 platform 117, 118 product extension 356 virality 282 annual offsites 379 annual revenue per user (ARPU) 215, 219, 232, 236 anonymity 43, 56–7 anti-poaching clauses 247 antidilution rights 245 API see application programming interface app descriptions 143 app development billion-dollar app 8, 389–425 CEO advice 406–13 getting acquired 414–25 people 395–405 process 390–1 five-hundred-million-dollar app 325–87 funding 328, 383–7 hiring staff 334–6, 337–40 killer product expansion 350–63 process 326–8 scaling 326, 330–6, 331–2 scaling marketing 341–9 scaling people 364–72, 377–9 scaling process 373–82 scaling product development 357–63 hundred-million-dollar app 251–324 international growth 295–307 process 252–4 product-market fit 255–6 retention of users 286–94 revenue engines 257–66, 275–85 user acquisition 267–74 million-dollar app 81–171 app Version 0.1 123–35 coding 133–4 design 129–33 feedback 127, 134–5 funding 152–60, 161–71, 176, 235–49 identity of the business 106–14 lean companies 115–22 metrics 136–9, 139 process 82–4 startup process 85–105 testing 126–8 user acquisition 140–51 ten-million-dollar app 173–249 growth engine 222–31, 235–49 metrics 211–21 new and improved Version 1.0 198–210 process 174–6 product–market fit 180–97 revenue engine 232–4 venture capital 235–49 app stores 22, 27–8, 33–4 see also Apple App Store; Google Play app-store optimisation (ASO) 142, 225 AppAnnie 205 Apple 19, 20, 31–2, 393 application programming interface 35–6 designers 129 Facetime app 46 iWatch 38–9 profit per employee 402–3 revenue per employee 401 visual voicemail 50 Worldwide Developers Conference (WWDC) 313 see also iPad; iPhone Apple App Store 22, 27, 32–3, 75, 88, 89, 117, 226 finding apps in 140, 141, 142–5 international apps 297–9 making submissions to 152–3 and profit per employee 403 ratings plus comments 204–5 Apple Enterprise Distribution 201–2 application programming interface (API) 35–6, 185, 360, 374 ARPU see annual revenue per user articles of incorporation 169 ASO see app-store optimisation Atari 20 Atomico 3, 261, 321, 383 attribution 227–31 for referrals 230–1 average transaction value (ATV) 214–15, 219, 232, 236, 387 Avis 95 backlinking to yourself 146 ‘bad leavers’ 247 Balsamiq.com 128 Banana Republic 352 bank accounts 164 banking 156–7 Bardin, Noam 43 Barr, Tom 338 Barra, Hugo 120, 306 Baseline Ventures 72 Baudu 226 beauty 131 BeeJiveIM 33 before product–market fit (BPMF) 180 ‘below the fold’ 143 Beluga Linguistics 297 Benchmark 75 benefits 398–400 beta testing 201–4 Betfair 358 Bezos, Jeff 366, 374 Bible apps 45 billion 9–10 Billion-Dollar Club 5 billionaires 9 Bing 226 ‘black-swan’ events 54 BlackBerry 23 Blank, Steve 257 Blogger 41 blood sugar monitoring devices 38 board seats 242, 243–4 board-member election consent 169 Bolt Peters 363 Booking.com 320 Bootstrap 145 Botha, Roelof 76, 77, 80 Box 7, 90, 276, 396–7, 411 brains 10 brainstorming 108 branding 111–13, 143, 263–4 Braun 129 Bregman, Jay xiii, 14–16, 95, 124, 209, 303 bridge loans 323 Brin, Sergey 366 Bring Your Own Infrastructure (BYOI) 17–18 Brougher, Francoise 340 Brown, Donald 44 Brown, Reggie 104–5 Bubble Witch 421 Buffet, Warren 4 build-measure-learn cycle 116 Burbn.com 72–4, 80 business advisors/coaches 103 business analysts 343 business culture 395–8 business goal setting 310–11 business models 67, 83, 87, 88–91, 175, 253, 259, 327, 351–2, 391, 400, 423–4 business success, engines of 183–4, 423–4 Business Wire 150 CAC see Customer Acquisition Cost Cagan, Marty 314 calendars 49 calorie measurement sensors 38 Cambridge Computer Scientists 160 camera feature 48 Camera+ app 48 Candy Crush Saga 6, 47, 87, 89, 131, 278–81, 318, 349, 421–2 card-readers 41–2 cash flow 164 CEOs see Chief Executive Officers CFOs see Chief Financial Officers channels incentive-based networks 270–1 mobile social-media 271–3, 272 mobile user-acquisition 269–70 source attribution 227–31 testing 224–7 traditional 268–9 viral 280–2 charging phones 49–50 Chartboost 149 chauffer hire see Uber app check-ins, location-based 72, 74 Chief Executive Officers (CEOs) 309, 380 advice from 406–13 and the long haul 68 and product centricity 185–6 role 337 Chief Financial Officers (CFOs) 316 Chief Operations Officers (COOs) 309, 326, 337–40, 380 Chief Technology Officers (CTOs) 186–7, 195 Chillingo 298 China 24–5, 146, 226, 306–7 Cisco 402 Clash of Clans (game) 6, 28, 36, 47, 87, 89, 97, 118, 227, 348–9, 398 Clements, Dave 120 Climate Corporation 412, 419 clock features 47 cloud-based software 67, 90 Clover 419 coding 133–4 cofounders 85, 91–105, 188, 191 chemistry 92–3 complementary skills 93 finding 96–9 level of control 94 passion 93–4 red flags 102–3 successful matches 104–5 testing out 100–2 cohort analysis 237, 287–8 Color.com (social photo-sharing) app 113, 255 colour schemes 111 Commodore 20 communication open 412–13 team 194 with users 208–9 Companiesmadesimple.com 163–4 computers 20–1, 29 conferences 97–8, 202, 312–13 confidentiality provisions 244 connectedness 30 ConnectU 105 consumer audience apps 233–4 content, fresh 147 contracts 165–6 convertible loans 163 Cook, Daren 112 cookies 228–9 Coors 348 COOs see Chief Operations Officers Cost Per Acquisition (CPA) 148–9 Cost Per Download (CPD) 148 Costolo, Dick 77–8, 79–80 costs, and user acquisition 148–9, 184, 236–7, 275–9, 282 Crash Bandicoot 33 crawlers 146–7 Cray-1 supercomputer 20 CRM see customer-relationship management CrunchBase 238 CTOs see Chief Technology Officers Customer Acquisition Cost (CAC) 148–9, 184, 236–7, 275–9 customer lifecycle 212–14 customer segments 346–7 customer-centric approach 344 customer-relationship management (CRM) 290–4, 343 customer-support 208–9 Cutright, Alyssa 369 daily active users (DAUs) 142 D’Angelo, Adam 75–6 data 284–5, 345–7 data engineers 284 dating, online 14, 87–8, 101–2, 263 decision making 379–82, 407–8 defining apps 31–4 delegation 407 delight, delivery 205–7 design 82, 129–33, 206–7 responsive 144 designers 132, 189–91, 363, 376 developer meetups 97 developers see engineers/developers development see app development; software development development agencies 196 ‘development sprint’ 192 Devine, Rory 358–9 Digital Sky Technologies 385 directors of finance 316–17 Distimo 205 DLD 97 Doerr, John 164, 310 Doll, Evan 42–3, 105 domain names 109–10 international 146 protection 145–6 Domainnamesoup.com 109 Dorsey, Jack 41, 58, 72, 75–7, 79–80, 104, 112, 215–16, 305, 312, 412–13 ‘double-trigger’ vesting 247 DoubleClick 414 Dow Jones VentureSource 64 down rounds 322–3 downloads, driving 150–1 drag along rights 245 Dribbble.com 132 Dropbox 7, 90, 131, 276 CEO 407, 410–11 funding 160 scaling 336 staff 399 Dunbar, Robin 364–5 Dunbar number 365 e-commerce/marketplace 28–9, 67, 89, 213–14 Chinese 306 Flipboard and 351–2 and revenue engines 232, 233–4, 276 social media generated 271–2 and user retention 288, 289 eBay 7, 28–9, 131, 180, 276 economic models 275 economies of scale 331–2, 331–2 eCourier 15, 95 education 68–9 edX 69 Ek, Daniel 357 Ellis, Sean 182 emails 291–3 emotion effects of smartphones on 29–30, 30 inspiring 223–4 employees see staff employment contracts 246–7 engagement 236, 278, 283 engineering VPs 337, 358–9 engineers/developers 190–1, 194–5, 361–2, 362, 370, 375–7, 405 enterprise 90, 233–4 Entrepreneur First programme 160 entrepreneurs 3–5, 7–8, 65, 262, 393–4, 409, 424 Ericsson 21 Etsy 107, 109, 110, 358 Euclid Analytics 149 Evernote 7, 90, 131, 399 ExactTarget 291 excitement 30 executive assistants 367 Exitround 419 experience 67–8, 264, 397 Fab.com 352 Facebook 7, 10, 26, 32, 48, 76, 226, 394, 422 and acquisition of users 271, 272, 273–4 acquisitions 416–18, 417 agile culture 375 alerts 293 and application programming interface 36 board 180 and business identity 114 and Candy Crush 280–1 Chief Executive Officer 406 cofounders 100–1 and Color 255–6 design 131, 206, 363 Developer Garage 97 driving downloads on 151 and e-commerce decisions 271, 272 and FreeMyApps.com 271 funding 419 and getting your app found 147 and the ‘hacker way’ 375 initial public offering 420–1 and Instagram 29, 51, 76–80, 90, 117 name 110 ‘No-Meeting Wednesday’ 376 product development 187 profit per employee 403 revenue per employee 401 scaling 336 and Snapchat 57 staff 339, 362, 363, 398, 401, 403 and virality 281 WhatsApp purchase 42, 54–6, 416–17, 417 zero-user-acquisition cost 278 and Zynga 279, 281 Facetime app 46 fanatical users 294 feedback 86, 127, 134–5, 182, 192–3, 198–201, 256, 396 loops 204, 211 qualitative 199 quantitative 199 see also analytics Feld, Brad 170, 241 Fenwick and West 168 Fiksu 264, 269–70 finance, VP of 317–18 finding apps 140–8, 148–9 FireEye 90 First Data 419 first impressions 107–10 Fitbit 38 fitness bracelets 38 flat rounds 322–3 Flipboard 6, 29, 42–3, 49, 51, 89–90 and application programming interface 36 Catalogs 351–2 cofounders 105 design 131, 207 funding 164 growth 351–2 platform choice 119 product innovation 351–2 user notifications 292 virality 281 zero-user-acquisition cost 278 Flurry 135, 149, 220 Fontana, Ash 233 Forbes magazine 40 Ford Motors 419 Founder Institute, The 168 founder vesting 166–7, 244 Foursquare 419 France Telecom 13 franchising 354 FreeMyApps.com 270–1 Friedberg, David 412 Froyo (Android mobile software) 7 Fujii, Kiyotaka 304 full service agencies 195–6 functionality 25–6, 45–50, 131 funding 72, 75–6, 84, 87–8, 152–60, 161–71, 179 accelerators 159–60 angel investors 154, 155–6, 323 for billion-dollar apps 391 convertible loans 163 core documents 169–70 for five-hundred-million-dollar apps 328, 383–7 founder vesting 166–7 for hundred-million-dollar apps 254, 258, 316–17, 318–24 incubators 159–60 legal aspects 163–4 and revenue engines 233–4 Series A 234, 238–40, 238, 240, 241, 242–6, 255, 319–21, 385 Series B 238, 241, 253, 260, 284, 319–21, 322, 384 Series C 384 signing a deal 167–8 for ten-million-dollar apps 152–60, 161–71, 176, 235–49 venture capital 72, 75, 156–8, 165–6, 235–49, 261–2, 383–5, 385, 418–19 game in game 348–9 gaming 42, 47, 318, 355 business model 67, 89 and revenue engines 232, 278–9 and user retention 288, 289 see also specific games Gandhi, Sameer 336 Gartner 271 Gates, Bill 4 general managers (GMs) 300–3 Gladwell, Malcolm 424 Glassdoor 361–2 Global Positioning System (GPS) 23 Gmail 72 GMs see general managers goal setting 40–1, 310–11 Goldberg, Dave 397 Goldman Sachs 385 ‘good leavers’ 247 Google 7, 19, 23, 27, 72, 88, 164, 226 acquisitions 43, 414–16, 418 application programming interface 35–6 beta testing 202 Chief Executive Officer 406–8 developer meetups 97 finding your app on 144, 147 Hangouts app 46 meetings 381–2 mission 404, 408–9 and the OKR framework 310 profit per employee 403, 405 revenue per employee 401, 405 scaling 332 and Snapchat 57 and source attribution 228–9 staff 339, 340, 361–2, 366, 401, 403, 404–5, 412 Thank God It’s Friday (TGIF) meetings 311–12 transparency 413 value 78 Waze app purchase 43 and WhatsApp 56 zero-user-acquisition cost 278 see also Android (mobile operating system) Google Ad Mob 149 Google AdSense 149 Google Analytics 135, 219–20, 345 Google Glass 38–9, 405 Google I/O conference 313 Google Maps 33, 35, 414, 416 Google Now 37 Google Play 88, 89, 117, 120, 226 and beta testing 202 finding apps in 141–5 profit per employee 403 ratings plus comments 204–5 Google Reader 72 Google Ventures 384 Google X 405 Google+ and business identity 114 and virality 281 Google.org 339 GPS see Global Positioning System Graham, Paul 184–5, 211 Graphical User Interface (GUI) 20 Greylock 321, 383 Gross, Bill 406–7, 409–10 Groupon 7, 51–2, 227, 344–5, 419 Grove, Andy 310 growth 267, 308–17 buying sustained 417–18 engines 184, 210, 222–31, 259, 265 and five-hundred-million-dollar apps 329–36 and Friday update meetings 311–12 and goal setting 310–11 and hiring staff 308–9, 411–12 and product and development teams 313–14 and staff conferences 312–13 targets 234, 260 see also acquisition (of users); international growth; scaling Growth Hackers 182 GUI see Graphical User Interface hackathons 99 Haig, Patrick 143 Hailo app xiii–xiv, 5, 36, 89, 386 big data 284–5 branding 112–13 cofounders 94–6 customer segments 346–7 customer-support 208–9 design 131, 132, 133, 206–7 development 123–7, 153–4 Friday update meetings 311 funding 162, 242 goal setting 310 growth 296–7, 299, 302–4, 308–11, 313, 315–17, 329–30, 334–6 hiring staff 308–9, 334–6, 338, 366–7 idea for 14–18 international growth 296, 297, 299, 302–4 market research 182 marketing 263, 264, 268, 270, 273, 341, 347–8 meetings 381 metrics 137–9, 216 name 107 organisational culture 396 platform choice 117, 120, 121 premises xiii–xiv, 177–8, 329–30, 371–2, 386 product development 189, 191, 196 retention 293–4 revenue engine 276 scaling development and engineering 357 scaling people 365–7 scaling process 377 team 258 testing 177–8, 201–4 and user emotionality 224 virality 280, 282 Hangouts app 46 Harris Interactive 31 HasOffers 149 Hay Day 47, 97 head of data 342 Heads Up Display (HUD) 38 heart rate measurement devices 37–8 Hed, Niklas 42 hiring staff 308–9, 334–6, 337–40, 365–70 history of apps 31–2 HMS President xiii–xiv, 177–9, 329, 371, 386 HockeyApp 202 HootSuite 151 Houston, Drew 407, 410–11 HP 180, 402 HTC smartphone 121 HUD see Heads Up Display human universals 44–5 Humedica 419 hyperlinks 147 hypertext markup language (HTML) 147 I/O conference 2013 202 IAd mobile advertising platform 149 IBM 20, 402 icons 143 ideas see ‘thinking big’ identity of the business 86 branding 111–13 identity crises 106–14 names 106–11 websites 113–14 image descriptions 147 in Mobi 149 in-app purchases 28 incentive-based networks 270–1 incorporation 163–4, 179 incubators 159–60 Index Ventures 3, 261 initial public offerings (IPOs) 64, 67–9, 78, 80, 246, 420–2 innovation 404–5 Instagram 6, 29, 48, 51, 67, 71–80, 88–90, 114, 117, 226, 278, 340, 417–18 cofounders 73–4 design 131 funding 75–6, 77–8 X-Pro II 75 zero-user-acquisition cost 278 instant messaging 46 Instantdomainsearch.com 109 integrators 410 Intel 310 intellectual property 165–6, 244, 247 international growth 295–307 Angry Birds 297–9 Hailo 296, 297, 299, 302–4 language tools 297 Square 295, 299, 304–6 strings files 296 Uber 299–302 International Space Station 13 Internet bubble 13 investment see funding iOS software (Apple operating system) 7, 23–4, 46, 75, 104 advertising 274 audience size 119 building apps for 116–22 and international apps 296 scaling development and engineering 357–8 time spent on 26 iPad 42–3, 118–20, 351 iPhone 6, 19, 22–3, 32, 38–9, 183, 351 advertising on 274 camera 48 designing apps for 117–18, 120 finding apps with 145 games 42, 47, 58 and Instagram 74–6 in Japan 306 and Square 104, 306 and Uber 301 user spend 117 and WhatsApp app 54–5 iPod 22 IPOs see initial public offerings Isaacson, Walter 32 iTunes app 22, 47, 88, 143 iTunes U app 69 Ive, Jony 129 iZettle 304 Jackson, Eric 40 Jain, Ankit 142 Japan 227, 304–6 Jawbone Up 38 Jelly Bean (Android mobile software) 7 Jobs, Steve 4, 22, 32, 323, 393, 425 journalists 150–1 Jun, Lei 306 Kalanick, Travis 299–300, 384, 422 Kayak 336 Keret, Samuel 43 Keyhole Inc. 414 keywords 143, 146 Kidd, Greg 104 King.com 349, 421–2 see also Candy Crush Saga KISSmetrics 291 KitKat (Android mobile software) 7 Klein Perkins Caulfield Byers (KPCB) 158, 261, 321, 383 Kontagent 135 Koolen, Kees 320, 339 Korea 30 Koum, Jan 42, 54, 55–6, 154, 321, 394, 416 Kreiger, Mike 73–6 language tools 297 Launchrock.com 113–14, 145, 202 Lawee, David 415 lawyers 103, 169, 170, 242 leadership 410–11 see also Chief Executive Officers; managers lean companies 69, 115–22, 154, 257, 320–1 Lee, Bob 340 legalities 163–70, 242–7, 301 letting go 406–7 Levie, Aaron 396–7, 411 Levinson, Art 32 LeWeb 97 Libin, Phil 399 licensing 356 life experience 67–8, 264 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291, 342 Line app 46, 226 Lingo24 297 LinkedIn 97, 226, 406, 408–9 links 147 liquidation preference 242, 243, 245 non-participating 245 Livio 419 loans, convertible 163 Localytics 135, 221 locations 69 logos 111–14 LTV see lifetime value luck 412 Luckey, Palmer 39 LVMH 304 Lyons, Carl 263 Maiden 95 makers 375–7 see also designers; engineers/developers managers 189–90, 300–3, 375–7, 405 MapMyFitness 419 market research 115, 127, 182 marketing data 345–7 and Facebook 271, 272, 273–4 and incentive-based networks 270–1 marketing engineering team 344–5 and mobile social media channels 271–3, 272 and mobile user-acquisition channels 269–70 partner marketing 347–8 scaling 341–9 teams 262–6, 337, 342 and traditional channels 268–9 VPs 262–6, 337, 342 marketplace see e-commerce/marketplace MasterCard 347–8 Matrix Partners 283 McClure, Dave 136, 160, 211, 234 McCue, Mike 42–3, 105, 351 McKelvey, Jim 41, 104 ‘me-too’ products 181 Medium 41 Meebo 73 meetings 379–82, 412–13 annual offsite 379 daily check-ins 381 disruptive nature 376–7 Friday update 311–12 meaningful 381–2 monthly strategic 380 quarterly 380 weekly tactical 380 Meetup.com 98–9 Mendelsen, Jason 170 messaging platforms 226 time spent on 46 and user retention 288, 289 metrics 136–9, 139, 211–21 activation 136, 137, 139, 153–4, 211–12, 213 annual revenue per user (ARPU) 215, 219, 232, 236 average transaction value (ATV) 214–15, 219, 232, 236, 387 consensual 215–16 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291, 342 and product-market fit 209–10 referral 137, 138, 139, 153, 154, 211–12, 213, 230–1 revenue 137, 138, 139, 154, 211–12, 213, 214–15, 219, 291 transparency regarding 312 see also acquisition (of users); retention (of users) mice 20 Microsoft application programming interface 35–6 revenue per employee 401 Windows 20, 22, 24 Millennial Media 149 minimum viable product (MVP) 123, 153 MirCorp 13–14 mission 261, 404, 408–9 Mitchell, Jason 51 Mitsui Sumitomo Bank 305 Mixpanel.com tool 135, 217–18, 220–1, 287, 290–1, 345 MMS see Multimedia Messaging Service Mobile Almanac 45 Mobile App Tracking 230, 231 mobile technology, rise of 19–39 MoMo app 306 Monsanto 419 moonshots 404–5 Moore, Jonathan 200 MoPub 149 Moqups.com 128 Mosaic 180 Motorola 21 Moz.com 143 Mullins, Jacob 419 Multimedia Messaging Service (MMS) 47 Murphy, Bobby 43, 104–5, 152–3 music player apps 47 MVP see Metrics into Action; minimum viable product names 106–11, 142 NameStation.com 108 Nanigans 273–4 National Venture Capital Association 64 native apps 33–4 NDA see Non Disclosure Agreement negotiation 265 Net Promoter Score (NPS) 206, 209 net-adding users 206 Netflix 400 Netscape 164, 180 New Enterprise Associates 385 New York Times news app 32–3, 256 news and alerts feature 48–9 Nextstop 72 Nguyen, Bill 255–6 NHN 227 Nike Fuelband 38 Nintendo Game Boy 47 Nokia 21, 35–6 Non Disclosure Agreement (NDA) 165 noncompetition/non-solicitation provision 244, 247 notifications 291–4 NPS see Net Promoter Score Oculus VR 39 OKR (‘objectives and key results’) framework 310–11, 380 OmniGraffle 128 open-source software 23, 34–5, 185 OpenCourseWare 68–9 operating systems 20–4 see also Android; iOS software operations VPs 337 org charts 258, 309 organisational culture 395–8 O’Tierney, Tristan 104 outsourcing 194–7 ownership and founder vesting 166–7 and funding 155, 156, 161–3, 318 oxygen saturation measurement devices 37–8 Paananen, Ilkka 118–19, 397–8 Page, Larry 4, 23, 382, 404, 407–8 Palantir 90 Palihapitiya, Chamath 187 Pandora 7, 47, 67, 131, 410 pay-before-you-download model 28 pay-per-download (PPD) 225 Payleven 304 payment systems 7, 33–4, 227, 304, 305 see also Square app PayPal 7, 227, 304, 305 Pepsi 196 Perka 419 perks 398–400 perseverance 67, 394, 410 personal computers (PCs) 29 perspiration measurement devices 38 Pet Rescue Saga 349, 421 Petrov, Alex 369 phablets 7 Pham, Peter 255 PhoneSaber 33 Photoshop 128 PIN technology 305 Pincus, Mark 311 Pinterest app 48, 226 and business identity 114 and e-commerce decisions 271, 272 and getting your app found 147 name 107 and virality 281 Pishevar, Shervin 300 pivoting 73–4 population, global 9–10 portfolio companies 261–2 PowerPoint 128 PPD see pay-per-download preferential return 243 premises 370–2 preparation 412 press kits 148, 150 press releases 150 Preuss, Dom 98 privacy issues 43, 56–7 private vehicle hire see Uber pro-rata rights 242, 243 producers 409 product chunks 360 product development scaling 357–63 scope 199 team building for 188–91 and team location 193–4 and vision 186–8, 191 see also app development; testing product expansion 350–63 product extension 354 product managers 189–90, 405 product-centricity 185–6, 314, 360 product-market fit 9, 180–97, 235–6, 248, 256–7 measurement 209–10, 212, 286–8 profit 267, 320, 342 profit margin 258–9, 318, 321 profit per employee 402–4, 403, 405 profitability 260, 277, 400 Project Loon 405 proms 12 proto.io tool 133 prototype apps 86, 174 app Version 0.1 123–35, 174 new and improved Version 1.0 198–210 rapid-design prototyping 132–3 PRWeb 150 PSP 47 psychological effects of smartphones 29–30, 30 pttrns.com 131 public-relations agencies 343 publicity 150–1, 225, 313 putting metrics into action 138–9 Puzzles and Dragons 47, 131 QlikView 221, 284–5 QQ 307 quality assurance (QA) 190–1, 196 Quora 76 QZone 307 Rabois, Keith 368, 369 Rakuten 227 Rams, Dieter 129 rapid-design prototyping 132–3 ratings plus comments 204–5 Red Bull 223 redemption codes 230 referrals (user) 137, 138, 139, 153, 154, 211–12, 213 attribution for referrals 230–1 referral codes 230 religious apps 45 remuneration 361–2, 362, 363 Renault 13 restated certification 169 retention (of users) 136–9, 153, 154 for five hundred-million-dollar apps 327, 341–3 for hundred-million-dollar apps 286–94, 288–9 measurement 286–8 for ten-million-dollar apps 206, 211–12, 213, 278 revenue 137–8, 139, 154, 211–12, 213, 214–15, 219, 236, 239–40, 267, 291, 331–2, 341–2, 354 revenue engines 184, 210, 232–4, 257–66, 265, 275–85 revenue per employee 400–2, 402, 405 revenue streams 27–9 Ries, Eric, The Lean Startup 115–16 Rockefeller, John D. 9 Rocket Internet 304 Rolando 33 Rosenberg, Jonathan 413 Rovio 58, 97, 118, 297–9, 318, 320–1, 336, 354, 409 see also Angry Birds Rowghani, Ali 77 Rubin, Andy 23 Runa 419 SaaS see software as a service Sacca, Chris 75–6 sacrifice 86–7 Safari Web browser 32 salaries 361–2, 362, 363 sales VPs 337 Salesforce 291 Samsung 23 Galaxy Gear smartwatch 38 smartphones 121 Sandberg, Sheryl 4, 100–1, 339, 397 SAP 304 scaling 259, 308, 312, 323–4, 326, 330–6, 331–2, 384–5 decision making 379–81 international growth 295–307 marketing 341–9 and organisational culture 396–8 people 338–9, 364–72 premature 334–5 process 373–82 product development and engineering 357–63 and product innovation 350–6 reasons for 333–4 skill set for 335–6 Schmidt, Eric 120 scope 199 screenshots 131, 144, 206 scrum masters (‘agile coaches’) 315, 359, 360 search functions 49 organic 141–2, 141, 145 search-engine optimisation (SEO) 142, 145–8, 225 Sedo.com 109 Seed Fund 136 Seedcamp 160 Sega Game Gear 47 segmentation 220, 287, 290, 346–7 self-empowered squads/units 360 SEO see search-engine optimisation Sequoia Capital 76, 77–80, 158, 255, 321, 383, 385 Series A funding 234, 238–40, 238, 240, 241, 242–6, 255, 261, 262, 319–21, 385 Series B funding 238, 241, 253, 260, 319–21, 322, 384 Series C funding 384 Series Seed documents 168 Sesar, Steven 263 sex, smartphone use during 31 Shabtai, Ehud 43 shares 156, 166–8, 244 ‘sharing big’ 51–2, 52 Shinar, Amir 43 Shopzilla 263 Short Message Service (SMS) 21, 46–7 Silicon Valley 71–4, 77, 79, 99, 162, 168, 180, 184, 255, 340, 361, 411, 422 Sina 227 sitemaps 146–7 skills sets complementary 93 diverse 409–10 for scaling 335–6 Skok, David 283 Skype app 7, 46, 111, 200–1, 226, 357, 419 Sleep Cycle app 48 Smartling 297 smartwatches 7, 38–9 SMS see Short Message Service Snapchat app 6, 43, 46, 56–7, 88, 89, 223, 226, 416, 418 cofounders 104–5 design 131 funding 152–3, 307, 320 name 107 platform 117 staff 340 valuations 333 virality 280, 283 zero-user-acquisition cost 278 social magazines 42–3 see also Flipboard social media 48 driving downloads through 151 and getting your app found 147 mobile channels 271–3, 272 and user retention 288, 289 Sofa 363 SoftBank 227 software development agile 192–3, 299, 315, 357, 377 outsourcing 194–5 see also app development software as a service (SaaS) 67, 90, 208, 214, 233, 276–7 Somerset House 329–30, 371 Sony 21, 47 SoundCloud 358 source attribution 227–31 space tourism 13–14 speech-to-text technology 50 speed 20 Spiegel, Evan 43, 56–7, 104–5, 152–3 Spinvox 50 Splunk 90 Spotify app 47, 357–8 SQL 284 Square app 6, 41–2, 58–9, 87, 89, 333, 350 branding 112 Chief Executive Officer 412–13 cofounders 104 design 131, 363 funding 320–1 international growth 295, 299, 304–6 marketing 348 metrics 215–16 name 107, 110 product–market fit 183 revenue engine 276 scaling people 367–8 scaling product innovation 352–3 staff 340, 367–8 transparency 312 virality 282 Square Cash 353 Square Market 353 Square Register 350, 352–3 Square Wallet 348, 350, 353 Squareup.com 144 staff at billion-dollar app scale 395–405, 423 attracting the best 91 benefits 398–400 conferences 312–13 conflict 334, 378 employee agreements 244 employee legals 246–7 employee option pool 244 employee-feedback systems 378 firing 370, 378 hiring 308–9, 334–6, 411–12 induction programmes 370 investment in 360 mistakes 369–70, 411–12 and premises 370–2 profit per employee 402–4, 403 revenue per employee 400–2, 402 reviews 370 scaling people 364–72, 377–9 scrum masters 315, 359, 360 training programmes 370 see also cofounders; specific job roles; teams Staples 419 Starbucks 338, 348 startup weekends 98 startups, technology difficulties of building 63–80 failure 63–5, 73–4 identity 106–14 lean 115–22, 154 process 82–4, 85–105 secrets of success 66–9 step sensors 38 stock markets 420–1 straplines 111 strings files 296 Stripe 160 style 111 subscriptions 90 success, engines of 183–4, 423–4 SumUp 304 Supercell 28, 47, 97, 118–19, 318, 336, 397–8, 401, 403 see also Clash of Clans; Hay Day SurveyMonkey 397 surveys 206, 209 synapses 10 Systrom, Kevin 71–80 tablets 7 Tableau Software 90 Taleb, Nicholas Nassim 54 Tamir, Diana 51 Tap Tap Revolution (game) 42 Target 419 taxation 164 taxi hailing apps see Hailo app TaxiLight 16 team builders 264 team building 188–91 teams 82, 174, 252, 390 complementary people 409–10 for five-hundred-million-dollar apps 326, 342–5, 357–63, 374, 386 growth 313–14, 326, 342–4 for hundred-million-dollar apps 258–61 located in one place 193–4 marketing 262–6, 342–4 marketing engineering 344–5 product development and engineering 357–63 ‘two-pizza’ 374 TechCrunch Disrupt 97, 99 technology conferences 97–8, 202, 312–13 Techstars 159, 160, 168 Tencent 307 Tencent QQ 226 term sheets 168, 169, 170, 243–4 testing 126–8, 177–8, 187–8, 192–3, 199–201 beta 201–4 channels 224–7 text messaging 21 unlimited packages 42 see also Short Message Service ‘thinking big’ 40–59, 82, 85 big problem solutions 41–3 disruptive ideas 53–9 human universals 44–5 sharing big 51–2, 52 smartphones uses 45–50 Thoughtworks 196 time, spent checking smartphones 25–6, 26, 45–50 Tito, Dennis 13 tone of voice 111 top-down approaches 311 traction 233, 252 traffic information apps 43 traffic trackers 146 translation 296–7 transparency 311–12, 412–13 Trilogy 13 Tumblr 110, 226, 399, 418 Twitter 41, 48, 54, 72, 226, 394 acquisitions 418 and application programming interface 36 and Bootstrap 145 and business identity 114 delivering delight 206 and e-commerce decisions 272 and FreeMyApps.com 271 funding 419, 421 and getting your app found 147 initial public offering 421 and Instagram 51, 76–7, 79–80 name 110 and virality 281 ‘two-pizza’ teams 374 Uber 6, 36, 87, 89, 333, 350 and attribution for referrals 231 design 131 funding 320, 384, 422 international growth 295, 299–302 name 107, 110 revenue engine 276 revenue per employee 401 scaling product innovation 355–6 staff 339, 399 user notifications 292 virality 280 Under Armour 419 Union Square Ventures (USV) 3, 158, 242, 261, 262, 288, 321, 323, 377, 383 unique propositions 198 UnitedHealth Group 419 URLs 110 ‘user experience’ (UX) experts 190 user journeys 127–8, 213–14 user notifications 291–4 user stories 193 users 83, 175, 252, 327, 390 activation 136, 137, 139, 153–4, 211–12, 213 annual revenue per user (ARPU) 215, 219, 232, 236 communication with 208–9 definition 137 emotional response of 223–4 fanatical 294 finding apps 140–8 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291 metrics 136–9 net-adding of 206 ratings plus comments 204–5 referrals 137, 138, 139, 153, 154, 211–12, 213, 230–1 target 83, 115, 127 wants 180–97 see also acquisition (of users); retention (of users) Usertesting.com 200–1 USV see Union Square Ventures valuations 83, 161–3, 175, 237–8, 238, 253, 318, 319, 322, 327, 333, 391 venture capital 72, 75, 156–8, 165–6, 235–49, 261–2, 383–5, 385, 418–19 Viber app 6, 46, 1341 video calls 46, 47 viral coefficient 282–4 ‘viral’ growth 225, 278, 279–84 Communication virality 281 and cycle time 283–4 incentivised virality 280–1 inherent virality 280 measurement 282–4 social-network virality 281 word-of-mouth virality 281–2 virtual reality 39 vision 261, 393–4, 408–9, 414, 415 voice calls 46–7 voice-over-Internet protocol (VOIP) 46 voicemail 50 Wall Street Journal 43, 55 warranties 246 Waze app 6, 43, 97 acquisition 415–16 design 131 name 107 zero-user-acquisition cost 278 web browsing 49 Web Summit 97 websites 113–14, 144–8 WebTranslateIt (WTI) 297 WeChat app 46, 226, 306 Weibo 48 Weiner, Jeff 408–9 Wellington Partners 4 Weskamp, Marcos 207 Westergren, Tim 410 WhatsApp 6, 42, 46, 54–6, 87, 90, 226, 394 acquisition 42, 54–6, 416, 416–17, 417 cofounders 96 design 131, 144 funding 154, 320–1 platform 117–18 valuations 333 virality 280 White, Emily 340 Williams, Evan 41, 65 Williams, Rich 344 Wilson, Fred 110, 242, 288, 323, 377 Windows (Microsoft) 20–1, 22, 24, 24 Winklevoss twins 105 wireframes 127–8 Woolley, Caspar 15–16, 95, 124, 338 WooMe.com 14, 87–8, 101–2, 263 Workday 90 world population 9–10 Worldwide Developers Conference (WWDC) 313 wowing people 8–9 WTI see WebTranslateIt Xiaomi 306 Y Combinator 159–60, 184–5, 211, 407, 410–11 Yahoo!

 

pages: 369 words: 80,355

Too Big to Know: Rethinking Knowledge Now That the Facts Aren't the Facts, Experts Are Everywhere, and the Smartest Person in the Room Is the Room by David Weinberger

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airport security, Alfred Russel Wallace, Amazon Mechanical Turk, Berlin Wall, Black Swan, book scanning, Cass Sunstein, corporate social responsibility, crowdsourcing, Danny Hillis, David Brooks, Debian, double entry bookkeeping, double helix, en.wikipedia.org, Exxon Valdez, Fall of the Berlin Wall, future of journalism, Galaxy Zoo, Hacker Ethic, Haight Ashbury, hive mind, Howard Rheingold, invention of the telegraph, jimmy wales, John Harrison: Longitude, Kevin Kelly, linked data, Netflix Prize, New Journalism, Nicholas Carr, Norbert Wiener, openstreetmap, P = NP, Pluto: dwarf planet, profit motive, Ralph Waldo Emerson, RAND corporation, Ray Kurzweil, Republic of Letters, RFID, Richard Feynman, Richard Feynman, Ronald Reagan, semantic web, slashdot, social graph, Steven Pinker, Stewart Brand, technological singularity, Ted Nelson, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Whole Earth Catalog, X Prize

Thank you to Barbara Tillett, also at the Library, for her help. 9 See http://www.trancheproject.org. 10 Interview with John Wilbanks. 11 Southan and Cameron, “Beyond the Tsunami,” p. 117–118. 12 Hiroaki Kitano, “Systems Biology: A Brief Overview,” Science 295, no. 5560 (March 1, 2002): 1662–1664. 13 For a superb introduction, see Steven Johnson, Emergence (Scribner, 2001). 14 See http://www.icosystem.com/labsdemos/the-game/. 15 Eric Bonabeau, “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems,” Proceedings of the National Academy of Sciences 99, suppl. 3 (May 14, 2002): 7280–7287, www.pnas.org/cgi/doi/0.1073/pnas.082080899. 16 Kellan Davidson, “Eureqa and Technological Singularity,” Ithaca Action News, May 12, 2010, http://ithacaactionnews.wordpress.com/2010/05/12/eureqa-and-technological-singularity/. 17 Quoted in Brandon Keim, “Download Your Own Robot Scientist,” Wired Science, December 3, 2009, http://www.wired.com/wiredscience/2009/12/download-robot-scientist/#ixzz0vrP0I4G9. See also the exceptional RadioLab program on this topic: “Limits of Science,” April 16, 2010, http://www.wnyc.org/shows/radiolab/episodes/2010/04/16/segments/149570. 18 Nicholas Taleb Nassim, The Black Swan (Random House, 2007). 19 The story may be apocryphal, according to a report by Nicholas Wade in “A Family Feud over Mendel’s Manuscript on the Laws of Heredity,” May 31, 2010, http://philosophyofscienceportal.blogspot.com/2010/06/gregor-mendel-and-pea-breeding.html. 20 Jennifer Laing, “Comet Hunter,” Universe Today, December 11, 2001, http://www.universetoday.com/html/articles/2001–1211a.html. 21 Jennifer Ouellette, “Astronomy’s Amateurs a Boon for Science,” Discovery News, September 20, 2010, http://news.discovery.com/space/astronomys-amateurs-a-boon-for-science.html. 22 Mark Frauenfelder, “The Return of Amateur Science,” Boing Boing, December 22, 2008, http://www.good.is/post/the-return-of-amateur-science/. 23 Thanks to the people who responded to the request for examples I posted on my Web site: Garrett Coakley, Jeremy Price, Miriam Simun, Andrew Weinberger, Jim Richardson, and Lars Ludwig.

And the solution to this problem is the Eureqa project.”17 The world’s complexity may simply outrun our brain’s capacity to understand it. Model-based knowing has many well-documented difficulties, especially when we are attempting to predict real-world events subject to the vagaries of history; a Cretaceous-era model of that era’s ecology would not have included the arrival of a giant asteroid in its data, and no one expects a black swan.18 Nevertheless, models can have the predictive power demanded of scientific hypotheses. We have a new form of knowing. This new knowledge requires not just giant computers but a network to connect them, to feed them, and to make their work accessible. It exists at the network level, not in the heads of individual human beings. But bigness is just the first property of networks the new scientific knowledge is absorbing. 2.

 

pages: 291 words: 81,703

Average Is Over: Powering America Beyond the Age of the Great Stagnation by Tyler Cowen

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Amazon Mechanical Turk, Black Swan, brain emulation, Brownian motion, Cass Sunstein, choice architecture, complexity theory, computer age, computer vision, cosmological constant, crowdsourcing, dark matter, David Brooks, David Ricardo: comparative advantage, deliberate practice, Drosophila, en.wikipedia.org, endowment effect, epigenetics, Erik Brynjolfsson, eurozone crisis, experimental economics, Flynn Effect, Freestyle chess, full employment, future of work, game design, income inequality, industrial robot, informal economy, Isaac Newton, Khan Academy, labor-force participation, Loebner Prize, low skilled workers, manufacturing employment, Mark Zuckerberg, meta analysis, meta-analysis, microcredit, Narrative Science, Netflix Prize, Nicholas Carr, pattern recognition, Peter Thiel, randomized controlled trial, Ray Kurzweil, reshoring, Richard Florida, Richard Thaler, Ronald Reagan, Silicon Valley, Skype, statistical model, stem cell, Steve Jobs, Turing test, Tyler Cowen: Great Stagnation, upwardly mobile, Yogi Berra

A player is least likely to make a major error when the game is tight, and if anything, players do their absolute best when they are faced with a slight disadvantage in their position. When players are decisively up or down, they don’t seem to think or concentrate with the same facility. Again, this is a sign of human rationality, at least if there is some need for a conservation of effort. Before these investigations, Ken expected to find evidence for a Nassim TalebBlack Swan” model of cognitive failure. That is, a lot of errors coming out of the blue. But in fact, radically surprising “Black Swan” errors don’t play much of a role in the final outcome. Most games are decided on the basis of the accumulation of advantages, and the level of error is fairly well predicted by the relative skills of the players. Ken finds these results all the way down to the level of a 1,600-rated player, which would be a middling club player in most cities (he has yet to look at the games of worse players).

 

pages: 829 words: 186,976

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

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airport security, availability heuristic, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Swan, Broken windows theory, Carmen Reinhart, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Freestyle chess, fudge factor, George Akerlof, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, new economy, Norbert Wiener, PageRank, pattern recognition, pets.com, prediction markets, Productivity paradox, random walk, Richard Thaler, Robert Shiller, Robert Shiller, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, wikimedia commons

Murrah Federal Building, 425 algorithms, 265, 426 all-in bet, 306 Allison, Graham, 433–35 Al Qaeda, 422, 424, 425, 426, 433, 435–36, 440, 444 Alzheimer’s, 420 Amazon.com, 352–53, 500 American exceptionalism, 10 American Football League (AFL), 185–86, 480 American League, 79 American Stock Exchange, 334 Amsterdam, 228 Anchorage, Alaska, 149 Anderson, Chris, 9 Angelo, Tommy, 324–26, 328 animals, earthquake prediction and, 147–48 Annals of Applied Statistics, 511–12 ANSS catalog, 478 Antarctic, 401 anthropology, 228 antiretroviral therapy, 221 Apple, 264 Archilochus, 53 Arctic, 397, 398 Arianism, 490 Aristotle, 2, 112 Armstrong, Scott, 380–82, 381, 388, 402–3, 405, 505, 508 Arrhenius, Svante, 376 artificial intelligence, 263, 293 Asia, 210 asset-price bubble, 190 asymmetrical information, 35 Augustine, Saint, 112 Australia, 379 autism, 218, 218, 487 availability heuristic, 424 avian flu, see bird flu A/Victoria flu strain, 205–6, 208, 483 Babbage, Charles, 263, 283 Babyak, Michael, 167–68 baby boom, 31 Babylonians, 112 Bachmann, Michele, 217 bailout bills, 19, 461 Bak, Per, 172 Baker, Dean, 22 Bane, Eddie, 87 Bank of England, 35 Barbour, Haley, 140 baseball, 9, 10, 16, 74–106, 128, 426, 446, 447, 451n aging curve in, 79, 81–83, 81, 83, 99, 164 betting on, 286 luck vs. skill in, 322 minor league system in, 92–93 results in, 327 rich data in, 79–80, 84 Baseball America, 75, 87, 89, 90, 90, 91 Baseball Encyclopedia, 94 Baseball Prospectus, 75, 78, 88, 297 basic reproduction number (R0), 214–15, 215, 224, 225, 486 basketball, 80n, 92–93, 233–37, 243, 246, 256, 258, 489 batting average, 86, 91, 95, 100, 314, 321, 321, 339 Bayer Laboratories, 11–12, 249 Bayes, Thomas, 240–43, 251, 253, 254, 255, 490 Bayesian reasoning, 240, 241–42, 259, 349, 444 biases and beliefs in, 258–59 chess computers’ use of, 291 Christianity and, 490 in climatology, 371, 377–78, 403, 406–7, 407, 410–11 consensus opinion and, 367 Fisher’s opposition to, 252 gambling esteemed in, 255–56, 362 priors in, 244, 245, 246, 252, 255, 258–59, 260, 403, 406–7, 433n, 444, 451, 490, 497 stock market and, 259–60 Bayes’s theorem, 15, 16, 242, 243–49, 246, 247, 248, 249, 250, 258, 266, 331, 331, 448–49, 450–51 in poker, 299, 301, 304, 306, 307, 322–23 Beane, Billy, 77, 92, 93–94, 99–100, 103, 105–7, 314 Bear Stearns, 37 beauty, complexity and, 173 beer, 387, 459 behavioral economics, 227–28 Belgium, 459 Bellagio, 298–99, 300, 318, 495 bell-curve distribution, 368n, 496 Bengkulu, Indonesia, 161 Benjamin, Joel, 281 Berlin, Isaiah, 53 Berners-Lee, Tim, 448, 514 BetOnSports PLC, 319 bets, see gambling Betsy, Hurricane, 140 betting markets, 201–3, 332–33 see also Intrade biases, 12–13, 16, 293 Bayesian theory’s acknowledgment of, 258–59 in chess, 273 and errors in published research, 250 favorite-longshot, 497 of Fisher, 255 objectivity and, 72–73 toward overconfidence, 179–83, 191, 203, 454 in polls, 252–53 as rational, 197–99, 200 of scouts, 91–93, 102 of statheads, 91–93 of weather forecasts, 134–38 Bible, 2 Wicked, 3, 13 Biden, Joseph, 48 Big Data, 9–12, 197, 249–50, 253, 264, 289, 447, 452 Big Short, The (Lewis), 355 Billings, Darse, 324 Bill James Baseball Abstract, The, 77, 78, 84 bin Laden, Osama, 432, 433, 434, 440, 509 binomial distribution, 479 biological weapons, 437, 438, 443 biomedical research, 11–12, 183 bird flu, 209, 216, 229 Black, Fisher, 362, 367, 369 “Black Friday,” 320 Black Swan, The (Taleb), 368n Black Tuesday, 349 Blanco, Kathleen, 140 Blankley, Tony, 50 Blodget, Henry, 352–54, 356, 364–65, 500 Blue Chip Economic Indicators survey, 199, 335–36 Bluefire, 110–11, 116, 118, 127, 131 bluffing, 301, 303, 306, 310, 311, 328 Bonus Baby rule, 94 books, 2–4 cost of producing, 2 forecasting and, 5 number of, 2–3, 3, 459 boom, dot-com, 346–48, 361 Boston, 77 Boston Red Sox, 63, 74–77, 87, 102, 103–5 Bowman, David, 161–62, 167 Box, George E.

The answer to what economists call the “equity premium puzzle”—why stocks have returned so much more money than bonds in a way that is disproportionate to the risks they entail—may simply be that the returns stocks achieved in the twentieth century were anomalous, and the true long-run return is not as high as 7 percent. * This is no surprise given how poor most of us—including most of us who invest for a living—are at estimating probabilities. The few who are good at it have the potential to clean up. However, most options traders receive a poor return, and it is a very risky activity on the whole. * As Nassim Nicholas Taleb detailed in The Black Swan and as Fama also discussed in his thesis, the movement of stock prices does not follow a gentle bell-curve distribution. Instead, stock-price movements are characterized by very occasional but very large swings up or down. The distribution of stock-market crashes can also be modeled fairly well by a power-law distribution, which is the same function that governs the frequency of earthquakes

Robert Shiller, the Yale economist, had noted its beginnings as early as 2000 in his book Irrational Exuberance.14 Dean Baker, a caustic economist at the Center for Economic and Policy Research, had written about the bubble in August 2002.15 A correspondent at the Economist magazine, normally known for its staid prose, had spoken of the “biggest bubble in history” in June 2005.16 Paul Krugman, the Nobel Prize–winning economist, wrote of the bubble and its inevitable end in August 2005.17 “This was baked into the system,” Krugman later told me. “The housing crash was not a black swan. The housing crash was the elephant in the room.” Ordinary Americans were also concerned. Google searches on the term “housing bubble” increased roughly tenfold from January 2004 through summer 2005.18 Interest in the term was heaviest in those states, like California, that had seen the largest run-up in housing prices19—and which were about to experience the largest decline. In fact, discussion of the bubble was remarkably widespread.

 

pages: 350 words: 109,220

In FED We Trust: Ben Bernanke's War on the Great Panic by David Wessel

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Asian financial crisis, asset-backed security, bank run, banking crisis, banks create money, Berlin Wall, Black Swan, central bank independence, credit crunch, Credit Default Swap, crony capitalism, debt deflation, Fall of the Berlin Wall, financial innovation, financial intermediation, full employment, George Akerlof, housing crisis, inflation targeting, London Interbank Offered Rate, Long Term Capital Management, market bubble, moral hazard, mortgage debt, new economy, Northern Rock, price stability, quantitative easing, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, savings glut, Socratic dialogue, too big to fail

The fact is, the U.S. economy did keep growing, and deflation didn’t arrive. Indeed, one reason investors were willing to pay so much for risky securities was that they believed — or acted as if they believed — that the Fed and other central banks had found a way to keep economies growing with little inflation and with recessions that were mild and infrequent. But Nassim Nicholas Taleb, an options trader who made his reputation by describing unanticipated but unusually important events that he called “black swans,” criticizes Greenspan — and Bernanke — for failing to see that the calm was masking a building of hidden risks. “It was like someone sitting on dynamite and saying, ‘It’s okay, we’re safe because nothing has happened.’” Despite its apparent successes, the Greenspan Fed had some well-respected contemporary critics. One of the most prominent was Bill White, a veteran of the British and Canadian central banks, who between 1994 and 2008 was a top economist at the Bank for International Settlements, an international organization in Switzerland for central bankers.

Wall Street Journal, November 4, 2005, A1. 56 “The most severe” Charles W. Calomiris, “The Subprime Turmoil: What’s Old, What’s New and What’s Next,” Jackson Hole, Wyoming, October 2, 2008. http://www.kc.frb.org/publicat/sympos/2008/ Calomiris.08.20.08.pdf 57 “make sense tactically” Transcript, FOMC meeting, June 24-25, 2003.http://www.federalreserve.gov/ monetarypolicy/fomchistorical2003.htm 57 “It was like someone” “Outspoken: A Conversation with Nassim Nicholas Taleb,” Washington Post, March 15, 2009, B2. 58 “an eventual crisis” William R. White, “Is Price Stability Enough?” Bank for International Settlements, April 2006. http://www.bis.org/publ/work205.pdf 58 “We tried in 2004” Greg Ip and Jon Hilsenrath, “Debt Bomb: Inside the Subprime Mortgage Debacle,” Wall Street Journal, August 7, 2007, A1. 58 “I will stipulate” Transcript, FOMC meeting, August 12, 2003. http://www.federalreserve.gov/monetarypolicy/ fomchistorical2003.htm 59 the Wall Street Journal: “WSJ Forecasting Survey — March 2008,” Wall Street Journal. http://online.wsj.com/public/resources/documents/ wsjecon0308.xls 59 A 2004 Fed working paper: Joshua Gallin, “The Long-Run Relationship between House Prices and Rents,” Board of Governors of Federal Reserve System, September 2004. http://www.federalreserve.gov/pubs/feds/2004/200450/ 200450pap.pdf 59 “I would tell audiences” Alan Greenspan, The Age of Turbulence: Adventures in a New World (New York: Penguin Press, 2007), 232. 60 “We cannot practice” Transcript, Federal Reserve Bank of Kansas City, Jackson Hole Conference, August 2008. http://www.kc.frb.org/PUBLICAT/SYMPOS/1999/ sym99prg.htm 61 “He didn’t say anything” John Cassidy, “Anatomy of a Meltdown,” The New Yorker, December 1, 2008. http://www.newyorker.com/reporting/2008/12/01/ 081201fa_fact_cassidy?

 

pages: 354 words: 26,550

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems by Irene Aldridge

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algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business process, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, diversification, equity premium, fault tolerance, financial intermediation, fixed income, high net worth, implied volatility, index arbitrage, interest rate swap, inventory management, law of one price, Long Term Capital Management, Louis Bachelier, margin call, market friction, market microstructure, martingale, New Journalism, p-value, paper trading, performance metric, profit motive, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic trading, trade route, transaction costs, value at risk, yield curve

In a New York Times article published on January 2, 2009, David Einhorn, the µ – 2.33 σ µ – 1.65 σ α = 5% α = 1% µ FIGURE 17.2 The 95 percent parametric VaR corresponds to µ−1.65σ of the distribution, while the 99 percent parametric VaR corresponds to µ−2.33σ of the distribution. Risk Management 257 founder of the hedge fund Greenlight Capital, stated that VaR was “relatively useless as a risk-management tool and potentially catastrophic when its use creates a false sense of security among senior managers and watchdogs. This is like an air bag that works all the time, except when you have a car accident.” The article also quoted Nassim Nicholas Taleb, the bestselling author of The Black Swan, as calling VaR metrics “a fraud.” Jorion (2000) points out that the VaR approach both presents a faulty measure of risk and actively pushes strategists to bet on extreme events. Despite all the criticism, VaR and ES have been mainstays of corporate risk management for years, where they present convenient reporting numbers. To alleviate the shortcomings of the VaR, many quantitative outfits began to parameterize extreme tail distributions to develop fuller pictures of extreme losses.

., 142, 147, 192, 195, 279 Suleiman, Basak, 260 Summers, Lawrence, 179 Swap trading: fixed-income markets, 40–42 foreign exchange markets, 43–46 Sycara, K., 279–280 Systematic trading, 15 distinguished from high-frequency trading, 18–19 System testing, automated system implementation, 248–249 INDEX Tail risk, 50 comparative ratios and, 56 risk measurement and, 257–258 Take-profit orders, 70, 73 Taleb, Nassim Nicholas, 257 Tamirisa, N.T., 183 Taskin, F., 183 Tâtonnement (trial and error), in price adjustments, 127–128 Tauchen, George, 125 Taxes, post-trade analysis of, 288 Taylor series expansion (bilinear models), 109–110 Technical analysis, 22–23 evolution of, 13–14, 15 inventory trading, 142–143 Technological innovation, financial markets and evolution of high-frequency trading, 7–13 Technology and High-Frequency Trading Survey, 21 Tepla, Lucie, 260 Testing methods, for market efficiency and predictability, 79–89 autoregression-based tests, 86 co-integration-based tests, 89 Martingale hypothesis and, 86–88 non-parametric runs test, 80–82 random walks tests, 82–86 Teversky, A., 253 Thaler, R., 87 Theissen, Eric, 142 Third-party research, 26 Thomson/Reuters, 25 Threshold autoregressive (TAR) models, 110 Tick data, 21, 115–125 bid-ask bounce and, 120–121 bid-ask spreads and, 118–120 duration models of arrival, 121–123 econometric techniques applied to, 123–125 properties of, 116–117 quantity and quality of, 117–118 Time distortion, automated system implementation, 243–245 Time-weighted average price (TWAP), 297 339 Index Timing risk costs, 293–294 Timing specifications, for orders, 68 Tiwari, A., 139 Tkatch, Isabel, 67, 132, 274, 277–278 Todd, P., 214 Tower Research Capital, 24 TRADE Group survey, 17–18 Trading methodology, evolution of, 13–19 Trading platform, 31 Trading software, 25 Trading strategy accuracy (TSA) back-testing method, 222–231 Trailing stop, 267 Transaction costs: information-based trading, 149–151 market microstructure trading, inventory models, 128–129 market versus limit orders, 62–63 portfolio optimization, 206–208 post-trade analysis of, 283–295 Transparent execution costs, 34, 284, 285–288 Treynor ratio, 51, 52t, 55 Triangular arbitrage, foreign exchange markets, 190 Uncovered interest parity arbitrage, foreign exchange markets, 191 Unit testing, automated system implementation, 247 Uppal, R., 210 Upside Potential Ratio, 53t, 56 Use case testing, automated system implementation, 249 U.S.

 

pages: 362 words: 99,063

The Education of Millionaires: It's Not What You Think and It's Not Too Late by Michael Ellsberg

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affirmative action, Black Swan, Burning Man, corporate governance, financial independence, follow your passion, future of work, hiring and firing, job automation, knowledge worker, Lean Startup, Mark Zuckerberg, means of production, meta analysis, meta-analysis, new economy, Peter Thiel, profit motive, race to the bottom, Sand Hill Road, shareholder value, side project, Silicon Valley, Skype, Steve Ballmer, telemarketer, Tony Hsieh

In a more positive example, a few kids sitting in their Harvard dorm room (before they dropped out) launched a venture that changed, within a few years, the way much of the world socializes and communicates. That’s the globalized, interconnected world we live in now. Changes in one part of the system impact the entire system. Prepare for many more interruptions, shocks, surprises, global reorganizations, “black swans,” and totally unforeseen developments on this scale (both positive and negative). The “left field” out of which random and unpredictable events can come has just gone global.8 In this increasingly unpredictable and chaotic world, the wisest choice for thriving and flourishing is to focus your efforts on cultivating skills, habits, and ways of being that will work for you under a wide range of market circumstances and economic realities, and which will allow you to bounce back and adapt to changes, shifts, shocks, crashes, and new opportunities as they arise.

Later in his book, he argues that more hours in school training hard in academic subjects, not fewer hours, is essential for inner-city kids’ success. 6 Shapiro, p. 782. 7 Williams, accessed January 15, 2010. 8 Kleinfield, accessed October 9, 2009. 9 Pink (2001), locations 197–199 on Kindle edition. 10 Pink, locations 810–819 on Kindle edition. 11 Pink, location 843 on Kindle edition. ■ SUCCESS SKILL #1 1 Johnson, location 1035 on Kindle edition. 2 Johnson, locations 2696–2714 on Kindle edition. 3 Komisar, p. 154 4 Komisar, pp. 65–66. 5 For a detailed and brilliant exposition of survivorship bias, see Taleb. 6 Godin (2010 A), accessed April 3, 2010. 7 Moskovitz, accessed March 22, 2010. ■ SUCCESS SKILL #2 1 Cohen, accessed December 13, 2010. 2 Bertoni, accessed December 13, 2010. 3 One of his product lines, the David DeAngelo series of trainings for men, is quite controversial. The early trainings in the series focus on helping men “pick up” women through pickup lines, tricks, and cocky attitudes.

■ SUCCESS SKILL #7 1 Quoted in Fireside Learning, accessed May 2, 2011. 2 Kawasaki, accessed May 2, 2011. 3 Sykes, p. 57. 4 Time, accessed May 2, 2011. 5 Fortune, p. 21. 6 Fortune, pp. 122–127, 163–164. 7 Fortune, p. 127. 8 Stewart, p. 13. 9 Wikipedia contributors, “Walt Disney,” accessed December 23, 2010. 10 Pinsky, p. 9. 11 Fortune, p. 122. 12 Ford, Henry, pp. 121–123. 13 Time, accessed May 2, 2011. ■ EPILOGUE 1 Munna, accessed January 29, 2011. 2 Taylor (2009 A), accessed January 29, 2011. 3 Taylor (2009 B), accessed January 29, 2011. 4 Reynolds, accessed January 31, 2011. 5 Segal, accessed January 31, 2011. 6 Thiel Foundation, accessed January 31, 2011. 7 Weisberg, accessed January 31, 2011. 8 For more on this topic, see Taleb. 9 Marmer, accessed April 12, 2011. 10 Herold, accessed March 17, 2011. INDEX Abramson, Josh Absolutepowerdating.com Academic degrees. See Higher education Ackoff, Russell AcroYoga Adams, Anthony Advice earning money from giving to mentors wake-up call, giving Andragogy Andros, Maria, success, evolution of Art of Earning a Living steps in See also Meaningful work, creating Asana.com Ash, David, success, evolution of Baby boomers Backpocketcoo.com Ballmer, Steve Banister, Cyan, success, evolution of Banister, Scott, outcome, focusing on Barry, Katie and Kerry Bencivenga, Gary, web site of Bisnow, Elliott on asking for advice as college non-graduate on contributing on mentors success, evolution of Summit Series Bisnow.com Blank, Steve Bloggers largest platform for making money vlogging Bootstrapping of capital and direct-response marketing elements of and financial stability investing method in of self-education success, case examples Brand marketing Brand of you building online defined versus having resume own name, using success, case examples Brandon, Craig Branson, Sir Richard Brown, James Buffett, Warren Burning Man festival Business Network International BustedTees California Leadership Center Capital bootstrapping of connection capital financial human capital Caples, John Carse, James Cash-generation ethic Cheng, Victor, sales coaching sessions by Cialdini, Robert Clarium Capital Clark, Brian, third tribe marketing Clason, George Clemens, Craig Clinton, Bill Colaizzi, Dr.

 

pages: 323 words: 95,939

Present Shock: When Everything Happens Now by Douglas Rushkoff

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algorithmic trading, Andrew Keen, bank run, Benoit Mandelbrot, big-box store, Black Swan, British Empire, Buckminster Fuller, cashless society, citizen journalism, clockwork universe, cognitive dissonance, Credit Default Swap, crowdsourcing, Danny Hillis, disintermediation, Donald Trump, double helix, East Village, Elliott wave, European colonialism, Extropian, facts on the ground, Flash crash, game design, global supply chain, global village, Howard Rheingold, hypertext link, Inbox Zero, invention of agriculture, invention of hypertext, invisible hand, iterative process, John Nash: game theory, Kevin Kelly, laissez-faire capitalism, Law of Accelerating Returns, loss aversion, mandelbrot fractal, Marshall McLuhan, Merlin Mann, Milgram experiment, mutually assured destruction, Network effects, New Urbanism, Nicholas Carr, Norbert Wiener, Occupy movement, passive investing, pattern recognition, peak oil, price mechanism, prisoner's dilemma, Ralph Nelson Elliott, RAND corporation, Ray Kurzweil, recommendation engine, Silicon Valley, Skype, social graph, South Sea Bubble, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, supply-chain management, the medium is the message, The Wisdom of Crowds, theory of mind, Turing test, upwardly mobile, Whole Earth Catalog, WikiLeaks, Y2K

Not even the economists who came up with these models are particularly immune from their often-imprecise predictions and recommendations. Many of the “quant” teams at hedge funds and the risk-management groups within brokerage houses use fractals to find technical patterns in stock market movements. They believe that, unlike traditional measurement and prediction, these nonlinear, systems approaches transcend the human inability to imagine the unthinkable. Even Black Swan author Nassim Taleb, who made a career of warning economists and investors against trying to see the future, believes in the power of fractals to predict the sudden shifts and wild outcomes of real markets. He dedicated the book to Benoit Mandelbrot. While fractal geometry can certainly help us find strong, repeating patterns within the market activity of the 1930s Depression, it did not predict the crash of 2007.

See financial world storage: apocalypto and, 265; digiphrenia and, 77; fractalnoia and, 238, 240; of information, 5, 142–49; new “now” and, 4, 5; overwinding and, 136, 142, 143, 145–49, 171, 184, 186, 188, 189, 190, 194; of time, 141–49 store windows, 165, 166 storytelling: accelerating change and, 14–16; audience participation in creation of, 61, 63, 64; benefits of, 13–14; content of stories and, 22–23; as creating context, 13–14; as cultural value, 13; digiphrenia and, 72–73, 85; endings in, 32–34, 62; failure of, 18; as influencing the future, 16; and learning from earlier generations, 138; linear, 7; manhood and, 39; as means of storing information values, 16; mechanics of, 19–20; narrative collapse and, 7, 18–23; new “now” and, 6; overwinding and, 138; and perpetuation of story, 32–33; and players as story, 64; as predicting the future, 16; responses to living in a world without, 39–43; role of, 13; as spoofs, 28; structure and, 22; traditional linear, 18–34, 61, 62, 66; as way of experiencing world, 13–14; as way of talking about the world, 13. See also narrative collapse stress: apocalypto and, 247, 250; on computers, 140n; digiphrenia and, 7, 73, 89, 100, 103, 121–22, 126, 128; narrative collapse and, 49, 65–66; overwinding and, 132, 136, 139, 140n, 144. See also tension/anxiety Surowiecki, James, 228 sync, digiphrenia and, 100–101, 106, 109, 121, 122, 126, 127, 128 systems theory, 200, 226–28 Taleb, Nassim, 229 Tarantino, Quentin, 30 Taylor, Frederick, 81 Tea Party, 53–55, 264 technology: apocalypto and, 249–50, 254, 255, 256–58, 259, 260, 263; development of new, 192; digiphrenia and, 7, 93–109; exploitation of, 30; fractalnoia and, 231–32; interactive, 211; narrative collapse and, 20, 30; new “now” and, 3; as partner in human evolution, 256–57; time as a, 76–87, 88–90; unintended consequences of, 249–50.

 

pages: 488 words: 144,145

Inflated: How Money and Debt Built the American Dream by R. Christopher Whalen

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Albert Einstein, bank run, banking crisis, Black Swan, Bretton Woods, British Empire, California gold rush, Carmen Reinhart, central bank independence, conceptual framework, corporate governance, cuban missile crisis, currency peg, debt deflation, falling living standards, fiat currency, financial deregulation, financial innovation, financial intermediation, floating exchange rates, Fractional reserve banking, full employment, global reserve currency, housing crisis, interchangeable parts, invention of radio, Kenneth Rogoff, laissez-faire capitalism, liquidity trap, means of production, money: store of value / unit of account / medium of exchange, moral hazard, mutually assured destruction, non-tariff barriers, oil shock, payday loans, Plutocrats, plutocrats, price stability, pushing on a string, quantitative easing, rent-seeking, reserve currency, Ronald Reagan, special drawing rights, The Chicago School, The Great Moderation, too big to fail, trade liberalization, transcontinental railway, Upton Sinclair, women in the workforce

Reinhart, Carmen, “This Time Is Different Chartbook: Country Histories on Debt, Default, and Financial Crises,” NBER Working Paper 18815, March 2010, Figure 66c, 119. 15. Friedman, Benjamin, “Postwar Changes in the American Financial Markets,” NBER Working Paper No. 458, March 1981, Issued in March 1981. 16. Whalen, Richard J., “The Shifting Equation of Nuclear Defense,” Fortune (June 1, 1967). 17. Morgan, Iwan, Deficit Government: Taxing and Spending in Modern America (Chicago: Ivan R. Dee, 1995), ix. 18. Taleb, Nassim, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2008). 19. Baldwin, Robert E., The Changing Nature of U.S. Trade Policy Since WWII (University of Chicago Press, 1984), 5–7. 20. Lake, David, The International Political Economy of Trade, Vol. I, (Cheltenham: Edward Elgar Publishing, 1993), 8–10. 21. The United States in World Affairs: The World Economy in 1951, Council on Foreign Relations, 225–229. 22.

During and after WWII, the notion of managing government from the national or macro level, a “God’s Eye View” in classical terms, was received as wisdom and gospel, an embedded part of the narrative of American government. Members of both major political parties in the United States became convinced that deficit spending would employ unused resources correct market failures, and produce optimal economic results. This narrative, this deliberate act of collective delusion, has governed the direction of the U.S. economy ever since. The author Nassim Taleb talks about the importance of narrative in how human beings come to believe that they understand a complex issue: 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.

Stevenson, Adlai Stewart, John Fat Years and the Lean Stewart, William Silver Knight (pamphlet) Stillman, James Stock investment New Era theory perspective, change Stock markets human nature, impact purchases (financing), short-term loans (usage) Stocks, decline Strong, Benjamin Bankers Trust Company exit Delano/House meeting Morgan control replacement Strong, William Subprime debt bubble, blame Subprime Debt Crisis (2008) Subprime debt crisis, Fed/Treasury assistance Subprime financial crisis, perspective (Raynes) Subprime housing crisis (2007-2009), issues Suez Canal, closing (1956) Sugar Equalization Board Summers, Larry Swanberg, W.A. Sylla, Richard Systemic risk, moral dilemma Szymczak, M.S. Taft, William Howard government debt Taleb, Nassim Tammany Hall Roosevelt, impact Tansil, Charles Callan America Goes to War Tariffs competitiveness, FDR promise FDR maintenance imposition increase protection, increase reduction FDR endorsement Hoover opposition Republican party position Tariffs for revenue only Taxes FDR increase reduction, passage Tennessee Iron & Coal Company U.S. Steel acquisition Tenth National Bank funds, Fisk collection attempt Terror, balance (exploitation) Texas, annexation (Cooke speculation) Third Bank of the United States, authorization/Tyler veto Third World loan portfolio Thomas, Elmer Thomas, J.J.

 

pages: 497 words: 144,283

Connectography: Mapping the Future of Global Civilization by Parag Khanna

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1919 Motor Transport Corps convoy, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, 9 dash line, additive manufacturing, Admiral Zheng, affirmative action, agricultural Revolution, Airbnb, Albert Einstein, amateurs talk tactics, professionals talk logistics, Amazon Mechanical Turk, Asian financial crisis, asset allocation, autonomous vehicles, banking crisis, Basel III, Berlin Wall, bitcoin, Black Swan, blockchain, borderless world, Boycotts of Israel, Branko Milanovic, BRICs, British Empire, business intelligence, call centre, capital controls, charter city, clean water, cloud computing, collateralized debt obligation, complexity theory, corporate governance, corporate social responsibility, credit crunch, crony capitalism, crowdsourcing, cryptocurrency, cuban missile crisis, data is the new oil, David Ricardo: comparative advantage, deglobalization, deindustrialization, dematerialisation, Deng Xiaoping, Detroit bankruptcy, diversification, Doha Development Round, edge city, Edward Snowden, Elon Musk, energy security, ethereum blockchain, European colonialism, eurozone crisis, failed state, Fall of the Berlin Wall, family office, Ferguson, Missouri, financial innovation, financial repression, forward guidance, global supply chain, global value chain, global village, Google Earth, Hernando de Soto, high net worth, Hyperloop, ice-free Arctic, if you build it, they will come, illegal immigration, income inequality, income per capita, industrial robot, informal economy, Infrastructure as a Service, interest rate swap, Internet of things, Isaac Newton, Jane Jacobs, Jaron Lanier, John von Neumann, Julian Assange, Just-in-time delivery, Kevin Kelly, Khyber Pass, Kibera, Kickstarter, labour market flexibility, labour mobility, LNG terminal, low cost carrier, manufacturing employment, mass affluent, megacity, Mercator projection, microcredit, mittelstand, Monroe Doctrine, mutually assured destruction, New Economic Geography, new economy, New Urbanism, offshore financial centre, oil rush, oil shale / tar sands, oil shock, openstreetmap, out of africa, Panamax, Peace of Westphalia, peak oil, Peter Thiel, Plutocrats, plutocrats, post-oil, post-Panamax, private military company, purchasing power parity, QWERTY keyboard, race to the bottom, Rana Plaza, rent-seeking, reserve currency, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Coase, Scramble for Africa, Second Machine Age, sharing economy, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, six sigma, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, South China Sea, South Sea Bubble, sovereign wealth fund, special economic zone, spice trade, Stuxnet, supply-chain management, sustainable-tourism, TaskRabbit, telepresence, the built environment, Tim Cook: Apple, trade route, transaction costs, UNCLOS, uranium enrichment, urban planning, urban sprawl, WikiLeaks, young professional, zero day

Subramanian, Arvind, and Martin Kessler. The Hyperglobalization of Trade and Its Future. Peterson Institute for International Economics, 2013. Sudjic, Deyan. Hundred Mile City. Mariner Books, 1993. Sunstein, Cass R. Infotopia: How Many Minds Produce Knowledge. Oxford University Press, 2008. ———. Simpler: The Future of Government. Simon & Schuster, 2013. Taleb, Nassim Nicholas. Antifragile: Things That Gain from Disorder. Random House Trade Paperbacks, 2014. ———. The Black Swan. Random House, 2010. Taniguchi, Eiichi, Tien Fang Fwa, and Russell G. Thompson. Urban Transportation and Logistics: Health, Safety, and Security Concerns. CRC Press, 2013. Tansey, Oisin. “Internationalized Regimes: A Second Dimension of Regime Hybridity.” Democratization 20, no. 7 (2013). Taylor, Peter. World City Network: A Global Urban Analysis.

“Global supply chain” and “global value chain” are often used interchangeably, with the latter sometimes preferred to emphasize the value-added processes not inherent in simple supply-demand terminology. Others speak of value webs or value networks to capture the wide range of participants involved in supply chains and their interdependent and mutually beneficial nature. *8 I use “supply chain world” or “supply-demand world” or “supply-demand system” or other variations interchangeably. *9 In his book Antifragile, Nassim Taleb demonstrates through the convexity principle that the degradation effect (harm) diminishes across a range of smaller units as opposed to a larger one of size equal to the sum of the smaller units. *10 Solids, liquids, and gases experience flow and friction when moving in the open or in contained spaces. In fluid mechanics, friction takes the form of viscosity, meaning a material’s resistance to changing its form

Some of the most towering intellects alive today have been steady intellectual mentors both for this book and generally over the past decade or more. It seems almost offensive to list them in one paragraph, but my appreciation for their brilliance and friendship goes beyond anything I can write: Graham Allison, Benjamin Barber, Eric Beinhocker, Daniel Bell, Ian Bremmer, Ann Florini, Tom Friedman, Robert Kaplan, Pratap Mehta, Pankaj Mishra, Charles Pirtle, Carne Ross, John Ruggie, Saskia Sassen, Richard Sennett, Nassim Taleb, and Scott Malcomson, who has edited my essays for close to a decade and kindly reviewed several chapters of this book as well. Friends have witnessed many times the fine line crossed from casual banter to intense debate, usually marked by the unsheathing of my Moleskine notepad. Beyond the innocent bystanders caught in the cross fire, I want to single out those who have been consistent partners in productive discourse: Ozi Amanat, David Anderson, Scott Anthony, Matt Armstrong, Alex Bernard, Neel Chowdhury, Laura Deal, Jon Fasman, Howard French, Jared Genser, Jan-Philipp Goertz, Jeremy Grant, Nisid Hajari, Niels Hartog, Seb Kaempf, Gaurang Khemka, Karan Khemka, Bernd Kolb, Mark Leonard, Greg Lindsay, Shaun Martin, Ann Mettler, Chandran Nair, Madhu Narasimhan, Pradeep Ramamurthy, Abhijnan Rej, Tom Sanderson, Rana Sarkar, Lutfey Siddiqi, David Skilling, Nick Snyder, Robert Steele, Dorjee Sun, Vijay Vaitheeswaran, Kirk Wagar, Chris Wilson, Art Winter, Jan Zielonka, and Teddy Zmrhal.

 

pages: 298 words: 43,745

Understanding Sponsored Search: Core Elements of Keyword Advertising by Jim Jansen

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