hindsight bias

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

Albert Einstein, Andrew Wiles, asset allocation, availability heuristic, backtesting, Black Swan, book value, butter production in bangladesh, buy and hold, capital asset pricing model, cognitive dissonance, compound rate of return, computerized trading, Daniel Kahneman / Amos Tversky, distributed generation, Elliott wave, en.wikipedia.org, equity risk premium, feminist movement, Great Leap Forward, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, managed futures, mental accounting, meta-analysis, p-value, pattern recognition, Paul Samuelson, Ponzi scheme, price anchoring, price stability, quantitative trading / quantitative finance, Ralph Nelson Elliott, random walk, retrograde motion, revision control, risk free rate, risk tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Sharpe ratio, short selling, source of truth, statistical model, stocks for the long run, sugar pill, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

After a two-week interval, 67 percent of the students remembered their predictions as being more accurate than they actually were. In other words, they were unable to recall their prior uncertainty. After several months, the percentage of students afflicted with hindsight bias jumped from 67 percent to 84 percent. The hindsight bias has been found to operate powerfully in trial testimony. Witnesses believe they are giving accurate accounts, but their recall of the order of events and of specific details are altered by knowing how matters actually turned out.53 The hindsight bias infects historical accounts. Historians, having the benefit of hindsight, will often point out that the rise of the Third Reich was quite predictable.

Some will experience initial success and because of the self-attribution bias are likely to attribute it to their expertise and the efficacy of the TA methods rather than chance. All of these factors can induce and maintain an unjustified sense of control and an ability to earn market-beating returns. The Hindsight Bias: I Knew Things Would Turn Out That Way The hindsight bias creates the illusion that the prediction of an uncertain event is easier than it really is when the event is viewed in retrospect, after its outcome is known. Once we learn the upshot of an uncertain situation, such as which team won a football game or in which direction prices 51 The Illusory Validity of Subjective Technical Analysis moved, subsequent to a TA pattern, we tend to forget how uncertain we really were prior to knowing the outcome.

It is because of this very pitfall that scientists are especially careful about defining procedures for making predictions and evaluating their accuracy. Subjective TA is especially prone to the hindsight bias because it lacks clearly defined rules for pattern identification, forecast generation, I wonder if I should bet on the Jets? The other team is pretty good. I knew the Jet’s would win. It was so obvious!! Why didn’t I bet? NY Jets Win Football Game October 1 October 2 Time FIGURE 2.6 The hindsight bias. October 3 52 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS and prediction evaluation. In real time, the practitioner of subjective TA faces a task of overwhelming ambiguity.


pages: 288 words: 81,253

Thinking in Bets by Annie Duke

banking crisis, behavioural economics, Bernie Madoff, Cass Sunstein, cognitive bias, cognitive dissonance, cognitive load, Daniel Kahneman / Amos Tversky, delayed gratification, Demis Hassabis, disinformation, Donald Trump, Dr. Strangelove, en.wikipedia.org, endowment effect, Estimating the Reproducibility of Psychological Science, fake news, Filter Bubble, Herman Kahn, hindsight bias, Jean Tirole, John Nash: game theory, John von Neumann, loss aversion, market design, mutually assured destruction, Nate Silver, p-value, phenotype, prediction markets, Richard Feynman, ride hailing / ride sharing, Stanford marshmallow experiment, Stephen Hawking, Steven Pinker, systematic bias, TED Talk, the scientific method, The Signal and the Noise by Nate Silver, urban planning, Walter Mischel, Yogi Berra, zero-sum game

He stated very clearly that he thought he should have known that the decision to fire the president would turn out badly. His decision-making behavior going forward reflected the belief that he made a mistake. He was not only resulting but also succumbing to its companion, hindsight bias. Hindsight bias is the tendency, after an outcome is known, to see the outcome as having been inevitable. When we say, “I should have known that would happen,” or, “I should have seen it coming,” we are succumbing to hindsight bias. Those beliefs develop from an overly tight connection between outcomes and decisions. That is typical of how we evaluate our past decisions. Like the army of critics of Pete Carroll’s decision to pass on the last play of the Super Bowl, the CEO had been guilty of resulting, ignoring his (and his company’s) careful analysis and focusing only on the poor outcome.

Finding no evidence in the record of any foreseeable likelihood of explosion at the time the foreman ordered the workers to remove tools from the tunnel, he concluded, “The verdict appears to be a consequence of hindsight bias—the human tendency to believe that whatever happened was bound to happen, and that everyone must have known it. If [the foreman] believed that an explosion was imminent, then he is a monster; but of that there is no evidence. Hindsight bias is not enough to support a verdict.” Once we know there was an explosion, it’s difficult to imagine the actions of the parties when the explosion was only one of several possible futures.

CHAPTER 5 Dissent to Win CUDOS to a magician Mertonian communism: more is more Universalism: don’t shoot the message Disinterestedness: we all have a conflict of interest, and it’s contagious Organized skepticism: real skeptics make arguments and friends Communicating with the world beyond our group CHAPTER 6 Adventures in Mental Time Travel Let Marty McFly run into Marty McFly Night Jerry Moving regret in front of our decisions A flat tire, the ticker, and a zoom lens “Yeah, but what have you done for me lately?” Tilt Ulysses contracts: time traveling to precommit Decision swear jar Reconnaissance: mapping the future Scenario planning in practice Backcasting: working backward from a positive future Premortems: working backward from a negative future Dendrology and hindsight bias (or, Give the chainsaw a rest) ACKNOWLEDGMENTS NOTES SELECTED BIBLIOGRAPHY AND RECOMMENDATIONS FOR FURTHER READING INDEX INTRODUCTION Why This Isn’t a Poker Book When I was twenty-six, I thought I had my future mapped out. I had grown up on the grounds of a famous New Hampshire prep school, where my father chaired the English department.


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

Abraham Maslow, Abraham Wald, affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, Apollo 13, Apple Newton, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, dark pattern, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Dunning–Kruger effect, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fake news, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Goodhart's law, Gödel, Escher, Bach, heat death of the universe, hindsight bias, housing crisis, if you see hoof prints, think horses—not zebras, Ignaz Semmelweis: hand washing, illegal immigration, imposter syndrome, incognito mode, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, karōshi / gwarosa / guolaosi, lateral thinking, loss aversion, Louis Pasteur, LuLaRoe, Lyft, mail merge, Mark Zuckerberg, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nocebo, nuclear winter, offshore financial centre, p-value, Paradox of Choice, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, power law, precautionary principle, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, Salesforce, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Streisand effect, sunk-cost fallacy, survivorship bias, systems thinking, The future is already here, The last Blockbuster video rental store is in Bend, Oregon, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Tragedy of the Commons, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, vertical integration, Vilfredo Pareto, warehouse robotics, WarGames: Global Thermonuclear War, When a measure becomes a target, wikimedia commons

We covered some of these biases way back in Chapter 1 with availability bias and the like. One other mental model to consider is hindsight bias, where, after an event occurs, in hindsight, there is a bias to see it as having been predictable even though there was no real objective basis on which it could have been predicted. Monday morning quarterbacking and hindsight is twenty-twenty are formulations of the same concept. Turn on the TV after any major event to see hindsight bias in action. Talking heads will explain why something occurred, and yet, if you had watched coverage before the event, you would not have found many predicting it ahead of time.

When experimental subjects are presented with various negligence scenarios, they typically rate an outcome as more foreseeable the worse the outcome is, even when the negligent act is the same. In other words, the worse the outcome, the worse the hindsight bias. In the context of leadership and learning new roles, hindsight bias can keep you from learning from past events. If you believe an event was predictable when it was not, you may take away that you made the wrong choices leading up to the event, when in reality you may have made the right choice given the information available at the time. For example, if you make an investment in a new technology or even personally in a stock or startup company, and it doesn’t work out, it doesn’t mean it wasn’t a good bet at the time.

For instance, when analyzing past investment decisions, you need to look at how your decision-making criteria applied to the winners and the losers as a whole, and not just to one of those subgroups, or else you may take away the wrong message. Another way to counteract hindsight bias is to take notes as events occur in real time. That way you have a more objective record of what happened and are not relying solely on potentially compromised recollections. Of course, literal recordings are the most objective record and are increasing in popularity. Some organizations record some meetings or produce structured notes, journalists record interviews with sources, and police are increasingly using body cams to document encounters. It is important to realize, though, that hindsight bias can affect you only in instances where the outcome could not be foreseen.


The Little Black Book of Decision Making by Michael Nicholas

Abraham Maslow, Airbnb, Albert Einstein, Apollo 13, call centre, classic study, clockwork universe, cognitive dissonance, Daniel Kahneman / Amos Tversky, Donald Trump, Frederick Winslow Taylor, hindsight bias, impulse control, James Dyson, late fees, Mahatma Gandhi, Nelson Mandela, Ralph Waldo Emerson, Richard Feynman, Richard Feynman: Challenger O-ring, scientific management, selection bias, Stephen Hawking

But once you are aware of this bias, you'll see it everywhere – from the immediate aftermath of the horrendous terrorist atrocities in Paris in November 2015, where the press began questioning how intelligence services had failed to anticipate the attacks as soon as the “facts” leading up to them began to emerge, to football supporters who believe they have far greater expertise at picking the team than the manager, to the times when we second-guess our own decisions: “I should have known not to take that job”, “I knew the housing market would collapse/go up”, “I should have known that he was being unfaithful to me”, “I knew that if I trusted her she'd hurt me”, “I should have listened to my intuition”, and on it goes … This “hindsight bias” refers to the tendency for uncertain outcomes to seem more likely once we know the outcome that has occurred. Because of it, we are prone to view what has already happened as relatively inevitable and obvious, not realising how the information about the outcome has affected us. One of the first psychologists to investigate hindsight bias was Baruch Fischoff who, together with Ruth Beyth, used President Richard Nixon's historically important 1972 diplomatic visits to China and Russia as the focus for a study.

Perhaps most importantly, hindsight severely reduces our ability to learn from past decisions. We'll look at why this is so important in the next couple of chapters. We are all susceptible to hindsight bias, but it can be very difficult to recognise what is happening. Running on Instinct Psychologists use the term heuristics to describe the unconscious mental shortcuts that we take to arrive at judgments or solve problems. To date, dozens of them have been identified; hindsight bias being just one example. When we are faced with difficult questions, high complexity or ambiguity, or a need for high speed, heuristics can help us to find answers or solutions that would otherwise be beyond conscious reach.

As you saw in the last chapter and elsewhere, however, the deeper level of truth is that it can be very hard to notice anything the mind is not looking for (remember my dad's bike in Chapter 10?). So, turning awareness of our hindsight bias into a practical reality requires considerable alertness – or present moment awareness – before there is any chance we'll consistently notice those times when the bias could be a problem. I might know that ice on the road makes driving dangerous, but if I can't spot the ice, that awareness is essentially useless. Training to overcome hindsight bias, or any other unconscious source of mental errors, needs to extend beyond understanding to also include mindful awareness, or mindfulness.


pages: 261 words: 70,584

Retirementology: Rethinking the American Dream in a New Economy by Gregory Brandon Salsbury

Alan Greenspan, Albert Einstein, asset allocation, Bear Stearns, behavioural economics, buy and hold, carried interest, Cass Sunstein, credit crunch, Daniel Kahneman / Amos Tversky, diversification, estate planning, financial independence, fixed income, full employment, hindsight bias, housing crisis, loss aversion, market bubble, market clearing, mass affluent, Maui Hawaii, mental accounting, mortgage debt, mortgage tax deduction, National Debt Clock, negative equity, new economy, RFID, Richard Thaler, risk tolerance, Robert Shiller, side project, Silicon Valley, Steve Jobs, the rule of 72, Yogi Berra

Such findings as these make a pretty good case for the buy-and-hold strategy of investing, but the stock market is only one part of a retirement planning strategy you could consider. There are any number of other ways you can accumulate a nest egg—all those ways simply have to fall within your risk tolerance and comfort zone. Such a stance can keep you from succumbing to a mind trick called hindsight bias. “People who experience hindsight bias misapply current hindsight to past foresight,” according to Hersh Shefrin in his book Beyond Greed and Fear. The previously held emotion may not have been terribly strong, but the subsequent experience can reinforce the emotion to the point where you think your premonition was just as strong.

Bigness bias—Whether it’s inflation or compound interest, people have a tendency to overlook small numbers such as 1% or 2%. However, over time, those numbers become big. So whether people are paying a small percentage per year on their credit card interest or earning small interest on an account, the overall sum that is paid or earned is actually very big. Hindsight bias—People often believe, after the fact, that some event was predictable and obvious when it was not predictable based on the information they had before the event took place. A person who’s unsure about making an investment might believe, after the investment goes up, that he did have the information ahead of time that told him that the investment would be a positive one.

Index A adjustable-rate mortgages (ARMs), 93-94 Against the Gods, The Remarkable Story of Risk (Bernstein), 13 Alt-A loans, 93 Alternative Minimum Tax (AMT), 133 The American Recovery and Reinvestment Act, 8 anchoring, 166, 169 Anderson, Brad, 4 Apple Computer, 46, 137 ARMs (adjustable-rate mortgages), 93-94 asset allocation, 75 attachment bias, 113-116 auto insurance, 159 automatic withholding, 57 automation, financial, 57 autos, spending boom on, 38 Avian flu, 18-19 B Becker, Lance, 89, 91 behavioral finance, 10-11 anchoring, 166, 169 attachment bias, 113-116 bigness bias, 180, 183 earned money versus found money, 116-117 effect of the human agent, 10 familiarity bias, 113-115 herding, 66, 70-72 hindsight bias, 180 house money effect, 86-89, 198 illusion of knowledge, 165-167 inheritance, 117-119 layering, 43-50 mental accounting, 42-45, 141-144 myopic loss aversion, 11-12, 66-70 number numbness, 180-182 overconfidence, 22, 26-27, 166-168 recommended reading, 13 regret and pride, 66-68 table of destructive financial behaviors, 197-199 wealth effect, 86-87 Belsky, Gary, 181 Benartzi, Shlomo, 13, 57, 114 beneficiary designations, 119-121 benefits coverage in retirement, determining, 171 Bernstein, Peter, 13 Best Buy, 4 Beyond Greed and Fear (Shefrin), 13, 74, 182 bias bigness bias, 180, 183 hindsight bias, 180 bigness bias, 180, 183 bird flu, 18 Blackstone, 39 Blake, David, 200 The Book Casino Managers Fear the Most!


pages: 257 words: 72,251

Nothing to Hide: The False Tradeoff Between Privacy and Security by Daniel J. Solove

Albert Einstein, cloud computing, Columbine, hindsight bias, illegal immigration, invention of the telephone, Marshall McLuhan, national security letter, Oklahoma City bombing, security theater, the medium is the message, Timothy McVeigh, traffic fines, urban planning

During the search, they find out about your religious or political beliefs, and they don’t like them. They also discover you’ve been betting on sports. They might arrest you for the illegal gambling as a pretext—just because they despise your beliefs. Hindsight Bias The timing of the warrant is crucial. It must be obtained before the government conducts the search. Why? The primary reason is hindsight bias. Suppose the police illegally search the home of a suspected terrorist and find various weapons. What judge is going to throw that evidence out because the police merely had a hunch when they did the search? Knowing the hunch turned out to be correct makes it very hard to question its validity.

And in circumstances where warrants truly are impractical, we must do more than just shove them aside; we must ensure that their key functions are achieved by other means. Why Require Warrants Supported by Probable Cause? Warrants supported by probable cause serve at least three critical functions. They limit police power and discretion, they restrict dragnet searches, and they prevent hindsight bias. Police Power and Discretion Warrants require a neutral and detached judge to decide whether a search is justified. They restrain police power. The police have a tremendous amount of discretion about when, where, and 126 The Suspicionless-Searches Argument how to search. They can enter your home, search through your things and your computer.

A warrant is kind of like a gamble. The police are saying there’s a decent likelihood they’ll find evidence of a crime, and the judge determines whether the odds are sufficiently good. Nobody knows yet how the bet will pan out. It’s very hard to make the same unbiased call when you know what happened. In psychology, hindsight bias is a well-recognized occurrence. It is sometimes referred to as the “I knew it all along” phenomenon. Countless studies have confirmed it. In a 1991 study, people were asked to predict whether Clarence Thomas would be confirmed to become a justice on the U.S. Supreme Court. Before the Senate vote, 58 percent predicted he’d be confirmed.


pages: 263 words: 75,455

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

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

The two pioneers of the field of behavioral finance, Daniel Kahneman and Amos Tversky, suggest that our overconfidence may stem from two other biases, self-attribution bias and hindsight bias.34 Self-attribution bias refers to our propensity to ascribe our successes to our skill, while blaming our failures on bad luck, rather than a lack of skill. For example, the stocks we buy that go up show our great stock picking skills, while those we buy that go down do so because of some outside factor, like Congress changing the law or the Federal Reserve increasing interest rates. If we do it often enough, we are led to the conclusion that we are skillful, which is as pleasant as it is wrong. Hindsight bias is the propensity to believe, after an event has occurred, that we predicted it before it happened.

Hindsight bias is the propensity to believe, after an event has occurred, that we predicted it before it happened. If, after watching some unlikely event unfold, you've ever said, “I knew that would happen,” when your reason for saying so was just some gut-feeling, you were subject to hindsight bias. The problem with hindsight bias is that if we think we predicted the past better than we actually did, we tend to believe that we can predict the future better than we actually can. A related bias is neglect of the base case. The bias manifests when we try to answer probabilistic questions like, “What is the probability that object A originates from class B?”

Availability bias Ayres, Ian Bachelier, Louis Bailey, Morris Bankruptcy prediction history of improving Batchelor, Roy Beat the Dealer (Thorp) Beat the Market: A Scientific Stock Market System (Thorp & Kassouf) Behavioral errors, quantitative investing's protection against cognitive biases experts' errors value investors'errors Behavioral Investing: A Practitioners Guide to Applying Behavioral Finance (Montier) Benchmarking Beneish, Messod Berk, Jonathan Bogue, Marcus Bonaime, Alice Book value-to-market capitalization ratio Brooks, Chris Buffett, Warren See's Candies acquisition Buybacks Campbell, John Cash flow on assets (CFOA) CGM Focus Fund Chava, Sudheer “The Checklist” (Gawande) The Checklist Manifesto: How to Get Things Right (Gawande) Chuvakhin, Nikolai Cloning Cognitive biases adjustment bias anchoring availability bias hindsight bias neglect of the base case overconfidence self-attribution bias Confirmation bias “Contrarian Investment, Extrapolation, and Risk” (Lakonishok, Schleifer, & Vishny) Cowles, Alfred, III “The Cross-Section of Expected Stock Returns” (Fama & French) Data mining “Decoding Inside Information” (Cohen, Malloy, & Pomorski) “Delisting Returns and Their Effect on Accounting-Based Market Anomalies” (Price, Beaver, & McNichols) Dumb money, paradox of behavioral errors, quantitative investing's protection against cognitive biases experts' errors value investors'errors quantitative value investing, power of value strategies Graham's quantitative Earnings manipulators and frauds, eliminating accruals detecting earnings manipulation PROBMs, predicting Enron Earnings yield Efficient market theory Einhorn, David Enron Enterprise yield (EBITDA and EBIT variations) Expert Political Judgment (Tetlock) Fama, Eugene Financial distress, measuring risk of bankruptcy prediction history of improving calculating universe, scrubbing Financial strength case study: Lubrizol Corporation comparing performance of F_SCORE and FS_SCORE financial strength score (FS_SCORE) current profitability formula and interpretation recent operational improvements stability Piotroski Fundamental Score (F_SCORE) analyzing formula and interpretation Fooled by Randomness (Taleb) Forward earnings estimate Franchises finding economic moats and excess returns persistence pricing power and big, stable margins See's Candies, acquisition by Buffett Fraud.


pages: 317 words: 100,414

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

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

When forecasts span months or years, the wait for a result allows the flaws of memory to creep in. You know how you feel now about the future. But as events unfold, will you be able to recall your forecast accurately? There is a good chance you won’t. Not only will you have to contend with ordinary forgetfulness, you are likely to be afflicted by what psychologists call hindsight bias. If you are old enough now to have been a sentient being in 1991, answer this question: Back then, how likely did you think it was that the incumbent president, George H. W. Bush (now known as Bush 41) would win reelection in 1992? We all know Bush 41 lost to Bill Clinton, but you may recall that he was popular after the victory in the Gulf War.

In this debate, each candidate heaps praise on his opponents while savaging himself—because Bush 41 was certain to crush whomever he faced. Everyone knew that. It’s why leading Democrats didn’t contest the nomination that year, clearing the way for the obscure governor of Arkansas, Bill Clinton. Once we know the outcome of something, that knowledge skews our perception of what we thought before we knew the outcome: that’s hindsight bias. Baruch Fischhoff was the first to document the phenomenon in a set of elegant experiments. One had people estimate the likelihood of major world events at the time of Fischhoff’s research—Will Nixon personally meet with Mao?—then recall their estimate after the event did or did not happen. Knowing the outcome consistently slanted the estimate, even when people tried not to let it sway their judgment.

On average, the experts recalled a number 31 percentage points higher than the correct figure. So an expert who thought there was only a 10% chance might remember herself thinking there was a 40% or 50% chance. There was even a case in which an expert who pegged the probability at 20% recalled it as 70%—which illustrates why hindsight bias is sometimes known as the “I knew it all along” effect. Forecasters who use ambiguous language and rely on flawed memories to retrieve old forecasts don’t get clear feedback, which makes it impossible to learn from experience. They are like basketball players doing free throws in the dark.


pages: 348 words: 83,490

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

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

In February 1994, the Federal Reserve Board raised rates. Citron’s response: “The recent increase in rates was not a surprise to us; we expected it and were prepared for it.” Now, there is a chance that Citron changed his view prior to the rate hike. But the much more plausible view is that he suffered from hindsight bias. Hindsight bias stands in the way of quality feedback—understanding how and why we made a particular decision. One antidote to this bias is to keep notes of why you make decisions as you make them. Those notes become a valuable source of objective feedback and can help sharpen future decision making. 16 Right from the Gut Investing with Naturalistic Decision Making People who make decisions for a living are coming to realize that in complex or chaotic situations—a battlefield, a trading floor, or today’s brutally competitive business environment—intuition usually beats rational analysis.

., we have bounded rationality), and any action that deviates from rationality in human interactions ignites speculation about how others will behave.1 In other words, if no one else is rational, it doesn’t pay for you to be. The second facet of expectations is that after an event occurs, humans tend to overestimate their pre-event knowledge of the outcome. This hindsight bias erodes the quality of the feedback we need to sharpen our analytical skills. Speculation and Enterprise Keynes divides the basis for expectations of future returns (he uses the word “yield”) into two parts: facts that are more or less certain, and events that you can forecast with varying degrees of confidence.

But when diversity is jeopardized—which it frequently is—markets depart significantly from the underlying fundamentals. Kidding Yourself A discussion of expectation is not complete without noting an odd human feature: once an event has passed, we tend to believe that we had better knowledge of the outcome before the fact than we really did. Known as hindsight bias, or more commonly the Monday-morning-quarterback syndrome, this research shows that people are not very good at recalling the way an uncertain situation appeared to them before finding out the results.9 Finance professor Hersh Shefrin illustrates the point by analyzing the comments of a former Orange County treasurer, Robert Citron.10 In his annual report dated September 1993, Citron wrote, “We will have level if not lower interest rates through this decade.


Global Catastrophic Risks by Nick Bostrom, Milan M. Cirkovic

affirmative action, agricultural Revolution, Albert Einstein, American Society of Civil Engineers: Report Card, anthropic principle, artificial general intelligence, Asilomar, availability heuristic, backpropagation, behavioural economics, Bill Joy: nanobots, Black Swan, carbon tax, carbon-based life, Charles Babbage, classic study, cognitive bias, complexity theory, computer age, coronavirus, corporate governance, cosmic microwave background, cosmological constant, cosmological principle, cuban missile crisis, dark matter, death of newspapers, demographic transition, Deng Xiaoping, distributed generation, Doomsday Clock, Drosophila, endogenous growth, Ernest Rutherford, failed state, false flag, feminist movement, framing effect, friendly AI, Georg Cantor, global pandemic, global village, Great Leap Forward, Gödel, Escher, Bach, Hans Moravec, heat death of the universe, hindsight bias, information security, Intergovernmental Panel on Climate Change (IPCC), invention of agriculture, Kevin Kelly, Kuiper Belt, Large Hadron Collider, launch on warning, Law of Accelerating Returns, life extension, means of production, meta-analysis, Mikhail Gorbachev, millennium bug, mutually assured destruction, Nick Bostrom, nuclear winter, ocean acidification, off-the-grid, Oklahoma City bombing, P = NP, peak oil, phenotype, planetary scale, Ponzi scheme, power law, precautionary principle, prediction markets, RAND corporation, Ray Kurzweil, Recombinant DNA, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, Singularitarianism, social intelligence, South China Sea, strong AI, superintelligent machines, supervolcano, synthetic biology, technological singularity, technoutopianism, The Coming Technological Singularity, the long tail, The Turner Diaries, Tunguska event, twin studies, Tyler Cowen, uranium enrichment, Vernor Vinge, War on Poverty, Westphalian system, Y2K

A society subject to regular minor hazards will treat those minor hazards as an upper bound on the size of the risks (guarding against regular minor floods but not occasional major floods). Risks of human extinction may tend to be underestimated since, obviously, humanity has never yet encountered an extinction event. 1 5.3 Hindsight bias Hindsight bias is when subjects, after learning the eventual outcome, give a much higher estimate for the predictability of that outcome than subjects who predict the outcome without advance knowledge. Hindsight bias is sometimes called the !-knew-it-all-along effect. Fischhoff and Beyth ( 1 975) presented students with historical accounts of unfamiliar incidents such as a conflict between the Gurkhas and the British in 1814.

As many as 76% of the control group concluded the flood was so unlikely that no precautions were necessary; 57% of the experimental group concluded the flood was so likely that failure to take precautions was legally negligent. A third experimental group was told the outcome and also explicitly instructed to avoid hindsight bias, which made no difference: 5 6% concluded the city was legally negligent. Judges cannot simply instruct juries to avoid hindsight bias; that debiasing manipulation has no significant effect. When viewing history through the lens of hindsight, we vastly underestimate the cost of preventing catastrophe. In 1986, the space shuttle Challenger exploded for reasons eventually traced to an 0-ring losing flexibility at low temperature (Rogers et al., 1 986).

People are surprised by catastrophes lying outside their anticipation, beyond their historical probability distributions. Then why are we so taken aback when Black Swans occur? Why did LTCM borrow a leverage of $125 billion against $4.72 billion of equity, almost ensuring that any Black Swan would destroy them? Because of hindsight bias, we learn overly specific lessons. After September 1 1 , the U S Federal Aviation Administration prohibited box-cutters on airplanes. The hindsight bias rendered the event too predictable in retrospect, permitting the angry victims to find it the result of 'negligence' such as intelligence agencies' failure to distinguish warnings of AI Qaeda activity amid a thousand other warnings.


pages: 327 words: 103,336

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

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

This tendency, which psychologists call creeping determinism, is related to the better-known phenomenon of hindsight bias, the after-the-fact tendency to think that we “knew it all along.” In a variety of lab experiments, psychologists have asked participants to make predictions about future events and then reinterviewed them after the events in question had taken place. When recalling their previous predictions, subjects consistently report being more certain of their correct predictions, and less certain of their incorrect predictions, than they had reported at the time they made them. Creeping determinism, however, is subtly different from hindsight bias and even more deceptive.

Creeping determinism, however, is subtly different from hindsight bias and even more deceptive. Hindsight bias, it turns out, can be counteracted by reminding people of what they said before they knew the answer or by forcing them to keep records of their predictions. But even when we recall perfectly accurately how uncertain we were about the way events would transpire—even when we concede to have been caught completely by surprise—we still have a tendency to treat the realized outcome as inevitable. Ahead of time, for example, it might have seemed that the surge was just as likely to have had no effect as to lead to a drop in violence. But once we know that the drop in violence is what actually happened, it doesn’t matter whether or not we knew all along that it was going to happen (hindsight bias).

But once we know that the drop in violence is what actually happened, it doesn’t matter whether or not we knew all along that it was going to happen (hindsight bias). We still believe that it was going to happen, because it did.3 SAMPLING BIAS Creeping determinism means that we pay less attention than we should to the things that don’t happen. But we also pay too little attention to most of what does happen. We notice when we just miss the train, but not all the times when it arrives shortly after we do. We notice when we unexpectedly run into an acquaintance at the airport, but not all the times when we do not. We notice when a mutual fund manager beats the S&P 500 ten years in a row or when a basketball player has a “hot hand” or when a baseball player has a long hitting streak, but not all the times when fund managers and sportsmen alike do not display streaks of any kind.


pages: 198 words: 53,264

Big Mistakes: The Best Investors and Their Worst Investments by Michael Batnick

activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, AOL-Time Warner, asset allocation, Bear Stearns, behavioural economics, bitcoin, Bretton Woods, buy and hold, buy low sell high, Carl Icahn, cognitive bias, cognitive dissonance, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, endowment effect, financial engineering, financial innovation, fixed income, global macro, hindsight bias, index fund, initial coin offering, invention of the wheel, Isaac Newton, Jim Simons, John Bogle, John Meriwether, Kickstarter, Long Term Capital Management, loss aversion, low interest rates, Market Wizards by Jack D. Schwager, mega-rich, merger arbitrage, multilevel marketing, Myron Scholes, Paul Samuelson, Pershing Square Capital Management, quantitative easing, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Thaler, Robert Shiller, short squeeze, Snapchat, Stephen Hawking, Steve Jobs, Steve Wozniak, stocks for the long run, subprime mortgage crisis, transcontinental railway, two and twenty, value at risk, Vanguard fund, Y Combinator

“If you just observe these people, they know the right thing to do…. But when they actually get into the game, they start reacting to the outcomes of the previous rounds.” Humans come preprogrammed with something called hindsight bias. It's a defect in our software that falsely leads us to believe we knew what was going to happen all along, when in reality we had no clue. Hindsight bias leads to regret, and regret leads to poor decision making. Regret steers our brain in two distinct ways: We do nothing out of fear that we'll make the wrong decision. “I'm going to hold onto this fund that's done horribly because I can't stand the thought of selling at the bottom,” and it can compel us to do something because we don't want to regret not doing it: “I'm going to buy this ICO (initial coin offering) because I won't be able to live with myself if I miss the next Bitcoin.”

This book aims to help the reader relate to some of their blunders and understand that temporary setbacks have knocked on all of our doors. All investors, from Peter Lynch to the average Joe, are hard‐wired with human emotions. We're risk averse, we anchor to our purchase point, and we're all manipulated by hindsight bias. And when we experience failure, usually it's self‐inflicted, which makes dealing with it objectively a very daunting task. Difficult as it is, we must figure out how to prevent previous mistakes from interfering with future decisions. People typically strive to replicate success. Kobe Bryant studied Michael Jordan and Paul Tudor Jones studied Jesse Livermore.

., 111 Hartford Accident Insurance Company (Twain investment), 28 Hartford Courant (Hawley ownership), 29 Hawking, Stephen, 37 Hawkins, Gregory, 39 Hawley, Joseph Roswell, 29 Heath, Thomas, 114 “Hedge Fund Miseries” (Steinhardt), 59 Heinze, Augustus/Otto, 19 Hemingway, Ernest, 28 Herbalife Ackman crusade, 3, 90–92 FTC charges, 93–94 sales, 91 stock, increase, 92 Heuristics, dangers, 16 H.H. Brown, Berkshire purchase, 79–81 High‐frequency trading Graham recognition, 7 Hilibrand, Lawrence, 39 Hindsight bias, 148 Hong Kong Land & Development Authority, investments, 40 Housing bubble, 132–133 Huckleberry Finn (Twain), 28 Human beings, motivation, 147 Hutton, Edward, 17 IBM investment, 50 shareholder wealth, 109 trading level, 70 Icahn, Carl, 92 Ignorance, Confidence, and Filthy Rich Friends, (Krass), 27–28 Index funds creation, 47, 52 momentum, 47 “Inflated Treasuries and Deflated Stockholders,” 8 Information impact, 119–120 processing inability, 87 Initial coin offering (ICO), 148 Insolvency, rules, 31 Instagram, Sacca investment, 149 Insurance payments, problems, 134 Intelligent Investor, The, (Graham), 4, 6, 121 Interest rates, Federal Reserve increase, 61 International bonds, US bonds (spreads), 41 Internet stocks, overvaluation, 104 Intrinsic value, determination, 5 Inverse ETF, purchase, 23 Investing purpose, 119 risk management, 23 uncertainty, 23 Investment values, 6 Investors appraisal, 6 cognitive/emotional biases, 5–6 expectations, 120 information usage, 87 preference, change, 47 searches, 5 Irrational confidence, 161 Irrational Exuberance (Shiller), 126 Irving, Henry, 30 Ivest Fund, 49–50 J.C.


pages: 319 words: 106,772

Irrational Exuberance: With a New Preface by the Author by Robert J. Shiller

Alan Greenspan, Andrei Shleifer, asset allocation, banking crisis, benefit corporation, Benoit Mandelbrot, book value, business cycle, buy and hold, computer age, correlation does not imply causation, Daniel Kahneman / Amos Tversky, demographic transition, diversification, diversified portfolio, equity premium, Everybody Ought to Be Rich, experimental subject, hindsight bias, income per capita, index fund, Intergovernmental Panel on Climate Change (IPCC), Joseph Schumpeter, Long Term Capital Management, loss aversion, Mahbub ul Haq, mandelbrot fractal, market bubble, market design, market fundamentalism, Mexican peso crisis / tequila crisis, Milgram experiment, money market fund, moral hazard, new economy, open economy, pattern recognition, Phillips curve, Ponzi scheme, price anchoring, random walk, Richard Thaler, risk tolerance, Robert Shiller, Ronald Reagan, Small Order Execution System, spice trade, statistical model, stocks for the long run, Suez crisis 1956, survivorship bias, the market place, Tobin tax, transaction costs, tulip mania, uptick rule, urban decay, Y2K

One theory has been that, in evaluating the soundness of their conclusions, people tend to evaluate the probability that they are right on only the last step of their reasoning, forgetting how many other elements of their reasoning could be wrong.14 Another theory is that people make probability judgments by looking for similarities to other known observations, and they forget that there are many other possible observations with which they could compare.15 The reason for overconfidence may also have to do with hindsight bias, a tendency to think that one would have known actual events were coming before they happened, had one been present then or had reason to pay attention.16 Hindsight bias encourages a view of the world as more predictable than it really is. Another factor in overconfidence as it relates to speculative markets is magical thinking. When we speak of people’s intuition about the likelihood that investments will do well or poorly and their own decisions to invest, we are speaking of their innermost thoughts—thoughts that they do not have to explain or justify to others.

I received 605 completed responses from individual investors 248 NOT ES TO PAGES 90–99 and 284 completed responses from institutional investors. See Shiller, Market Volatility, pp. 379–402, for the analysis of the results that I wrote in November 1987. 19. Of course, since the questionnaire was filled out after the crash, part of this reported concern with overpricing may have been due to hindsight bias. Indeed we cannot completely trust even the self-categorization, into buyers versus sellers on October 19, that respondents made on the questionnaire. The anonymity of the questionnaires, the plea for truthfulness, and the stated purpose of the questionnaire as a tool for scientific research on the crash should all have helped to provide us with more nearly objective answers, but of course no survey results can be trusted completely. 20.

See Allan Collins, Eleanor Warnock, Nelleke Acello, and Mark L. Miller, “Reasoning from Incomplete Knowledge,” in Daniel G. Bobrow and Allan Collins (eds.), Representation and Understanding: Studies in Cognitive Science (New York: Academic Press, 1975), pp. 383–415. 16. See Dagmar Strahlberg and Anne Maass, “Hindsight Bias: Impaired Memory or Biased Reconstruction,” European Review of Social Psychology, 8 (1998): 105–32. 17. See E. J. Langer, “The Illusion of Control,” Journal of Personality and Social Psychology, 32 (1975): 311–28; see also G. A. Quattrone and Amos Tversky, “Causal versus Diagnostic Contingencies: On Self-Deception and the Voter’s Delusion,” Journal of Personality and Social Psychology, 46(2) (1984): 237–48. 18.


pages: 500 words: 145,005

Misbehaving: The Making of Behavioral Economics by Richard H. Thaler

3Com Palm IPO, Alan Greenspan, Albert Einstein, Alvin Roth, Amazon Mechanical Turk, Andrei Shleifer, Apple's 1984 Super Bowl advert, Atul Gawande, behavioural economics, Berlin Wall, Bernie Madoff, Black-Scholes formula, book value, business cycle, capital asset pricing model, Cass Sunstein, Checklist Manifesto, choice architecture, clean water, cognitive dissonance, conceptual framework, constrained optimization, Daniel Kahneman / Amos Tversky, delayed gratification, diversification, diversified portfolio, Edward Glaeser, endowment effect, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, experimental economics, Fall of the Berlin Wall, George Akerlof, hindsight bias, Home mortgage interest deduction, impulse control, index fund, information asymmetry, invisible hand, Jean Tirole, John Nash: game theory, John von Neumann, Kenneth Arrow, Kickstarter, late fees, law of one price, libertarian paternalism, Long Term Capital Management, loss aversion, low interest rates, market clearing, Mason jar, mental accounting, meta-analysis, money market fund, More Guns, Less Crime, mortgage debt, Myron Scholes, Nash equilibrium, Nate Silver, New Journalism, nudge unit, PalmPilot, Paul Samuelson, payday loans, Ponzi scheme, Post-Keynesian economics, presumed consent, pre–internet, principal–agent problem, prisoner's dilemma, profit maximization, random walk, randomized controlled trial, Richard Thaler, risk free rate, Robert Shiller, Robert Solow, Ronald Coase, Silicon Valley, South Sea Bubble, Stanford marshmallow experiment, statistical model, Steve Jobs, sunk-cost fallacy, Supply of New York City Cabdrivers, systematic bias, technology bubble, The Chicago School, The Myth of the Rational Market, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, transaction costs, ultimatum game, Vilfredo Pareto, Walter Mischel, zero-sum game

There he had worked with two guys whose names I had never heard: Daniel Kahneman and Amos Tversky. Baruch told me about his now-famous thesis on “hindsight bias.” The finding is that, after the fact, we think that we always knew the outcome was likely, if not a foregone conclusion. After the virtually unknown African American senator Barack Obama defeated the heavily favored Hillary Clinton for the Democratic Party presidential nomination, many people thought they had seen it coming. They hadn’t. They were just misremembering. I found the concept of hindsight bias fascinating, and incredibly important to management. One of the toughest problems a CEO faces is convincing managers that they should take on risky projects if the expected gains are high enough.

One of the toughest problems a CEO faces is convincing managers that they should take on risky projects if the expected gains are high enough. Their managers worry, for good reason, that if the project works out badly, the manager who championed the project will be blamed whether or not the decision was a good one at the time. Hindsight bias greatly exacerbates this problem, because the CEO will wrongly think that whatever was the cause of the failure, it should have been anticipated in advance. And, with the benefit of hindsight, he always knew this project was a poor risk. What makes the bias particularly pernicious is that we all recognize this bias in others but not in ourselves.

In order to get managers to be willing to take risks, it is necessary to create an environment in which those managers will be rewarded for decisions that were value-maximizing ex ante, that is, with information available at the time they were made, even if they turn out to lose money ex post. Implementing such a policy is made difficult by hindsight bias. Whenever there is a time lapse between the times when a decision is made and when the results come in, the boss may have trouble remembering that he originally thought it was a good idea too. The bottom line is that in many situations in which agents are making poor choices, the person who is misbehaving is often the principal, not the agent.


pages: 654 words: 191,864

Thinking, Fast and Slow by Daniel Kahneman

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

Asked to reconstruct their former beliefs, people retrieve their current ones instead—an instance of substitution—and many cannot believe that they ever felt differently. Your inability to reconstruct past beliefs will inevitably cause you to underestimate the extent to which you were surprised by past events. Baruch Fischh off first demonstrated this “I-knew-it-all-along” effect, or hindsight bias, when he was a student in Jerusalem. Together with Ruth Beyth (another of our students), Fischh off conducted a survey before President Richard Nixon visited China and Russia in 1972. The respondents assigned probabilities to fifteen possible outcomes of Nixon’s diplomatic initiatives. Would Mao Zedong agree to meet with Nixon?

Further experiments showed that people were driven to overstate the accuracy not only of their original predictions but also of those made by others. Similar results have been found for other events that gripped public attention, such as the O. J. Simpson murder trial and the impeachment of President Bill Clinton. The tendency to revise the history of one’s beliefs in light of what actually happened produces a robust cognitive illusion. Hindsight bias has pernicious effects on the evaluations of decision makers. It leads observers to assess the quality of a decision not by whether the process was sound but by whether its outcome was good or bad. Consider a low-risk surgical intervention in which an unpredictable accident occurred that caused the patient’s death.

One group was shown only the evidence available at the time of the city’s decision; 24% of these people felt that Duluth should take on the expense of hiring a flood monitor. The second group was informed that debris had blocked the river, causing major flood damage; 56% of these people said the city should have hired the monitor, although they had been explicitly instructed not to let hindsight distort their judgment. The worse the consequence, the greater the hindsight bias. In the case of a catastrophe, such as 9/11, we are especially ready to believe that the officials who failed to anticipate it were negligent or blind. On July 10, 2001, the Central Intelligence Agency obtained information that al-Qaeda might be planning a major attack against the United States.


pages: 111 words: 1

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

Alan Greenspan, Antoine Gombaud: Chevalier de Méré, availability heuristic, backtesting, behavioural economics, Benoit Mandelbrot, Black Swan, commoditize, complexity theory, corporate governance, corporate raider, currency peg, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, endowment effect, equity premium, financial engineering, fixed income, global village, hedonic treadmill, hindsight bias, junk bonds, Kenneth Arrow, Linda problem, Long Term Capital Management, loss aversion, mandelbrot fractal, Mark Spitznagel, Market Wizards by Jack D. Schwager, mental accounting, meta-analysis, Michael Milken, Myron Scholes, PalmPilot, Paradox of Choice, Paul Samuelson, power law, proprietary trading, public intellectual, quantitative trading / quantitative finance, QWERTY keyboard, random walk, Richard Feynman, risk free rate, road to serfdom, Robert Shiller, selection bias, shareholder value, Sharpe ratio, Steven Pinker, stochastic process, survivorship bias, too big to fail, Tragedy of the Commons, Turing test, Yogi Berra

Psychologists call this overestimation of what one knew at the time of the event due to subsequent information the hindsight bias, the “I knew it all along” effect. Now the civil servant called the trades that ended up as losers “gross mistakes,” just like journalists call decisions that end up costing a candidate his election a “mistake.” I will repeat this point until I get hoarse: A mistake is not something to be determined after the fact, but in the light of the information until that point. A more vicious effect of such hindsight bias is that those who are very good at predicting the past will think of themselves as good at predicting the future, and feel confident about their ability to do so.

As I increasingly started living this book after the initial composition, I found luck in the most unexpected of places. It is as if there were two planets: the one in which we actually live and the one, considerably more deterministic, on which people are convinced we live. It is as simple as that: Past events will always look less random than they were (it is called the hindsight bias). I would listen to someone’s discussion of his own past realizing that much of what he was saying was just backfit explanations concocted ex post by his deluded mind. This became at times unbearable: I could feel myself looking at people in the social sciences (particularly conventional economics) and the investment world as if they were deranged subjects.

We gave them names such as the “I’m as good as my last trade” effect, the “sound-bite effect,” the “Monday morning quarterback” heuristic, and the “It was obvious after the fact” effect. It was both vindicating for traders’ pride and disappointing to discover that they existed in the heuristics literature as the “anchoring,” the “affect heuristic,” and the “hindsight bias” (it makes us feel that trading is true, experimental scientific research). The correspondence between the two worlds is shown in Table 11.1. I start with the “I’m as good as my last trade” heuristic (or the “loss of perspective” bias)—the fact that the counter is reset at zero and you start a new day or month from scratch, whether it is your accountant who does it or your own mind.


pages: 199 words: 48,162

Capital Allocators: How the World’s Elite Money Managers Lead and Invest by Ted Seides

Albert Einstein, asset allocation, behavioural economics, business cycle, coronavirus, COVID-19, crowdsourcing, data science, deliberate practice, diversification, Everything should be made as simple as possible, fake news, family office, fixed income, high net worth, hindsight bias, impact investing, implied volatility, impulse control, index fund, Kaizen: continuous improvement, Lean Startup, loss aversion, Paradox of Choice, passive investing, Ralph Waldo Emerson, risk tolerance, Sharpe ratio, sovereign wealth fund, tail risk, The Wisdom of Crowds, Toyota Production System, zero-sum game

All investors have a self-serving bias that wants to take credit when good things happen and defer blame when bad outcomes arise. It takes System 2 thinking to investigate when a bad outcome came out of a good decision process, and is even harder to recognize when dumb luck caused a good outcome to arise from a bad process. Hindsight bias gets in the way of accurately assessing past decisions. Investors subconsciously change the facts that led to decisions. Allocators are intelligent and thoughtful, which unfortunately only serves to strengthen their resolve. The smarter they are, the better they are at rationalizing existing beliefs, perfecting motivated reasoning, and sticking to opinions.

Documenting decisions, creating a decision group, and considering the role of luck are useful tools to build feedback loops in the decision process. 1. Decision journal Improving the investment process requires an accurate statement of the facts and beliefs known at the time the decision gets made. Hindsight bias will cloud our memory of what we knew. A decision journal ensures a CIO can learn and grow from a review of past decisions. Part of my conviction in the quality of my decision to bet with Buffett comes from a white paper I wrote at the time of the bet. I based my assessment of the probability of success on an outside view of the wager – the S&P 500 had never before outperformed the hedge fund index over a ten-year period.

C: Communism – share all the data transparently, especially information that paints a view in a bad light. U: Universalism – seek the objective truth. Pay attention to the bias to overweight or dismiss facts depending on who is communicating the message. D: Disinterested – note our emotional conflicts of interest. Be mindful of motivated reasoning, confirmation bias, and hindsight bias. OS: Objective Skepticism – approach the world asking why things aren’t true. 3. Role of luck As the skill of managers increased over time, luck has become more important in outcomes. The dynamic pointed out by Michael Mauboussin has permeated investing just as it did with Major League Baseball hitters over the last few decades.


Infotopia: How Many Minds Produce Knowledge by Cass R. Sunstein

affirmative action, Andrei Shleifer, availability heuristic, behavioural economics, Build a better mousetrap, c2.com, Cass Sunstein, cognitive bias, cuban missile crisis, Daniel Kahneman / Amos Tversky, Edward Glaeser, en.wikipedia.org, feminist movement, framing effect, Free Software Foundation, hindsight bias, information asymmetry, Isaac Newton, Jean Tirole, jimmy wales, market bubble, market design, minimum wage unemployment, prediction markets, profit motive, rent control, Richard Stallman, Richard Thaler, Robert Shiller, Ronald Reagan, Savings and loan crisis, slashdot, stem cell, systematic bias, Ted Sorensen, the Cathedral and the Bazaar, The Wisdom of Crowds, winner-take-all economy

But in groups with diverse views, people quickly learn that their own position is not universally held, and hence the bias is reduced. In these cases, group deliberation supplies an important corrective. Or consider the hindsight bias: people’s tendency to believe, falsely but with the benefit of hindsight, that they would have accurately predicted the outcome of an event (an accident, a natural disaster, an illness, a change in the stock market). Compared to individuals, groups are slightly less susceptible to hindsight bias.18 Apparently, group members who are not susceptible to that bias are able to persuade others that it is indeed a bias. But the broader point is that with group discussion, individual errors are usually propagated rather than eliminated, and amplification of mistakes is quite likely.

Stasser and Dietz-Uhler, “Collective Choice, Judgment, and Problem Solving,” 48. 242 / Notes to Pages 76–79 17. Personal communication with Reid Hastie, University of Chicago Business School (July 24, 2004), who has conducted experiments on this issue for many years. 18. See generally Dagmar Stahlberg et al., “We Knew It All Along: Hindsight Bias in Groups,” Organizational Behavior and Human Decision Processes 63 (1995): 46. 19. MacCoun, “Comparing Micro and Macro Rationality,” 124 (emphasis omitted). 20. See Stasser and Titus, “Hidden Profiles,” 304, 306–13 (discussing hidden profile experiments). 21. Daniel Gigone and Reid Hastie, “The Common Knowledge Effect: Information Sharing and Group Judgments,” Journal of Personality and Social Psychology 65 (1993): 971–73 (explaining hidden profiles by reference to common knowledge effect). 22.

See statistical groups team players, 201, 204 See also deliberation; social pressures groupthink, 12–13, 67, 223–24 Guantánamo Bay detainees, 6 Gulick, Luther, 202–3 Guthrie, Woody, 164, 166 Habermas, Jürgen, 11, 49, 71–72 hackers, 170, 173 Hanson, Robin, 104 Hargittai, Eszter, 190 Hayek, Friedrich, 216, 224 blogs and, 186, 196 dispersed information and, 118– 21, 130, 137 prediction markets and, 17, 118– 27, 130, 135, 137, 141 price system theory of, 14–15, 17, 120–21, 126, 127, 129, 130, 135, 137, 159, 173, 186, 197, 221 Wikipedia project and, 156–57, 159 herd behavior, 129, 141, 224 heuristics, 75–79 Hewitt, Hugh, 182, 183–84 Hewlett-Packard, 112–13, 117, 131, 173 Hicks, Angie, 192 hidden profiles, 124, 163, 224 blogs and, 186, 223 deliberation and, 17, 81–88, 100– 101, 102, 203, 204, 205, 210, 212 hindsight bias, 80 Index / 265 Hollywood Stock Exchange, 111–12 Homeland Security Department, U.S., 214 homogeneity, deliberative group, 46, 55 Hooke, Robert, 217 horse races, 112, 139–40 House of Representatives, U.S., 27 HP. See Hewlett-Packard HSX (Hollywood Stock Exchange), 111–12 hurricane futures market, 118 Hurricane Katrina, 76 Hussein, Saddam, 29 IBM, 173 “ideal speech situation,” 72 identity, group-related, 65, 79, 95– 96 IEM.


pages: 624 words: 127,987

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

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

Looking for disconfirming information is uncomfortable, but it’s useful, whatever you ultimately decide. SHARE THIS CONCEPT: http://book.personalmba.com/confirmation-bias/ Hindsight Bias Finish each day and be done with it. You have done what you could. Some blunders and absurdities no doubt crept in; forget them as soon as you can. Tomorrow is a new day; begin it well and serenely and with too high a spirit to be encumbered with your old nonsense. —RALPH WALDO EMERSON, ESSAYIST AND POET How do you feel when you realize that you’ve made a mistake? Hindsight Bias is the natural tendency to kick yourself for things you “should have known.” If you lose your job, you “should have known it was coming.”

Changing the past is outside of your Locus of Control (discussed later), so there’s no sense in wasting energy on self-doubt, wondering what might have been. Hindsight Bias becomes destructive if you negatively judge yourself or others for not knowing the unknowable. As the saying goes, “Hindsight is 20-20.” Reinterpret your past mistakes in a constructive light, and focus your energy on what you can do right now to move in a positive direction. SHARE THIS CONCEPT: http://book.personalmba.com/hindsight-bias/ Performance Load If not controlled, work will flow to the competent man until he submerges. —CHARLES BOYLE, FORMER U.S.

See Working with others Growth mind-set Guiding structure, for mental/physical health Gunaratana, Henepola Bhante Guthy-Renker Habits Hansson, David Heinemeier Health and energy cycles guidelines and modern world Hedging Hero’s Journey Hierarchy of funding Hindsight bias HiPPO rules Honesty, analytical Hook, creating Hope Diamond Human drives. See also Drives, human Human mind. See Mind and behavior Human performance, versus scalability Humanization IKEA Improvements and accumulation and amplification innovation versus competition Incentive-caused bias Incremental augmentation Inflows Influence, recommended reading Ingram, Mark Inhibition, mental Initial public offering Innovation, versus competition Insurance business, requirements of defined lifetime value Interdependence, in systems Intermediary distribution Internet as distraction, avoiding and duplication Interpretation, mental Iteration cycle and feedback and incremental augmentation and iteration velocity WIGWAM method Jobs, Steve Jones, Daniel T.


pages: 190 words: 53,409

Success and Luck: Good Fortune and the Myth of Meritocracy by Robert H. Frank

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, Alan Greenspan, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, attribution theory, availability heuristic, behavioural economics, Branko Milanovic, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, carried interest, Daniel Kahneman / Amos Tversky, David Brooks, deliberate practice, en.wikipedia.org, endowment effect, experimental subject, framing effect, full employment, Gary Kildall, high-speed rail, hindsight bias, If something cannot go on forever, it will stop - Herbert Stein's Law, income inequality, invisible hand, labor-force participation, lake wobegon effect, loss aversion, low interest rates, meritocracy, minimum wage unemployment, Network effects, Paradox of Choice, Paul Samuelson, Report Card for America’s Infrastructure, Richard Thaler, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Rory Sutherland, selection bias, side project, sovereign wealth fund, Steve Jobs, the long tail, The Wealth of Nations by Adam Smith, Tim Cook: Apple, ultimatum game, Vincenzo Peruggia: Mona Lisa, winner-take-all economy

To accomplish that goal, the steps we need to take are not intrusive, nor do they require additional layers of bureaucracy. But we’ll be unlikely to take those steps if too many people feel certain they can’t work. 2 WHY SEEMINGLY TRIVIAL RANDOM EVENTS MATTER Psychologists use the term “hindsight bias” to describe the human tendency to think that events are more predictable than they are. In the late 1940s, the sociologist Paul Lazarsfeld staged a vivid demonstration of the phenomenon by describing a study purporting to have found that World War II soldiers from rural areas were much better able than their urban counterparts to cope with the demands of military life.1 Just as Lazarsfeld suspected, people who read the results of this study found them completely unsurprising: Of course the grueling lives led by rural men would make them much better equipped to endure wartime stresses!

The actual study found the reverse: It was the soldiers from urban areas who fared much better in the military. Lazarsfeld’s point was that when you think you already know what happened, it’s easy to invent reasons for why it had to happen. Extending Lazarsfeld’s work, the sociologist Duncan Watts has argued that hindsight bias operates with particular force when people observe unusually successful outcomes.2 The problem, he suggested, is that it’s almost always easy to create a narrative after the fact that portrays such outcomes as having been inevitable. Yet every event is the outcome of a complex and interwoven sequence of steps, each of which depends on those preceding it.

., 51 Gilligan, Vince, 24, 31 Gilovich, Tom, 1, 80, 131 Gladwell, Malcolm, 33, 36 globalization, 54 Godfather, The, 23 Goff, Rick, xiv golden opportunity, 17, 109, 127, 130 Graf, Steffi, 45 Gramlich, Edward M., 27 gratitude, 98–103 Great Recession, the, 124, 134 Gross, Terry, 5 H&R Block, 43 Harvard University, 34, 36, 48, 72, 136 headwinds, 63, 64, 80, 81 height, 8 Hewlett-Packard, 53 High School Baseball Web, 62 high-speed rail, 87 hindsight bias, 21 Homo economicus, 129 hostile takeover litigation, 36 human capital, 40, 66 Huo, Yuezhou, 95 Huxley, Aldous, vii IBM, 34, 35, 51 Ice King, 37 income inequality, 52–55, 112, 113; and bankruptcy rates, 114, 115; and divorce rates, 114, 115; and government stimulus policy, 162, 163; and hours worked, 115; and long commute times, 114, 115; and spending by the wealthy, 165 individual vs. collective incentives, 17, 110, 117, 169 infrastructure, 12, 18, 87, 90, 91, 98, 111, 119, 120, 124, 147, 162 jealousy, 122 Johnson, Harold, 134–41 Journal of Political Economy, 28 JVC, 44 Kahneman, Daniel, 28, 70 Kardashian, Kim, 9 keeping up with the Joneses, 112 Keillor, Garrison, 72 Kildall, Gary, 34–36 Koble, Amy, 102 Koufax, Sandy, 142 Kristof, Nicholas, xiv, xv Krueger, Alan, 8 LaBelle, Patti, 103 Lake Wobegon Effect, 72 Landier, Augustin, 50 Langone, Kenneth, 104 last-name effects, 39 Lazarsfeld, Paul, 21 Leonard, Elmore, 5 Leslie, Ian, 22 Lewis, Michael, xii, xiii, xv, xvi Liar’s Poker, xiii liberals, xi, 17, 83 Little League baseball, 142 Lockdown, 30 Locke, John, 96 Lokkins, Elmer, 106 London School of Economics, 4 Long Tail, The, 47 lost-envelope thought experiment, 130 lottery winners, 69, 72 Louvre, the, 22 Major League Baseball, 62, 141 Manove, Michael, 74 markets for classical music, 46, 47 Marshall, Alfred, 41 Martin, Brett, 31 material living standards, 14, 90 Matthew Effect, 24 Mauboussin, Michael, 69 McCullough, Michael, 102 Mechanical Turk, 95, 137 meritocracy, xi, xii Merton, Robert K., 24 Mialon, Hugo, 14 Microsoft, 34, 35, 44 Milanovic, Branko, 7 Mlodinow, Leonard, 35 Mona Lisa, 9, 22–23 Morocco, 87 motivated cognition, 72 MS DOS, 35 Munger, Charlie, 39 Murphy, Liam, 97 Music Lab, 30, 45 Nagel, Thomas, 97 naïve optimism, 11, 12, 70–72, 75 National Center for Education Statistics, 87 National Institutes of Health, 135 natural selection, 73, 116 natural stupidity, 70 Nepal, 7, 14, 86, 112 Nepotist, The, 30, 49 Netflix, 47 Netherlands, 20 network effects, 43–45, 48 New Orleans, 25 New York City, 107; cost of weddings in, 110; dwelling sizes of the wealthy in, 120; hypercompetitive music scene in, 30; penthouses with sweeping views in, 121 New York Metropolitan Opera, 47 New York Times, xiv, 4, 29 New Yorker, 61, 103 New Zealand, 20 Nixon, Richard, 105 no-free-lunch principle, 109 Nobel Prize, 28 Northeastern University, 98 NPR, 5, 126 numerical simulation, 64 Nunn, Sam, 126 Obama, Barack, 84, 91 Ohio State University, 135 O’Neal, Ryan, 23 Organization for Economic Cooperation and Development, 115 orthodox (or standard, or traditional) economic theories, 13, 69, 70, 112, 115 Our Kids, 144 Pacino, Al, 23 Palomar, 128 Patterson, Tim, 35, 36 Peace Corps, 7, 86 Perkins, Tom, 104 Peruggia, Vincenzo, 22 piano manufacturing, 42 Piketty, Thomas, 55 political polarization, 17 Porsche, 15, 16, 91, 119 positional arms control agreements, 118 positional arms races, 116, 117, 118, 144 positional concerns, 115, 116, 118, 122 positive feedback loops, 9, 44, 51, 104, 105 potholes, 16, 91 poverty, 14 Prince Ali Lucky Five Star, 72 Princeton University, xii, 133 progressive consumption tax, 118–27, 158–71; and consumption by retirees, 164; and regressivity, 160; as a Pigouvian tax, effect on economic growth, 161, 162; as a Pigouvian tax, effect on wealth inequality, 166; transition from the current tax system, 162; treatment of durable purchases, 160; treatment of loans, 159, 160; versus taxes on specific luxuries, 163, 164 public investment (see also infrastructure), 13 Putnam, Robert, 144 Puzo, Mario, 23 QDOS (“quick and dirty operating system”), 35 Rai, Birkhaman, 7, 86 Reagan, Ronald, 90 Reardon, Sean, xv Reddit, 56 Reese, PeeWee, 142 Regan, Dennis, 131 relative purchasing power, 92 Review of Economics and Statistics, 28 Rhodes, Frank H.


pages: 256 words: 60,620

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

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

He ended up winning the respect of his former adversaries, as his team of rivals navigated the United States through the Civil War.34 3. Keep track of previous decisions. We humans have an odd tendency: once an event has passed, we believe we knew more about the outcome beforehand than we really did. This is known as hindsight bias. The research shows people are unreliable in recalling how an uncertain situation appeared to them before finding out the results. My family was driving to the airport to catch a flight for a vacation. We could have gone either on Interstate 95 or on the Merritt Parkway, two roughly equivalent routes.

As Søren Kierkegaard, the Danish philosopher said, “Life must be understood backwards …But it must be lived—forwards.”35 So we generally fail to consider enough alternatives looking forward and think we knew what was going on looking backward. The antidote to both is to write down the rationale behind decisions and to consistently revisit past actions. A decision-making journal is a cheap and easy routine to offset hindsight bias and encourage a fuller view of possibilities. 4. Avoid making decisions while at emotional extremes. Making decisions under ideal conditions is tough enough, but you can be sure your decision-making skills will rapidly erode if you are emotionally charged. Stress, anger, fear, anxiety, greed, and euphoria are all mental states antithetical to quality decisions.

On the other hand, the process of writing has made me even more aware of how hard it is to think clearly about a lot of problems. The reality is that we are prone to making mistakes, which when combined with incomplete information and lots of uncertainty, lead to poor outcomes. A bigger problem is what happens after the fact. Once outcomes are revealed, hindsight bias kicks in and lots of commentators suggest they knew what was going to happen before the fact. Further, when things go south, everyone wants someone to blame. (And when they go up, everyone seeks to take credit.) If nothing else, this book should encourage you to be circumspect as events and decisions unfold.


pages: 419 words: 102,488

Chaos Engineering: System Resiliency in Practice by Casey Rosenthal, Nora Jones

Amazon Web Services, Asilomar, autonomous vehicles, barriers to entry, blockchain, business continuity plan, business intelligence, business logic, business process, cloud computing, cognitive load, complexity theory, continuous integration, cyber-physical system, database schema, DevOps, fail fast, fault tolerance, hindsight bias, human-factors engineering, information security, Kanban, Kubernetes, leftpad, linear programming, loose coupling, microservices, MITM: man-in-the-middle, no silver bullet, node package manager, operational security, OSI model, pull request, ransomware, risk tolerance, scientific management, Silicon Valley, six sigma, Skype, software as a service, statistical model, systems thinking, the scientific method, value engineering, WebSocket

As with prior parts of the book, we present here a diverse selection of viewpoints on human factors to both reinforce the value proposition of Chaos Engineering, and to demonstrate its flexibility as a nascent discipline within software engineering. Chapter 9. Creating Foresight In order to determine and envision how to achieve reliability and resilience that our customers and businesses are happy with, organizations must be able to look back unobscured by hindsight bias. Resilient organizations don’t take past successes as a reason for confidence. Instead, they use them as an opportunity to dig deeper, find underlying risks, and refine mental models of how our systems succeed and fail. There are key components of Chaos Engineering beyond building platforms for testing reliability and running Game Days.

Let’s quickly touch on David Woods’s Law of Fluency, which states that once experts become so skilled in their own expertise, they cannot recognize, nor fathom their expertise as a thing.5 Expertise hides the effort involved in work; your role as a facilitator understanding operating procedures in preparation for a chaos experiment is to uncover and ultimately disseminate what that effort looks like. Another reason for this ordering is that new employees have a fresh eye for the system and unobscured hindsight bias. Cognitive psychologist Gary Klein has extensively studied differences between the mental models of newer and more tenured employees: The new employee can see the times when the system broke down not the time when it worked.6 The facilitator should draw their understanding of the system based on the description team members are providing.

At the end of the day, RCA is not reducing the number or severity of security defects in our products. Our current mindset and processes are making the problem worse, not better. The reactionary state of the industry means that we quickly use the “root cause” as an object to attribute and shift blame. Hindsight bias often confuses our personal narrative with truth, and truth is an objective fact that we as investigators can never fully know. The poor state of self-reflection, human factors knowledge, and the nature of resource constraints further incentivize this vicious pattern. Most reported “root causes” of data breaches4 are not due to malicious efforts or criminal activity.


pages: 489 words: 106,008

Risk: A User's Guide by Stanley McChrystal, Anna Butrico

"Hurricane Katrina" Superdome, Abraham Maslow, activist fund / activist shareholder / activist investor, airport security, Albert Einstein, Apollo 13, banking crisis, Bernie Madoff, Boeing 737 MAX, business process, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, computer vision, coronavirus, corporate governance, cotton gin, COVID-19, cuban missile crisis, deep learning, disinformation, don't be evil, Dr. Strangelove, fake news, fear of failure, George Floyd, Glass-Steagall Act, global pandemic, Googley, Greta Thunberg, hindsight bias, inflight wifi, invisible hand, iterative process, late fees, lockdown, Paul Buchheit, Ponzi scheme, QWERTY keyboard, ride hailing / ride sharing, Ronald Reagan, San Francisco homelessness, School Strike for Climate, Scientific racism, Silicon Valley, Silicon Valley startup, Skype, social distancing, source of truth, Stanislav Petrov, Steve Jobs, Thomas L Friedman, too big to fail, Travis Kalanick, wikimedia commons, work culture

Halo Effect: “Halo Effect: Definition and How It Affects Your Daily Life,” Healthline, April 1, 2019, https://healthline.com/health/halo-effect. Status Quo Bias: Kendra Cherry, “How the Status Quo Bias Affects Your Decisions,” Verywell Mind, May 11, 2020, https://verywellmind.com/status-quo-bias-psychological-definition-4065385. Hindsight Bias: Kendra Cherry, “How the Hindsight Bias Affects How We View the Past,” Verywell Mind, May 6, 2020, https://verywellmind.com/what-is-a-hindsight-bias-2795236. Plan-Continuation Bias: “Plan-Continuation Bias,” APA Dictionary of Psychology, accessed September 22, 2020, https://dictionary.apa.org/plan-continuation-bias. Ingroup Bias: “Ingroup Bias,” APA Dictionary of Psychology, accessed September 22, 2020, https://dictionary.apa.org/ingroup-bias.

The bias to lean on an existing belief and continually search for information to support it. (narrative) Halo Effect. The bias to see someone favorably, regardless of actions. (leadership) Status Quo Bias. The bias to believe the current state of affairs is the preferable option. (action) Hindsight Bias. The bias to believe, after an event has occurred, that they would have predicted what the outcome of the event would have been. (timing) Plan-Continuation Bias. The bias to not alter the course of action when situations change. (adaptability) Ingroup Bias. The bias to think those within a group are superior than those outside of it.


pages: 1,088 words: 228,743

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

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

Confirmation bias We seek evidence that supports our view and we interpret ambiguous evidence as supportive. Hindsight bias Hindsight makes past outcomes, even major surprises, appear virtually inevitable after the fact. (“The market crash was bound to happen.”) More personally, memories play tricks on us: we often recall that we had assigned a high likelihood to events that subsequently materialized. (“I knew all along that housing was overpriced.”) Hindsight reinforces overconfidence but may also cause regret. By making the past appear more predictable than it really was, hindsight bias fools us into seeing the future as more predictable than it can ever be.

• Two visual aids—an elephant and a cube—help the reader keep “the big picture” in mind through the book. • Although I present large amounts of empirical evidence about historical returns and forward-looking indicators, as well as numerous theories in an attempt to make sense of the data, I believe it is important to stress humility. Hindsight bias makes us forget how difficult forecasting is, especially in highly competitive financial markets. Expected returns are unobservable and our understanding of them is limited. Even the best experts’ forecasts are noisy estimates of prospective returns. It was six men of Hindostan, To learning much inclined, Who went to see the elephant (Though all of them were blind); That each by observation Might satisfy his mind.

However, my classification mixes in Hirshleifer’s (2001) argument that heuristic simplification and self-deception together provide a unified explanation for most of the judgment and decision biases identified in experimental psychology. On the limits to arbitrage, Shleifer–Vishny (1997) is the classic article, while on psychology and prospect theory the definitive paper is Kahneman–Tversky (1979). Among other works, I allude to Griffin–Tversky (1992) on representativeness/conservatism; Taleb (2001) and Zweig (2008) on hindsight bias; Bordalo–Gennaioli–Shleifer (2010) on salience theory; Shefrin–Statman (1985), Odean (1998), and Frazzini (2006) on disposition effect; Thaler–Johnson (1990) on the house money effect; and Ellsberg (1961) on ambiguity aversion. Turning to applications, besides Shiller’s book Irrational Exuberance, I highlight the writings of Keynes (1936), Minsky (1986), and Soros (2003, 2008).


pages: 384 words: 118,572

The Confidence Game: The Psychology of the Con and Why We Fall for It Every Time by Maria Konnikova

Abraham Maslow, attribution theory, Bear Stearns, behavioural economics, Bernie Madoff, Bluma Zeigarnik, British Empire, Cass Sunstein, cognitive dissonance, cognitive load, coherent worldview, Daniel Kahneman / Amos Tversky, dark triade / dark tetrad, endowment effect, epigenetics, Higgs boson, higher-order functions, hindsight bias, lake wobegon effect, lateral thinking, libertarian paternalism, Milgram experiment, placebo effect, Ponzi scheme, post-work, publish or perish, Richard Thaler, risk tolerance, seminal paper, side project, Skype, Steven Pinker, sunk-cost fallacy, the scientific method, tulip mania, Walter Mischel

They termed the tendency the hindsight bias. In hindsight, we don’t just say we should have known it. We say we did, in fact, know it. So what could Norfleet do, once his initial money was lost? Either he could admit he’d been wrong, that he’d fallen for the magic wallet scam—one of the oldest in the book—or he could say he’d known there was risk all along, but that he had made the investment because, fundamentally, the plan was sound. And if the latter was true, then why not continue to show support by giving over even more money? In hindsight, he was being daft. In the moment, he was exhibiting a hindsight bias of the strongest kind.

We fall for the tale because we want to believe its promise of personal gain—and don’t much feel like recalling any reasons why that promise may be more smoke and mirrors than anything else. In fact, Baruch Fischhoff, a social psychologist at Carnegie Mellon who studies how we make decisions, even has a name for instances of past misdirection: the knew-it-all-along effect or, as it’s more commonly known, hindsight bias. I knew it was a scam the whole time. So the fact that I don’t think that this scheme is a scam now speaks all the more highly for its integrity. The confidence man need not even convince us by this point. We’re quite good at getting over that hurdle ourselves. We don’t see what the evidence says we should see.

., ref1, ref2 Hartzell, Oscar ref1, ref2, ref3, ref4, ref5, ref6, ref7 Haugtvedt, Curtis ref1 Hauser, Marc ref1 health ref1 health products ref1 hedge funds ref1 Heilbroner, Robert ref1 Herbert, David ref1 Herschberg, Jenks ref1 Herschel, John ref1 Herschel, William ref1 Hewitt, Marvin Harold ref1 Hill, Richard ref1 hindsight bias ref1, ref2, ref3 Hines, Kelly Smith ref1 Hobbes, Thomas ref1 Holmes, Oliver Wendell ref1 Hone, Richard ref1 Hopkins, Budd ref1 hot-hand fallacy ref1 Houdini, Harry ref1, ref2, ref3 How the Mind Works (Pinker), ref1 How We Die (Nuland), ref1 Human Knowledge: Its Scope and Its Limits (Russell), ref1 HumInt ref1, ref2 Hunt, Shelby ref1 Hurd, Judge ref1 Hustlers and Con Men (Nash), ref1 Ickes, William ref1 identifiable-victim effect ref1 identity theft ref1, ref2, ref3 immoral behavior ref1 information priming ref1 insects ref1 insider trading ref1, ref2, ref3 intelligence ref1 Internet ref1, ref2, ref3, ref4, ref5 International Foundation for Art Research (IFAR), ref1, ref2 interrupted tasks ref1 investments ref1, ref2, ref3, ref4 Iraq War ref1 IRS and taxes ref1, ref2, ref3 It’s Always Sunny in Philadelphia, ref1 Jacobson, Lenore ref1 Jaeger, Wilf ref1, ref2 Jagatic, Tom ref1 Jahoda, Marie ref1, ref2 Jamal, Karim ref1 James, William ref1, ref2 Jarvik, Murray ref1 Jelly-Schapiro, Joshua ref1, ref2, ref3 Joan ref1 Johns Hopkins Magazine, ref1 Johnson, Paul ref1 Johnson, Samuel ref1 Jones, Robert ref1 Jonke, Eric ref1 Journal of Vibration and Control, ref1 judgments ref1, ref2, ref3 like-dislike ref1, ref2, ref3 about trustworthiness ref1 juries ref1 Kafka, Franz ref1 Kahneman, Daniel ref1, ref2, ref3, ref4, ref5, ref6, ref7 Keating, Caroline ref1 Kelley, Harold ref1 Kipling, Rudyard ref1 Knetsch, Jack ref1 Knight, Alan ref1 Knoedler & Company ref1, ref2 knowledge ref1, ref2 false ref1 of self ref1 Knowles, Eric ref1, ref2 Kramer, Roderick ref1, ref2, ref3 Kube, Jacqueline ref1 Kuhn, Deanna ref1 Kuklinski, Richard ref1 Kunda, Ziva ref1 Kurniawan, Rudy ref1, ref2, ref3 Lagrange, Pierre ref1 Lake Wobegon ref1, ref2 Landmark ref1 land scheme ref1 Langenderfer, Jeff ref1 Langer, Ellen ref1 language ref1 Laplace, Marquis de ref1 Lavoisier, Antoine ref1 Law, John ref1 law of small numbers ref1 Lazare Industries ref1 Lebowitz, Fran ref1 Lee, Blancey ref1, ref2 Lee, Porsha ref1 Lee, Rachel ref1 Lees, Captain ref1 legitimization effect ref1 Lehrer, Jonah ref1, ref2, ref3, ref4 Leslie, Cecil ref1 Levine, Moe ref1 Levy, Jack ref1 Lewis, Milo F., ref1 Life, ref1 liking and disliking ref1, ref2, ref3 limits ref1 Lincoln, Robert Todd ref1 Linn, Jay ref1 Lloyd, Robin ref1, ref2 Locke, Richard Adams ref1 Loewenstein, George ref1, ref2, ref3 London Evening Standard, ref1 Lorenz, Konrad ref1 lotteries ref1, ref2, ref3, ref4, ref5 Louis XIV, King ref1 Louis XVI, King ref1 Lovell, Simon ref1, ref2, ref3 lowball ref1 Lustig, Victor ref1, ref2, ref3 lying ref1, ref2, ref3 white lies ref1, ref2 Lyon, Gary ref1 Machiavellianism ref1 MacGregor, Gregor ref1 Mack, John ref1 Mackay, Charles ref1 Macklin, Rhonda ref1 Madame Zingara ref1, ref2 Madan, Gunish ref1, ref2 Madan, Sandip ref1 Madoff, Bernie ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10 magazine subscriptions ref1 Magician Among the Spirits, A (Houdini), ref1 magicians ref1, ref2, ref3 Mam, Somaly ref1 manipulation ref1 Marie Antoinette ref1 mark ref1 Markowitz, David ref1 Marks, Rose ref1 Maslow, Abraham ref1 Mathewson, Grover ref1 MatthewPAC ref1 Maurer, David ref1, ref2, ref3, ref4, ref5, ref6 McCann, Madeleine ref1 McCormick, Jim ref1 meaning ref1, ref2 Melville, Herman ref1 memory ref1, ref2, ref3, ref4 mental overload ref1 Mesmer, Franz Friedrich Anton ref1 Meyer, Max ref1 Mielnicki, Tomasz ref1 Milani, Denise ref1, ref2, ref3, ref4 Milgram, Stanley ref1 Milkman, Katherine ref1 Miller, John and Louis ref1 Miller, William Franklin ref1, ref2, ref3, ref4, ref5, ref6, ref7 Millet, Robert ref1 mind perception ref1 mirroring ref1 Mirvish, David ref1 Mississippi Company ref1 Mitchell, Sylvia ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8 Moffitt, Tim ref1 money: paper ref1 thinking about ref1 money box ref1 Montague, Miss St.


pages: 1,737 words: 491,616

Rationality: From AI to Zombies by Eliezer Yudkowsky

Albert Einstein, Alfred Russel Wallace, anthropic principle, anti-pattern, anti-work, antiwork, Arthur Eddington, artificial general intelligence, availability heuristic, backpropagation, Bayesian statistics, behavioural economics, Berlin Wall, Boeing 747, Build a better mousetrap, Cass Sunstein, cellular automata, Charles Babbage, cognitive bias, cognitive dissonance, correlation does not imply causation, cosmological constant, creative destruction, Daniel Kahneman / Amos Tversky, dematerialisation, different worldview, discovery of DNA, disinformation, Douglas Hofstadter, Drosophila, Eddington experiment, effective altruism, experimental subject, Extropian, friendly AI, fundamental attribution error, Great Leap Forward, Gödel, Escher, Bach, Hacker News, hindsight bias, index card, index fund, Isaac Newton, John Conway, John von Neumann, Large Hadron Collider, Long Term Capital Management, Louis Pasteur, mental accounting, meta-analysis, mirror neurons, money market fund, Monty Hall problem, Nash equilibrium, Necker cube, Nick Bostrom, NP-complete, One Laptop per Child (OLPC), P = NP, paperclip maximiser, pattern recognition, Paul Graham, peak-end rule, Peter Thiel, Pierre-Simon Laplace, placebo effect, planetary scale, prediction markets, random walk, Ray Kurzweil, reversible computing, Richard Feynman, risk tolerance, Rubik’s Cube, Saturday Night Live, Schrödinger's Cat, scientific mainstream, scientific worldview, sensible shoes, Silicon Valley, Silicon Valley startup, Singularitarianism, SpaceShipOne, speech recognition, statistical model, Steve Jurvetson, Steven Pinker, strong AI, sunk-cost fallacy, technological singularity, The Bell Curve by Richard Herrnstein and Charles Murray, the map is not the territory, the scientific method, Turing complete, Turing machine, Tyler Cowen, ultimatum game, X Prize, Y Combinator, zero-sum game

(Sadly, we humans can’t rewrite our own code, the way a properly designed AI could.) Speaking of “hindsight bias” is just the nontechnical way of saying that humans do not rigorously separate forward and backward messages, allowing forward messages to be contaminated by backward ones. Those who long ago went down the path of phlogiston were not trying to be fools. No scientist deliberately wants to get stuck in a blind alley. Are there any fake explanations in your mind? If there are, I guarantee they’re not labeled “fake explanation,” so polling your thoughts for the “fake” keyword will not turn them up. Thanks to hindsight bias, it’s also not enough to check how well your theory “predicts” facts you already know.

“Mysterious Answers” next asks whether science resolves these problems for us. Scientists base their models on repeatable experiments, not speculation or hearsay. And science has an excellent track record compared to anecdote, religion, and . . . pretty much everything else. Do we still need to worry about “fake” beliefs, confirmation bias, hindsight bias, and the like when we’re working with a community of people who want to explain phenomena, not just tell appealing stories? This is then followed by The Simple Truth, a stand-alone allegory on the nature of knowledge and belief. It is cognitive bias, however, that provides the clearest and most direct glimpse into the stuff of our psychology, into the shape of our heuristics and the logic of our limitations.

., “People Focus on Optimistic Scenarios and Disregard Pessimistic Scenarios While Predicting Task Completion Times,” Journal of Experimental Psychology: Applied 6, no. 3 (2000): 171–182, doi:10.1037/1076-898X.6.3.171. 5. Buehler, Griffin, and Ross, “Inside the Planning Fallacy.” 6. Ibid. 8 Illusion of Transparency: Why No One Understands You In hindsight bias, people who know the outcome of a situation believe the outcome should have been easy to predict in advance. Knowing the outcome, we reinterpret the situation in light of that outcome. Even when warned, we can’t de-interpret to empathize with someone who doesn’t know what we know. Closely related is the illusion of transparency: We always know what we mean by our words, and so we expect others to know it too.


American Secession: The Looming Threat of a National Breakup by F. H. Buckley

Affordable Care Act / Obamacare, Andrei Shleifer, belling the cat, Bernie Sanders, British Empire, Cass Sunstein, colonial rule, crony capitalism, desegregation, diversified portfolio, Donald Trump, Francis Fukuyama: the end of history, guns versus butter model, hindsight bias, illegal immigration, immigration reform, income inequality, low interest rates, Michael Milken, military-industrial complex, old-boy network, Paris climate accords, race to the bottom, Republic of Letters, reserve currency, Ronald Coase, Stephen Fry, Suez crisis 1956, transaction costs, Washington Consensus, wealth creators

An ardent defender of slavery, he seems to have had a hand in crafting the Supreme Court’s notorious Dred Scott decision. As president, he managed to infuriate northern Democrats and the Republican abolitionists without winning over southern secessionists. Historians routinely list him as the worst of our presidents. But what do they know? It’s all too easy to fall prey to the hindsight bias when judging past actions. We know just how the coach blew the Sunday football game—on Monday morning. We know the pitcher should have been pulled—but only after he had given up the home run. So try to imagine yourself in Buchanan’s shoes before the Civil War began, and ask yourself what you would have done.

But had it turned out differently, had Lee won the Battle of Gettysburg and marched on Washington, had hundreds of thousands of deaths failed to reunite the country, had slavery endured and had Lincoln lived on to the mediocrities of old age, we might remember him as the worst of our presidents. If you think otherwise, your hindsight bias is showing. And now? Were a state to secede today, we would have two presidential models to choose from, Buchanan and Lincoln. Buchanan is remembered as a weak-minded failure, but is it so certain that we’d want to see a Lincoln in office, ready to use any means necessary to preserve the Union, ready to sacrifice the lives of many thousands of soldiers?


Phil Thornton by The Great Economists Ten Economists whose thinking changed the way we live-FT Publishing International (2014)

Alan Greenspan, availability heuristic, behavioural economics, Berlin Wall, bitcoin, Bretton Woods, British Empire, business cycle, business process, call centre, capital controls, Cass Sunstein, choice architecture, cognitive bias, collapse of Lehman Brothers, Corn Laws, creative destruction, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, double helix, endogenous growth, endowment effect, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, fiat currency, financial deregulation, fixed income, Ford Model T, full employment, hindsight bias, income inequality, inflation targeting, invisible hand, Isaac Newton, John Maynard Keynes: Economic Possibilities for our Grandchildren, joint-stock company, Kenneth Arrow, Kenneth Rogoff, Kickstarter, liquidity trap, loss aversion, mass immigration, means of production, mental accounting, Myron Scholes, paradox of thrift, Pareto efficiency, Paul Samuelson, Post-Keynesian economics, price mechanism, pushing on a string, quantitative easing, Richard Thaler, road to serfdom, Ronald Coase, Ronald Reagan, school vouchers, Simon Kuznets, The Chicago School, The Wealth of Nations by Adam Smith, Thomas Malthus, Toyota Production System, trade route, transaction costs, unorthodox policies, Vilfredo Pareto, women in the workforce

Kahneman has cited a study of chief financial officers carried out by Duke University in the US showing that there was no correlation between their forecasts for the stock market and what actually happened. There are many more biases that show that people make decisions in a way that reveals that people’s preferences are not stable, as classical economists say, but can shift according to how they understand the choice open to them (i.e. because they have reframed it). These include: • Hindsight bias, which encourages people to infer their own skills from successes that were down to luck or timing. A stockbroker who rode a share price boom will believe he is a genius, which in turn fuels his optimism bias. • Confirmation bias, or the tendency to place more emphasis on evidence that favours your existing view and ignore that which does not.

Bush 139 influence on Margaret Thatcher 138–9 influence on Ronald Reagan 139 influence on the monetarists 138–9 key economic theories 122–36 key ideas 142 libertarian views 134–6, 140 long-term legacy 137–41 nature of the free market system 131–3 Nobel Prize (1974) 137 opposition to central state planning 134–6, 140 out of fashion 129–31 prices and knowledge 131–3 Prices and Production (1931) 126, 130 rejection of government control of the economy 120 study of philosophy and economics 121–22 The Road to Serfdom (1944) 135, 138, 140 time and the value of capital 124–6 verdict 141–2 Hegel, Georg 51–2, 54 herd behaviour 105 heuristics and bias in decision making 222–5 Hicks, John 173 High Speed 2 train line from London to the North 125 hindsight bias 227 242Index Hobbes, Thomas 5 hubris hypothesis 227 human behaviour, Becker’s approach 212–15 human capital theory (Becker) 200–2, 210 human decision making processes (Kahneman) 221–5 Hume, David 4, 97 Hutcheson, Francis 3–4 illusion of validity concept 220, 224 income inequality in the present day 64–6 individualism, view of Friedman 155–7 industrial districts 84–6, 87 industrial economics 84–6, 87 Industrial Revolution 11 inflation 107, 110 actions of the central banks 161 and Keynesian policies 127 and money supply 151–2 relationship with unemployment 153–5 Institute of Economic Affairs 138, 161 interest rates effects of adjustments 103–4 effects of credit expansion 123–4 natural rate of interest (Hayek) 123 intergenerational economics 178–80 International Bank of Reconstruction and Development 109 international economics and trade, view of Samuelson 183–7 International Monetary Fund (IMF) 108–9, 113, 186 international trade and comparative advantage (Ricardo) 35–8 international trade theory 184–5 intervention during economic depression, view of Keynes 92–3, 94, 105–6 investment, volatility caused by uncertainty 104–5 invisible hand concept (Smith) 7–9 Johnson, Harry 94 Johnson, Lyndon B. 110, 190 joint-stock companies 86 Kahneman, Daniel (1934– ) 206, 217–36 behavioural economics 218–19, 233–6 biases and errors in financial decision making 225–32 cognitive biases 222–5 decision making under risk 228–32 early life and influences 219–20 economic writings and theories 221–32 from psychology to economics 225–32 gambler’s fallacy (misconception of chance) 224 heuristics and bias in decision making 222–5 human decision making processes 221–5 illusion of validity concept 220, 224 long-term legacy 233–4 loss aversion 230–2 multidisciplinary approach to economics 218 Nobel Prize for economic sciences (2002) 218, 220 optimism bias and overconfidence 226–7 Prospect Theory 228–32, 234 Thinking, Fast and Slow (2012) 226–7, 234 verdict 235–6 Kennedy, John F. 110, 190 Keynes, John Maynard (1883–1946) 19, 73, 86, 91–116, 171 aggregate demand and the role of government 102–4 Bretton Woods agreement 95, 108–9 causes of unemployment 101 challenging the classical consensus 99–106 Index243 clash with Hayek 120, 126–31 criticism from monetarists 110–11 criticism of self-correction of markets 99, 105–6 criticism of the gold standard 95, 98, 107 criticism of the quantity theory of money 97 drivers of recession 101 early life and influences 93–4 effects of changes in money supply 97 effects of interest rate adjustments 103–4 effects of reducing wages 101–2 elevation to the House of Lords 106 end of the Keynesian revival 113–14 First World War and aftermath 95–7 focus on demand side economics 127 General Theory 99–106 Great Crash (1929) 98, 99 Great Depression (1930s) 99–100 International Bank of Reconstruction and Development 109 International Monetary Fund 108–9 investments as King’s College Bursar 98, 114 investor expectations and uncertainty 104–5 key ideas 115–16 liquidity preference theory 105, 113 long-term legacy 109–14 marginal propensity to consume (MPC) 103 marginal propensity to save (MPS) 103 move into economics 94–8 multiplier concept 103 national economist to international statesman 106–9 paradox of thrift 101 periods in and out of favour 92–3 plans for post-WWII international economy 107–9 popularity of Keynesianism 109–10 revival in the 2008 financial crisis 111–13 savings and investment 100–1 Second World War and aftermath 106–9 severe falls in output 101–2 state intervention during economic depression 92–3, 94, 105–6 Treaty of Versailles 95–6 and investment volatility 104–5 unpopularity beginning in the 1970s 110–11 verdict 115 Keynes, John Neville 93 Klaus, Vaclav 140 Kotlikoff, Laurence 179 Krugman, Paul 180, 191 Kuznets, Simon 148 Laar, Mart 140 labour-intensive goods, effects of increase in wages 33 labour market, human capital concept 200–2, 210 laissez-faire economic system 9 rejection by Keynes 105–6 law of diminishing returns 31 Lehman Brothers collapse (2008) 42, 67 Leviathan (Hobbes) 5 Levitt, Steve 234 libertarian views Friedman 157 Hayek 134–6, 140 life choices, economic perspective 203–6 Lindbeck, Assar 168 liquidity preference theory 105, 113 London School of Economics (LSE) 122, 126, 128 loss aversion 230–2 Lucas, Robert 202 244Index Mackintosh, William 109 Malthus, Thomas Robert 31, 33, 169 marginal analysis 80–2 marginal change concept (Marshall) 80–2 marginal propensity to consume (MPC) 103 marginal propensity to save (MPS) 103 marginal rate of substitution 180 market equilibrium price 76–7 market mechanism (Smith) 15–16 market price, supply and demand factors 15–16 market self-correction, criticism by Keynes 99, 105–6 marriage, economic perspective 203–6 Marshall, Alfred (1842–1924) 71–89, 170 and the business world 84–6 ceteris paribus approach to economic analysis 79–80 concept of time in supply and demand 77–9 early life and influences 73–4 economics as a science 73, 86 economics theories 75–86 elasticity of demand 82–4 geographical effects in economics 84–6 industrial districts 84–6, 87 industrial economics 84–6, 87 influence on Keynes 93, 95 interaction between costs and value 75–7 key ideas 88–9 long-term legacy 86–8 marginal analysis 80–2 marginal change concept 80–2 mathematical approach to economics 72 microeconomics 72, 86 political economy 74 price as interaction of supply and demand 75–9 Principles of Economics (1890) 72, 76, 77–8, 87–8, 188 supply and demand model 75–84 verdict 88 Marx, Karl (1818–83) 19, 49–68 and the global financial crisis (2008) 61–3 capitalist exploitation of the working class 56–8, 62–3 capitalist production process 54–6 communism 50 Communist Manifesto (Marx and Engels) 52, 58–61 Das Kapital 52, 53–4, 59–61, 62, 67–8 distribution of economic value 54–6 downfall of capitalism 56–8, 61–3 early life and influences 51–3 economics theories 53–8 ‘fictitious capital’ concept 62 income inequality in the present day 64–6 key ideas 68 long-term legacy 63–7 surplus value of labour 54–6 verdict 67–8 view of Marxist governments 66 mass production 11 Massachusetts Institute of Technology (MIT) 170 mathematical approach to economics Marshall 72 Samuelson 169–70 mercantilism 7–8, 22–3 mergers and acquisitions 226–7 Merton, Robert 187 microeconomics 172–3, 174, 196 work of Marshall 72, 86 Microsoft 233 middle class, rise of 64 Mieses, Ludwig von 121–2 Mill, James 30–1 Mill, John Stuart 30, 181 The Principles of Political Economy (1848) 188 Modigliani, Franco 173 monetarism 110, 138–9, 146, 151–2 monetarist rule 152 Index245 money supply and the Great Depression (1930s) 150–2 effects of changes in (Keynes) 97 role in running the economy 151–2 monopolies evil of 10–11 regulation to prevent 21–2 multiplier effect 103, 174–5 Murphy, Kevin 201, 210–12 NAIRU (non-accelerating inflation of unemployment) 153–5 Nashat, Guity 206 neoclassical synthesis 174 neo-Keynesianism 168–9, 173–5 net profit 81 New Classical Economics 159 New Deal (Franklin D.


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The Joys of Compounding: The Passionate Pursuit of Lifelong Learning, Revised and Updated by Gautam Baid

Abraham Maslow, activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, Albert Einstein, Alvin Toffler, Andrei Shleifer, asset allocation, Atul Gawande, availability heuristic, backtesting, barriers to entry, beat the dealer, Benoit Mandelbrot, Bernie Madoff, bitcoin, Black Swan, book value, business process, buy and hold, Cal Newport, Cass Sunstein, Checklist Manifesto, Clayton Christensen, cognitive dissonance, collapse of Lehman Brothers, commoditize, corporate governance, correlation does not imply causation, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, deep learning, delayed gratification, deliberate practice, discounted cash flows, disintermediation, disruptive innovation, Dissolution of the Soviet Union, diversification, diversified portfolio, dividend-yielding stocks, do what you love, Dunning–Kruger effect, Edward Thorp, Elon Musk, equity risk premium, Everything should be made as simple as possible, fear index, financial independence, financial innovation, fixed income, follow your passion, framing effect, George Santayana, Hans Rosling, hedonic treadmill, Henry Singleton, hindsight bias, Hyman Minsky, index fund, intangible asset, invention of the wheel, invisible hand, Isaac Newton, it is difficult to get a man to understand something, when his salary depends on his not understanding it, Jeff Bezos, John Bogle, Joseph Schumpeter, junk bonds, Kaizen: continuous improvement, Kickstarter, knowledge economy, Lao Tzu, Long Term Capital Management, loss aversion, Louis Pasteur, low interest rates, Mahatma Gandhi, mandelbrot fractal, margin call, Mark Zuckerberg, Market Wizards by Jack D. Schwager, Masayoshi Son, mental accounting, Milgram experiment, moral hazard, Nate Silver, Network effects, Nicholas Carr, offshore financial centre, oil shock, passive income, passive investing, pattern recognition, Peter Thiel, Ponzi scheme, power law, price anchoring, quantitative trading / quantitative finance, Ralph Waldo Emerson, Ray Kurzweil, Reminiscences of a Stock Operator, reserve currency, Richard Feynman, Richard Thaler, risk free rate, risk-adjusted returns, Robert Shiller, Savings and loan crisis, search costs, shareholder value, six sigma, software as a service, software is eating the world, South Sea Bubble, special economic zone, Stanford marshmallow experiment, Steve Jobs, Steven Levy, Steven Pinker, stocks for the long run, subscription business, sunk-cost fallacy, systems thinking, tail risk, Teledyne, the market place, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, time value of money, transaction costs, tulip mania, Upton Sinclair, Walter Mischel, wealth creators, Yogi Berra, zero-sum game

And yet very few of us think about how we engage in decision-making. Confusing actual experience with a false memory of it can be a big deterrent to learning from our past investing outcomes. And if we are not learning effectively from our past experiences, we cannot improve our process to make better decisions. Because of narrative fallacy, hindsight bias, and imperfect memory, it is almost impossible to recall with 100 percent accuracy the reasons why we made a particular decision in the past. Our brain fools us by presenting a distorted picture of the circumstances under which we made the decision, which leads to the wrong conclusion about why a stock turned out to be a mistake or a success.

As Robert Heinlein writes, “man is not a rational animal; he is a rationalizing animal.”1 After the fact, everything seems obvious; in hindsight, fundamental analysis of any event can be reconstituted and is always brilliant. A journal is the most objective way to remain true to yourself and to avoid hindsight bias. More important, it helps you continuously learn from your mistakes, and these insights will be your greatest teachers in life, business, and investing. The significant intrinsic value of learning from one’s personal mistakes (and, even more important, learning vicariously from others’ mistakes) over an investing lifetime is grossly underestimated.

I find that it is highly beneficial to refer to this information whenever the market undergoes its periodic steep corrections. Human behavior in the markets has not really changed much over time. The process of structuring our thoughts into a journal entry brings clarity to our thinking. Journaling by hand reduces the possibility of hindsight bias. It is hard to look at your own writing and deny your previous thoughts. A periodic review is an important part of the process. This is how you start getting better. Realizing where you make mistakes, why you make them, and what the common mistakes are that you tend to make all can help you improve over time.


pages: 387 words: 120,155

Inside the Nudge Unit: How Small Changes Can Make a Big Difference by David Halpern

Affordable Care Act / Obamacare, availability heuristic, behavioural economics, carbon footprint, Cass Sunstein, centre right, choice architecture, cognitive dissonance, cognitive load, collaborative consumption, correlation does not imply causation, Daniel Kahneman / Amos Tversky, data science, different worldview, endowment effect, gamification, happiness index / gross national happiness, hedonic treadmill, hindsight bias, IKEA effect, illegal immigration, job satisfaction, Kickstarter, language acquisition, libertarian paternalism, light touch regulation, longitudinal study, machine readable, market design, meta-analysis, Milgram experiment, nudge unit, peer-to-peer lending, pension reform, precautionary principle, presumed consent, QR code, quantitative easing, randomized controlled trial, Richard Thaler, Right to Buy, Ronald Reagan, Rory Sutherland, Simon Kuznets, skunkworks, supply chain finance, the built environment, theory of mind, traffic fines, twin studies, World Values Survey

Prior to this, David was Chief Analyst in the Prime Minister’s Strategy Unit (2001–2007), and has held academic positions at the Universities of Cambridge, Oxford and Harvard. To the elected FOREWORD ONE OF THE most powerful and pernicious of the many cognitive biases that have been uncovered by behavioural scientists is ‘hindsight bias’, first investigated by Baruch Fischhoff when he was a graduate student studying at the Hebrew University with Daniel Kahneman and Amos Tversky. Simply put, hindsight bias is the phenomenon that after the fact, we think we knew it all along. Would America elect an African-American as President before a woman? Sure, we all thought that could happen. Did we think in 2000 that fifteen years later most of us would be carrying powerful computers in our pockets that could keep us up-to-date with email, answer nearly any factual question just by speaking to it, and get us anywhere without getting lost?

(page numbers in italics refer to illustrations) advertising: and alcohol 100–1 and humour 100 and shock 98–100, 100 and smoking 99, 100 airport expansion 98 alcohol 100–1, 127 and calories 100 and pregnancy 126–7 Alexander, Danny 281 anaesthetics 17 ‘animal spirits’ 207, 210, 211 Aos, Steve 282 Ariely, Dan 96–7, 134, 325 Aristotle 221, 240 Armstrong, Hilary 34 Asch, Solomon 26 ASH (Action on Smoking and Health) 189 Ashford, Maren 57, 83 attentional spotlight 83–4 Ayres, Ian 142 Bazerman, Max 134, 325 Beales, Greg 36 Behavioural Insights Team (BIT) (see also nudging): arguments lost by 212–14 becomes social-purpose company 350 beginnings of x–xi, 50–8, 56, 58, 341 current numbers employed by xiii, 341 current trials by 341 expansion of xiii governments follow 11 initial appointments to 56–7, 56 initial scepticism towards 9 most frequent early criticisms of 333 naming of x–xi, 52–3 objectives of 54–5 and transparency, efficacy and accountability, see under nudging and webpage design 275–9, 276 World Bank’s request to 125 year of scepticism experienced by 274 behavioural predators 312–13 Benartzi, Shlomo 64 benefits, see welfare benefits Bentham, Jeremy 221–2 BIG lottery 283 ‘Big Society’ 43, 50, 142, 250 BIT, see Behavioural Insights Team Blair, Tony 151, 225 and behavioural approaches in government 302 Brown takes over from 36, 260–1 review into tenure of 34 Strategy Unit of 31 Tories’ admiration of 50 Bogotá 135, 146 Bohnet, Iris 123 Britton, John 188 Brown, Gordon 34 becomes PM 36, 260–1 Byrne, Liam 47 Cameron, David 151 BIT set up by 8 and Coalition Agreement 38 and data transparency 159 Hilton appointed by 43 and randomised controlled trials 274 and response to notes 186 and smoking 194 and well-being 225–8, 227, 250 car tax 3, 91, 92, 275–8 carrier bags 23 Centre for Ageing Better 282 Centre for Local Economic Growth (LEG) 282, 288 Chand, Raj 146 charities 116–20, 142–4, 144 and reciprocity 116 Chetty, Raj 64 childbirth, see pregnancy and childbirth Cialdini, Robert 34–6, 47, 107–8, 109, 113, 121–2, 308, 312 Clegg, Nick, and Coalition Agreement 38 Cochrane, Dr Archie 269–71, 295, 297 Cochrane Collaboration 271 cocktail-party effect 86 cognitive dissonance 21 cognitive psychology 27–9, 28 Colbourne, Tim 215 College of Policing 282, 289 Collins, Kevan 283, 285 Community First 254–5 commuting 219–20, 263–4 conflict and war 20–1, 27, 87, 344–5 consumer feedback 161–9, 167 improvements driven by 168–9 in public sector 163–9, 167 cooling-off periods 77 Council Tax 95 crime prevention (see also theft): ‘scared straight’ approach to 266–8, 267 and ‘What Works’ institutes 289 Darley, J. 27, 110 data transparency 153–84 and better nudges 179–80 and consumer feedback 161–9, 167 improvements driven by 168–9 in public sector 163–9, 167 and food labelling 172, 178 and machine-readable code 154, 157, 159 and RACAP 157 in restaurants 178 and understandable information 176–9 on cancer 178–9 on car safety 177–8 on financial products 177 and utility suppliers 154–60, 155 Davey, Ed 157 Deaton, Angus 243 decision fatigue 141 Deep Blue 7 Diener, Ed 231 disability-adjusted life years (DALYs) 272 discontinuity design 161–2 doctors’ handwriting 72, 72 Dolan, Paul 47–8, 220 Down, Nick 113 drivers’ behaviour 18, 18 Duckworth, Angela 247 Dunn, Elizabeth 220, 237, 250, 256 Durand, Martine 243 Dweck, Carol 343 e-cigarettes 188–97, 193, 215 estimated years of life saved by 195, 216 and non-smokers 193–4 and quit rates 192–3, 193 by socio-economic grouping 195 Early Intervention Foundation (EIF) 282 EAST (Easy, Attractive, Social, Timely) framework 10, 60, 149, 349 Attractive 80–105, 81, 85, 94 Easy 62–79, 68, 72, 73 and jobcentres 200 Social 106–25, 115, 118, 120, 122 (see also social influence) Timely 126–49, 129 Easterlin, Richard 238 eating habits 139, 171, 307 (see also obesity/weight issues) and choice 306–7 and food pyramid/plate illustrations 41, 41 and food tax 301–2 and healthy/unhealthy food 41, 82, 101–2, 216, 302 ‘mindless’ 171 Economic and Social Research Council 283 economy, UK 205–12 econs 6–7, 178, 223 education 137, 282 financial 64 further 146–7 and timely intervention 146–7 and ‘What Works’ institutes 283–7, 284, 286 Educational Endowment Foundation (EEF) 282, 283–7, 284, 286 Effectiveness and Efficiency (Cochrane) 295 endowment effect 140 Energy Performance Certificate 179 energy ratings 135 energy and utility suppliers, see utility suppliers Enterprise Bill 159 Epley, Nick 260–1 established behaviour, see habits ethnicity, and recruitment 137–9, 344 experimental government 266–98, 270, 272, 276 and crime prevention 266–8, 267 ethics of 325–8 (see also nudging: and accountability) and growth vouchers 279–80 and organ donation 275–9, 276 and overseas health-aid programmes 273 and radical incrementalism 291 and ‘What Works’ institutes 281–90, 292–4 Centre for Ageing Better 282 Centre for Crime Reduction 289 Centre for Local Economic Growth (LEG) 282, 288 Early Intervention Foundation (EIF) 282, 288 Educational Endowment Foundation (EEF) 282, 283–7, 284, 286 experimental psychology 24–6 farmers 145 ‘fat tax’ 301–2 (see also eating habits) fertiliser 145 Feynman, Richard 296, 297 financial crisis 45, 46, 206, 336 (see also UK economy) financial products 177, 206 fines, collecting 3–4, 52, 89, 90–1 Fischhoff, Baruch ix Fisher, Ronald 291 Fiske, Susan 84, 86, 325, 345 food pyramid/plate illustrations 41, 41 forms, prefilling 73–4 fossils 35 Frederick the Great 15, 16 Freud, Lord 279 Gallagher, Rory 55, 88–9, 158, 197–8, 204, 343, 349 gender equality, and company boards 123 Genovese, Kitty 109–10 Gigerenzer, Gerd 178 Gilbert, Danny 139, 220 Gino, Francesca 347 giving 116–20, 142–4, 144, 250 God Complex 269 Gove, Michael 287 Grant, Adam 347 Green Book 46, 228, 258, 259 Grice, Joe 233 Gross Domestic Product (GDP) 222–4, 255 (see also UK economy) Grove, Rohan 211 growth vouchers 279–80 Gyani, Alex 197–8, 203, 204, 343, 349 habits: and early intervention 128–32 key moments to prompt or reshape 132–9 and tax payments 131 Hallsworth, Michael 48, 113 Hancock, Matthew 279 hand washing 99, 140 happy-slave problem 231 Haynes, Laura 56–7 hearing 25 Heider, Fritz 345 Helliwell, John 226–7, 232 Henry VIII 17 herd instinct 161 Heywood, Sir Jeremy 2, 215, 217, 281 The Hidden Wealth of Nations (Halpern) 44 Highway Code 20 Hillman, Nick 165 Hilton, Steve x, 43–4, 51, 53–4, 159, 190, 214, 215, 225–6, 247, 250 and randomised controlled trials 274 hindsight bias ix HMRC 2–3, 8, 87–8, 89, 113, 115, 118, 120, 181–2 (see also tax payments) BIT member’s secondment to 113 non-tax-related business communications sent via 210–11 and online tax forms 74 and randomised controlled trials 274 Homer, Lin 210 honesty 133–4 honours 98 horses’ behaviour 18–19, 19 hospitals: and doctors’ handwriting 72, 72 and patient charts 72–3, 73 Hume, David 221 Hunt, Stefan 209 Hurd, Nick 250 Hutcheson, Francis 221 hyperbolic discounting 139 imprinting 128–9, 129 infant development 128–30 (see also pregnancy and childbirth) and early mother–child ‘meshing’ 129 (see also imprinting) in geese 128–9, 129 and mother’s depression 129 Influence: The Psychology of Persuasion (Cialdini) 34–5, 312 Inglehart, Ronald F. 229 Inland Revenue, see HMRC Institute for Government 40, 46–50 J-PAL 294 jobcentres 120–1, 197–205, 200, 201, 343, 349 (see also unemployment) John, Peter 96 The Joyless Economy (Scitovsky) 223 judges 140 Kahneman, Daniel 27, 29–30, 32, 48, 220, 226, 230 BIT’s work commended by 11 Kasparov, Garry 7 Kennedy, Robert F. 218, 222 Kettle, Stuart 125 Keynes, John Maynard 210, 211–12 King, Dom 48, 72 Kirkman, Elspeth 121, 146 knife crime 122 Kuznets, Simon 222 Laibson, David 64–5, 245, 307 Latene, B. 27, 110 Layard, Richard 225, 242, 248 Lazy Town 82 Legatum Institute 242–3 letters/messages, simplifying 71–3 and handwriting 72 in hospitals 72–3, 73 and prefilled forms 73–5 Letwin, Oliver 213, 217, 281, 295 Life satisfaction (discussion paper) 225 (see also well-being) Linos, Elizabeth 137, 344 List, John 286 litter 23, 35, 94, 107–8, 114 Loewenstein, George 307, 324, 345 loft/wall insulation 3, 75–6 Lorenz, Konrad 128–9, 129 lotteries, as incentive 94–6 Luca, Michael 161–2, 166, 177 Lyard, Richard 238 Lyons, Michael 250 MacFadden, Pat 34 Mackenzie, Polly 51, 215 Major, John 46 Manzi, James 295–6 Marcel, Anthony 136 Martin, Steve 113 Matheson, Jill 227 Mayhew, Pat 66 Mazar, Nina 347 Meacher, Michael 224 mental health 246–9 Merkel, Angela 243 midata, see data transparency Milgram, Stanley 26, 327 Miliband, Ed 34 military recruitment advertising 87 Milkman, Katherine 323 Mill, John Stuart 221 MINDSPACE framework 49–50, 50, 60, 72 motorcycle helmets 66–7 Mulgan, Geoff 225, 301–2 Mullainathan, Sendhil 343 National Citizenship Service (NCS) 251–2, 251 National Institute for Health and Care Excellence (NICE) 195, 271, 281, 290 Nesta 350 Nguyen, Sam 55, 197–8, 343 The Nicomachean Ethics (Aristotle) 240 nicotine-replacement therapy (NRT) 193, 193 (see also smoking) 9/11 28 Norton, Mike 256, 347 Nudge (Thaler, Sunstein) ix–x, 6–7, 39, 157, 234 Nudge Unit, see Behavioural Insights Team nudging (see also Behavioural Insights Team; EAST framework): and accountability 324–5 and experimentation, ethics of 325–8 and the public voice 328–32, 329 defined and discussed 22–4 and efficacy 304, 315–24 and familiarity with approach 319–24 relative 318–19 improving, with better data 179–80 rediscovery of 13 and subconscious priming 136 and transparency 304–15 and behavioural predators 312–13 and choice 306, 314–15 and effective communication vs propaganda 307–11, 311 Nurse Family Partnership 129 Obama, Barack 39–40, 254 acceptance speech of 38 Obama, Michelle 101 obesity/weight issues 101, 170–3, 307 (see also eating habits) in children, levelling of 173 and food labelling 172 and ‘mindless’ eating 171 O’Donnell, Sir Gus (later Lord) 45–6, 47, 57, 225, 227, 227, 242, 258 OECD 293, 340 Office of War Information (US) 21 Olds, David 130 online shopping 109 Ord, Toby 273 organ donation 9, 37, 52, 275–9 Orwell, George 309, 311 Osborne, George 45 and data transparency 159 O’Shaughnessy, James 247 Overman, Henry 288 Paley, William 221 paternalism x, 33, 51, 316 Pelenur, Marcos 135 pensions xii, 9, 62–5, 331 and choice 307 PMSU’s paper on 33 people’s parliaments 332 perception 24–5, 25 Personality responsibility and behaviour change (discussion paper) 301–2 police, ethnic recruits into 137–9, 344 potato consumption 15–16 pregnancy and childbirth 126–7 (see also infant development) Prescott, John 302 Prime Minister’s Strategy Unit (PMSU) 31–3, 47, 53, 225, 337 and Personality responsibility and behaviour change paper 301–2 psychological operations (PsyOps) 30, 308–9, 333 Putnam, Robert 253 radical incrementalism 291 randomised controlled trials (RCTs) 8, 113, 132, 182, 252, 270, 274–5, 283, 297–8, 339 and HMRC 274 Raseman, Sophie 157 RECAP 157 recycling 35 Red Tape Challenge 57 Reeves, Richard 51 Revenue and Customs, see HMRC road fuel 23 road traffic, see vehicles Roberto, Christine 101, 178 Rogers, Todd 146, 321 Rolls-Royce 208 Roosevelt, Franklin D. 21 Ruda, Simon 125, 137, 214, 344 Sainsbury, Lord (David) 46–7 Sanders, Michael 57, 116, 119, 142–3, 146 Scheving, Magnús 81, 82–3 Scitovsky, Tibor 223 Scott, Stephen 247 Seligman, Marty 232, 247 Sen, Amartya 231 Service, Owain 2, 56, 69 Sesame Street 101 Shadbolt, Sir Nigel 158 Shafir, Eldar 343, 345 sight 24–5, 25 Silva, Rohan x–xi, 43–5, 51, 53–4, 159 Singer, Tania 345 small businesses 205–9 passim (see also UK economy) smart disclosure 157 smoke detectors 99 smoking 9, 23, 99, 100, 138 and e-cigarettes 188–97, 193, 215 estimated years of life saved by 195, 216 and non-smokers 193–4 and nicotine-replacement therapy (NRT) 193 and pregnancy 126–7 prevalence of 189 and quit rates 192–3, 193 by socio-economic grouping 195 SNAP framework 48 social influence 26–7, 106–25 and bystander intervention 110 dark side of 109–10 and litter 107–8, 114 norms of: descriptive vs injunctive 108 picking apart 107–11 in policy 111–15 and online shopping 109 and personal touch 119–21 and reciprocity 115–17 social psychology 107 Soman, Dilip 337 Southern Cross station staircase 85 speed bumps 76–7 Sportacus 81–3, 81 Stanford Prison 26–7 Steinberg, Tom 254 stickk.com 142 subconscious priming 136 suicide 67–9, 68, 77 Sunstein, Cass ix–x, 6–7, 22, 39–42, 44, 57, 73, 305, 307, 314 and RACAP 157 supermarkets 80–1, 84, 86, 171–2 and food labelling 173, 178 Sutherland, Rory 187–8 tailored defaults. 307 tax payments 3, 8, 23, 52, 87–8, 88, 89, 112–14, 118, 120, 131, 181–2 in Central America 125 Council Tax 95 and habits 131 and lottery incentive 96–7 and online tax forms 74–5 and randomised controlled trials 274 road duty 3, 91, 92, 275–8 social-norm-based approach to 113, 115 Tetlock, Philip 192 Thaler, Richard 6–7, 22, 39, 44, 50, 51, 53, 57, 305 and BIT’s name 53 and RACAP 157 theft (see also crime prevention): mobile phones 173–6, 174, 175 and target-hardening 78, 214 vehicles: cars 169–70 motorcycles 66–7 time, perception of 128 time-inconsistent preferences 128, 139–45 Times 301–2 tobacco, see smoking Turner Lord (Adair) xii, 33, 331 Tversky, Amos 27, 29, 230 UK economy 205–12, 215, 216 (see also financial crisis; Gross Domestic Product) unemployment 120–1, 122, 197–205, 200, 201, 216, 343, 349 (see also jobcentres) and well-being 255–6 utilitarianism 221–2 utility suppliers: and data transparency 154–60 switching among 153–4, 155–6, 155, 160, 213 vehicles 18–20 safety of 177–8 and speeding 76–7, 92–5, 100 varied penalties for 147 thefts of: cars 169–70 motorcycles 66–7 Victoria, Queen 17 visas 132 Vlaev, Ivo 48 Volpe, Kevin 320 voter registration 95–6 Walsh, Emily 123 Wansink, Brian 171, 306 war 20–1 war and conflict 20–1, 27, 87, 344–5 weight, see obesity/weight issues welfare benefits 8 and conditional cash transfers 135, 145 and timing of payments 135 well-being 218–65 and community 249–55, 251 and commuting 219–20, 263–4 by country 229, 238, 243 drivers of 235–41 material factors 237–9 social factors 239–41 (see also well-being: and community) sunny disposition 235–7 early concepts of 220–2 and GDP 222–4, 255 and governance and service design 258–62 and happy-slave problem 231 and income, work and markets 255–7 and Life satisfaction paper 225 measuring 222–4 big questions concerning 231–3 subjective 228–31 and mental health 246–9 and National Citizenship Service programme 251–2, 251 by occupation 244 and policy 242–3, 258 subjective 224, 228–31 and giving 250 (see also giving) by occupation 244–5 and prostitutes 231–2 UK government’s programme on 226–8, 233–5, 234, 240 unemployment’s effects on 255–6 and utilitarianism 221–2 What Works institutes 281–90, 292–4, 340 Centre for Ageing Better 282 Centre for Crime Reduction 289 Centre for Local Economic Growth (LEG) 282, 288 Early Intervention Foundation (EIF) 282, 288 Educational Endowment Foundation (EEF) 282, 283–7, 284, 286 When Harry Met Sally 160–1 ‘wicked problems’ 170 Willetts, David 165 World Bank 125, 293, 309, 340 World Values Survey (WVS) 229 yelp.com 161–2 Young, Lord 279 ACKNOWLEDGEMENTS THERE ARE MANY people who deserve thanks and credit for the work and results of the Behavioural Insights Team that this book describes, and a rather shorter list for the writing and editing of the book itself.


pages: 397 words: 110,130

Smarter Than You Think: How Technology Is Changing Our Minds for the Better by Clive Thompson

4chan, A Declaration of the Independence of Cyberspace, Andy Carvin, augmented reality, barriers to entry, behavioural economics, Benjamin Mako Hill, butterfly effect, citizen journalism, Claude Shannon: information theory, compensation consultant, conceptual framework, context collapse, corporate governance, crowdsourcing, Deng Xiaoping, digital rights, discovery of penicillin, disruptive innovation, Douglas Engelbart, Douglas Engelbart, drone strike, Edward Glaeser, Edward Thorp, en.wikipedia.org, Evgeny Morozov, experimental subject, Filter Bubble, folksonomy, Freestyle chess, Galaxy Zoo, Google Earth, Google Glasses, Gunnar Myrdal, guns versus butter model, Henri Poincaré, hindsight bias, hive mind, Howard Rheingold, Ian Bogost, information retrieval, iterative process, James Bridle, jimmy wales, John Perry Barlow, Kevin Kelly, Khan Academy, knowledge worker, language acquisition, lifelogging, lolcat, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Netflix Prize, Nicholas Carr, Panopticon Jeremy Bentham, patent troll, pattern recognition, pre–internet, public intellectual, Richard Feynman, Ronald Coase, Ronald Reagan, Rubik’s Cube, sentiment analysis, Silicon Valley, Skype, Snapchat, Socratic dialogue, spaced repetition, superconnector, telepresence, telepresence robot, The future is already here, The Nature of the Firm, the scientific method, the strength of weak ties, The Wisdom of Crowds, theory of mind, transaction costs, Twitter Arab Spring, Two Sigma, Vannevar Bush, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize, éminence grise

As teenagers, 70 percent said religion was helpful to them; in their forties, only 26 percent recalled that. Fully 82 percent of the teenagers said their parents used corporal punishment, but three decades later, only one third recalled their parents hitting them. Over time, the men had slowly revised their memories, changing them to suit the ongoing shifts in their personalities, or what’s called hindsight bias. If you become less religious as an adult, you might start thinking that’s how you were as a child, too. For eons, people have fought back against the fabrications of memory by using external aids. We’ve used chronological diaries for at least two millennia, and every new technological medium increases the number of things we capture: George Eastman’s inexpensive Brownie camera gave birth to everyday photography, and VHS tape did the same thing for personal videos in the 1980s.

See Gmail search method, 33, 37 Google Blogger, 275 Google Chat, 42 Google Docs, 155 Google Earth, 62, 171 Google Glass, 138, 141–42 Gosling, Sam, 215–16 Graham, Steve, 184 Granovetter, Mark, 227–29 Gray, Brenna Clarke, 56 Great Firewall (China), 250, 271, 273 Greeks, ancient, on writing versus debate, 68–69, 75 Grindr, 81 Guardian, 170 Guardian Project, 274 Gurrin, Cathal, 33–35, 41–42 Gutenberg, Johann, 12, 118–19, 121 Haiti earthquake, 63, 265–66 Hajizada, Adnan, 268–69, 274 Haley, Ben, 209–10 Hamilton, Buffy, 207 Hamilton, Filippa, 108 hand waving, 53–54 Harris, Frances, 205–6 hashtag, development of, 65–66 Hayden, Theresa Nielsen, 79 Heath, Christian, 213 Hein, Ethan, 72–73 Henkin, David, 49 Hersman, Erik, 62 Hickey, Lisa, 215 Hinckl, Andy, 285–86 hindsight bias, 27 Historia Naturalis, 40 history, learning through video games, 199–202 hive mind, 172 Holmes, Sherlock, 172–73 homophily, 230–31, 261, 261–63 Horvitz, Eric, 39 Hydra, 5 hyperlinks, early concept, 123 index, origin of, 121 India, and online dissent, 275–76 Innis, Harold, 8 innovation and discovery eureka moments, 131–32 theory of multiples, 58–66 Instagram, 109–10 Instapaper, 136 Internet censorship, global view, 250 early visionaries on, 122–23 human dependence on, 116 as social observation tool, 153 Internet & American Life Project, 187–88 Iran dissidents, identifying online, 270 media bans in, 267 photomanipulation, use of, 107 Ito, Mizuko, 210–11 Jackson, Maggie, 137 Jacobi, Emily, 261 James, William, 237 Jardin, Xeni, 108 Jcham979, 94–95, 98 Jenkins, Henry, 187, 202 Jennings, Ken, 282, 288 Jeopardy!

See geolocation; mapping Loftus, Elizabeth, 24–25 Logo, 190–93 Logo Microworlds, 192 LOLcat-crafting, 108–9 Looxcie, 41 Los Angeles Times wikitorial, 159 Lost (TV show), 96 Lostpedia, 187 Luff, Paul, 213 Lunsford, Andrea, 66–68 Luria, Alexandr, 40 Luther, Martin, 249 McCain, John, 88 McIntosh, Jonathan, 100 MacKinnon, Rebecca, 270, 276 McLuhan, Marshall, 8, 102 McPherson, Sam, 187 Mad Libs, 191 MadV, 101 Magna Carta, 276 Maher, Ahmed, 255 Mahfouz, Asmaa, 259 MakerBot, 111–12 maker movement, 103 Malebranche, Nicolas, 119–20 Manjoo, Farhad, 261 Mann, Steve, 266–67 Many Eyes, 91–92 mapping electoral districts, tool for, 84–86 Haiti earthquake relief, 265–66 Ushahidi, development of, 62–63 Marconi, Guglielmo, 59 Marcus, Gary, 14 Maree, Daniel, 265 marginalia, 82 Mario Kart (video game), 37 Mark, Gloria, 135–36, 137 Mark, Kevin, 79 Martin, Trayvon, 264–65 mash-up videos, 100 math digital instruction, 175–78, 181–83, 191 learning difficulty related to, 189 “Mathematical Creation” (Poincaré), 131–32 Maverick, Augustus, 6 Mayer-Schönberger, Viktor, 42, 241 Mechanical Turk, 1 media convergence, 111 medical diagnosis supercomputer, 284–85 meditation, 137–38 Meier, Patrick, 266 memex, 123, 143 memorization opponent of, 119–20 proponents of, 132–33 memory. See also forgetting artificial, lifelog as, 29–44 context, importance of, 26 digital aids, 27–28 and digital tools, lack of research in, 134–35 episodic memory, 25–27 hindsight bias, 27 knowledge, incorporating, 129, 133 limitations of, 24–27 loss, as popular topic, 23–24 for meaning over details, 129, 133–34 search, efficiency of, 32 semantic memory, 116 social memory, 124 spaced retention, 144–45 transactive memory, 124–31 writing, benefits for, 57 versus written word, historical view, 117–20 Menger Sponge, 113 Mercury Grove, 217 Merton, Robert, 60 Mesopotamian writing, 116–17 metamemory, 124–25 microblogging, forms of, 76–77 micro-celebrity, 238 microfilm, 123 Mill, John Stuart, 133 Milli, Emin, 268–69 “Million Follower Fallacy, The” (Cha), 234–35 Million Hoodie March, 265 Miloševic, Slobodan, 267 mindfulness, 14, 137–38, 232 Minority Report (film), 105 Minsky, Marvin, 72 Miyamoto, Shigeru, 149 Mnemosyne, 133 Moholy-Nagy, László, 110 Momus, 238 Montaigne, Michel de, 120 Moodscope, 90 Moore’s Law, 90 Morozov, Evgeny, 270 Morse, Samuel, 95 moving image.


pages: 231 words: 64,734

Safe Haven: Investing for Financial Storms by Mark Spitznagel

Albert Einstein, Antoine Gombaud: Chevalier de Méré, asset allocation, behavioural economics, bitcoin, Black Swan, blockchain, book value, Brownian motion, Buckminster Fuller, cognitive dissonance, commodity trading advisor, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, delayed gratification, diversification, diversified portfolio, Edward Thorp, fiat currency, financial engineering, Fractional reserve banking, global macro, Henri Poincaré, hindsight bias, Long Term Capital Management, Mark Spitznagel, Paul Samuelson, phenotype, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, rent-seeking, Richard Feynman, risk free rate, risk-adjusted returns, Schrödinger's Cat, Sharpe ratio, spice trade, Steve Jobs, tail risk, the scientific method, transaction costs, value at risk, yield curve, zero-sum game

The retrospective safe haven fallacy, put simply, is playing Monday morning, armchair quarterback (meaning making judgments after a game is over, from the safety of an armchair, as opposed to while actually playing on Sunday when NFL games are typically played), second‐guessing or reenacting real‐time decisions after those decisions were actually made by someone else. It is also known more formally as retrospective determinism, hindsight bias, or, best of all, the knew‐it‐all‐along phenomenon, whereby it is accepted that because a series of events, such as a market crash, happened a certain way under specific historical circumstances, what occurred was therefore the inevitable consequence of those circumstances. Risk somehow always appears so obvious and predictable, and the last crash always made so much sense, retrospectively, that is—based on what we now know, but what wasn't known at the time.

See also Cost‐effectiveness analysis (CEA) with insurance, 88–92, 94–95 and Kelly criterion, 81, 84–87 and Kelly optimal bet size, 84, 86 with Nietzsche's demon, 70–72 and non‐ergodicity, 72–77 optimizing, 53–54 in Petersburg merchant trade, 48–52 in Petersburg wager, 41–43 raising, 187 with Schrödinger's demon, 68 of SPX portfolio with safe havens, 132–135 as time average, 75 25‐year compounded SPX returns, 121 for US Treasuries, 172 Geometric effect: in cost‐effectiveness analysis, 136–142 for gold, 181 in Petersburg merchant trade, 47 for real‐world safe havens, 184 and safe haven frontier, 186 tradeoff between arithmetic cost and, 152 when reshuffling returns, 143 Geometric growth, 49, 50 Geometric mean maximization criterion, 54, 80, 81 Geometric random walk, 70 Gladiators, 162 Global macro strategy, 108 Goal of investing, 15, 99 Gold, 166, 178–184, 187 Golden Theorem, 30, 67, 70, 73 Goldilocks weightings, 81, 91–92, 134 Graham, Benjamin, 9, 55, 83, 104, 151 Great Pirates, 157–160, 198 Growth: compound, 49–50, 135 compound annual growth rate, 15, 16, 20–21. See also Compound annual growth rate (CAGR) geometric, 49, 50 Growth rate of wealth, 17, 75 H Häkkinen, Mika (“The Flying Finn”), 155, 156 Hedge funds, 163, 175–178, 182–184 Hedging, 17 Hemingway, Ernest, 4, 21 Herrigel, Eugen, 13 Hindsight bias, 113 Holism: agnostic investing, 144–148 bootstrapping, 127–129. See also Bootstrap methodology clinical trials, 129–136 cost‐and‐effect relationship, 136–142 emergent properties in, 124–126 and framing, 151–157 and offensive defense, 148–151 reductionism vs., 123–127 and reshuffling of returns, 142–144 and specialized vs. localized thinking, 157–160 Hopeful havens, 112–114 Horizontal profit and loss, 48 Hybrid safe havens, 106 Hydraulica (Johann Bernoulli), 31 Hydrodynamica (Daniel Bernoulli), 31 Hypothesis(—es): about effects of safe havens, 130.


pages: 677 words: 121,255

Giving the Devil His Due: Reflections of a Scientific Humanist by Michael Shermer

Alfred Russel Wallace, anthropic principle, anti-communist, anti-fragile, barriers to entry, Berlin Wall, Black Lives Matter, Boycotts of Israel, Chelsea Manning, clean water, clockwork universe, cognitive dissonance, Colonization of Mars, Columbine, cosmological constant, cosmological principle, creative destruction, dark matter, deplatforming, Donald Trump, Edward Snowden, Elon Musk, fake news, Flynn Effect, germ theory of disease, Great Leap Forward, gun show loophole, Hans Rosling, heat death of the universe, hedonic treadmill, helicopter parent, Higgs boson, hindsight bias, illegal immigration, income inequality, intentional community, invisible hand, Johannes Kepler, Joseph Schumpeter, Kim Stanley Robinson, laissez-faire capitalism, Laplace demon, luminiferous ether, Mars Society, McMansion, means of production, mega-rich, Menlo Park, microaggression, military-industrial complex, moral hazard, moral panic, More Guns, Less Crime, Multics, Oklahoma City bombing, Peter Singer: altruism, phenotype, positional goods, power law, public intellectual, race to the bottom, Richard Feynman, Ronald Coase, Silicon Valley, Skype, social intelligence, Social Justice Warrior, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Suez crisis 1956, TED Talk, the scientific method, The Wealth of Nations by Adam Smith, Timothy McVeigh, transaction costs, WikiLeaks, working poor, Yogi Berra

These heuristics are also known as cognitive biases because they often distort our percepts to fit preconceived concepts, and they are part of a larger process called “motivated reasoning,” in which no matter what belief system is in place – religious, political, economic, or social – they shape how we interpret information that comes through our senses and motivate us to reason our way to finding the world to be precisely the way we wish it were. As I argue in The Believing Brain, our beliefs are formed for a variety of subjective, emotional, psychological, and social reasons and then are reinforced through these belief-confirmation heuristics and justified and explained with rational reasons.8 The confirmation bias, the hindsight bias, the self-justification bias, the status quo bias, the sunk-cost bias, the availability bias, the representative bias, the believability bias, the authority bias, and the consistency bias are just a few of the ways cognitive psychologists have discovered that we distort the world. It is not so much that scientists are trained to avoid these cognitive biases – as I argued in Why People Believe Weird Things, smart people can be even better at rationalizing beliefs they arrived at for nonsmart reasons – as it is that science itself is designed to force you to ferret out your errors and prejudices because if you don’t someone else will, often with great glee in a public forum, from peer-review commentary to social media (where all pretentions to civil discourse are stripped away).

By contrast, and as a test of sorts, there are the counterexamples of über-smart, creative, hardworking people who never prosper. If genes and environment are everything (or nearly so), then why do so many people with good genes and lugubrious environments fail (or at least fail to succeed, if only living mediocre lives)? We cannot simply employ the hindsight bias by taking only successful people and looking to see what they did to become successful and then back-engineer those traits, package them into a program (or self-help book!), and dispense it into the world for consumers to imbibe and prosper. That’s not how science works. I call this the Biography Bias, evident in the reception of Walter Isaacson’s bestselling biography of Steve Jobs, as readers scrambled to understand what made the mercurial genius so successful.

., 295 Grobman, Alex, 20, 78 Gross, Paul, 317 gun control denying publicity to mass murderers, 179–180 effects in Austria, 187–190 high-capacity magazines, 175 proposals for, 179–180 proposals to prevent Sandy Hook Events, 171–175 rights of citizens and, 175–176 statistics for individual homicides and mass killings, 163–165 gun control debate deaths from gun violence compared to terrorism violence, 191–192 defense against tyranny argument, 178–179 different metaphors for the nation as a family, 192–197 link between gun onwership and gun deaths, 182–191 machine gun regulation and restriction, 191 self-defense argument, 177–178 views of John Lott, 182–191 what conservatives and liberals really differ on, 192–197 gun culture effectiveness of gun controls, 29 gun law reform Australia, 174–175 guns in the home statistics for deaths related to, 164–165 Guth, Alan, 122 Haidt, Jonathan, 65, 74, 132, 245, 303 Hamlet, 265 Hancock, Graham claim of an ancient lost civilization, 311–327 Handbook of Philosophy and Public Policy, 44 Harari, Yuval Noah, 130 Hare, Robert, 165 Harrett, Clark, 238 Harris, Eric, 169 Harris, Sam, 87, 241, 304 Harvey, William, 229 hate speech question of banning, 28–37 response to, 13–16 Have Gun – Will Travel (television show), 298 Hawass, Zahi, 315 Hawking, Stephen, 111, 124–125 Hayek, Friedrich, 216–217 Heavens on Earth (Shermer), 103, 109, 310 hedonic treadmill, 201, 202, 210 helicopter parenting, 65, 74 Hemenway, David, 186 Hensley, Melody, 73 Herschel, John, 45 heuristics, 23–24 Heyerdahl, Thor, 315 Heying, Heather, 303 hindsight bias, 24 Hitchens, Christopher, 1, 16, 55, 82, 87, 210debate about his beliefs, 276–281 dinner and drinks with, 282–286 on freedom of speech, 3–6 on hate speech, 13 sense of loss following his death, 276 Hitchens’ Dictum, 5–6 Hitchens’ Theorem, 5 Hitler, Adolf, 28, 31 HMS Bounty mutineers society on Pitcairn Island, 156–159 Hobbes, Thomas, 139, 229, 240, 309 Hoffman, Donald, 305–306 Holmes, James, 170 Holmes, Oliver Wendell, Justice, 1, 2–3, 16 Holmes’ Axiom, 3 Holocaust denial, 5, 20–21, 22–23, 38–43as a criminal act, 38–43 Hooker, Joseph, 45, 287 Horowitz, David, 282 Houdini, Harry, 270, 283 how lives turn out free will–determinism debate, 264–265 human nature and, 256–258 Just World Theory, 255 role of contingency, 258–264 role of environment and society, 258–264 role of luck, 258–264 Unjust World Theory, 255–256 views on important influences, 255–258 How We Believe (Shermer), 87 Hubbard, L.


pages: 322 words: 77,341

I.O.U.: Why Everyone Owes Everyone and No One Can Pay by John Lanchester

Alan Greenspan, asset-backed security, bank run, banking crisis, Bear Stearns, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Monday: stock market crash in 1987, Black-Scholes formula, Blythe Masters, Celtic Tiger, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, Daniel Kahneman / Amos Tversky, diversified portfolio, double entry bookkeeping, Exxon Valdez, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, fixed income, George Akerlof, Glass-Steagall Act, greed is good, Greenspan put, hedonic treadmill, hindsight bias, housing crisis, Hyman Minsky, intangible asset, interest rate swap, invisible hand, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", Jane Jacobs, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, junk bonds, Kickstarter, laissez-faire capitalism, light touch regulation, liquidity trap, Long Term Capital Management, loss aversion, low interest rates, Martin Wolf, money market fund, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, negative equity, new economy, Nick Leeson, Norman Mailer, Northern Rock, off-the-grid, Own Your Own Home, Ponzi scheme, quantitative easing, reserve currency, Right to Buy, risk-adjusted returns, Robert Shiller, Ronald Reagan, Savings and loan crisis, shareholder value, South Sea Bubble, statistical model, Tax Reform Act of 1986, The Great Moderation, the payments system, too big to fail, tulip mania, Tyler Cowen, value at risk

What has had an effect, however, is the work of two Israeli psychologist-economists, Daniel Kahneman and Amos Tversky, who have produced a body of work studying “the susceptibility to erroneous intuitions of intelligent, sophisticated, and perceptive individuals,” in the words of the fascinating autobiography written by Kahneman on the occasion of winning the Nobel Prize in 2002. I have a confession to make about Kahneman and Tversky. I’d never heard of them until Kahneman won the Nobel,* and when I first read about their work, it seemed to me to consist of things which were surprising only to economists. One of their interests was “hindsight bias,” the way in which a random sequence of events is given structure and narrative by the false perspective of looking back over it from its outcome. Another was “loss aversion,” the fact that people place a higher value on not losing money than on gaining it; another was on “the law of small numbers,” referring to people’s tendency to draw overconfident conclusions from small amounts of evidence.

., 32, 214, 220 Haarde, Geir, 12 Haji-Ioannou, Stelios, 227 Haldane, Andrew, 36–37 Halifax, 38, 89 Hamanaka, Yasuo, 51 Harlot’s Ghost (Mailer), 172 health care, 13, 17, 198, 217, 222, 226–27 hedges, hedge funds, 164–66, 171 definition of, 54n–55n LTCM and, 54–56, 80, 142, 162, 164–65, 230–31 risk and, 49–50, 52, 58, 115, 155, 205 hedonic treadmill, 218 heuristics, 137–38 hindsight bias, 137 Hollinger, 59 Home Owners’ Loan Corporation, 99 Hong Kong, 7–8, 13–14 Hongkong and Shanghai Bank, 7, 53 Hoover, Herbert, 98–99 houses, housing, home ownership, 27–29, 40, 82–102, 109–32, 149, 157–60, 163–66, 187 balance sheets and, 27–28, 38 bubbles in, 5, 86–87, 89–90, 92, 101, 115, 159–60, 170, 173–74, 176–78, 216–17, 219, 223 foreclosures on, 83–85, 126–31 in Iceland, 10–11 inflation and, 88, 101, 179–80 in Ireland, 92, 110, 170–71 leverage and, 60–61, 83, 95, 97 liquidity and, 28–29, 90, 96–97 for low-income borrowers, 100, 113, 118, 121–23, 126–27, 130–31, 163 politics and, 87–89, 91, 96–101, 177–78 prices of, 5, 28–29, 37–38, 61, 71, 86–91, 101, 109–11, 113, 115, 125, 157, 160, 164–66, 173–76, 194, 208 and sense of dislocation, 95–97 in U.K., 38, 87–98, 110, 122, 177–78 in U.S., 37, 82–86, 95, 97–101, 109–10, 114–15, 122, 125–31, 157–58, 163 see also mortgages HSBC, HSBC Holding, 36, 53 Hume, David, 147 Hypo Real Estate, 40 IBM, 58, 65, 69 Iceland, Icelanders, 15 economic crisis in, 9–12, 23–24, 40, 170, 216, 223 pots and pans revolution in, 223 Iguchi, Toshihide, 51 illusion of validity, 140 incentives, 206–11, 224, 228 for bankers, 19, 37, 206–8 bond-rating agencies and, 209–11 incomes, 4, 13, 17, 66, 171, 203–4, 212, 221 balance sheets and, 26, 28, 30–31 banking and, 19–20, 37, 206–8, 218 housing and, 60, 90, 93–94, 100, 126, 130–32, 163 inflation and, 92, 179 India, 3–4 industrialization, 96–97 inequality, see equality, inequality inflation, 107, 144, 147, 220–21 asset price, 109–10 housing and, 88, 101, 179–80 incomes and, 92, 179 interest rates and, 102–3, 172–73, 178–80, 221 ING Group, 36 Innumeracy (Paulos), 8 insolvency, see solvency, insolvency interest, interest rates, 11, 24, 58–64 bonds and, 20, 61–63, 103, 107–10, 112, 144 and cost of money, 102–3 credit and, 172–73, 175, 209 derivatives and, 20, 47, 58, 63–64, 66, 69–70, 114, 121–22 government determination of, 102–3, 107–8, 172–80, 221 Greenspan and, 107–8, 165, 173–77 loans and, 59–60, 66, 74, 102, 108, 145, 172–73 mortgages and, 8, 58, 86, 89, 91–92, 95, 100, 102, 108, 110, 112–14, 122, 128, 145–46, 174, 176, 212 risk and, 69–71, 144–45, 165 International Monetary Fund (IMF), 15, 19, 186 International Swaps and Derivatives Association (ISDA), 79–80, 183 investing, investments, investors, 28, 58–63, 101–9, 171–72, 175–77, 181, 187, 213, 221 banks and, 25, 30, 43, 228 blue chip, 106 bonds and, 62–63, 102–3, 107–8, 111, 208–9 of China, 109, 176–77 derivatives and, 54–56, 58, 69–70, 73, 117, 120, 132, 153, 158, 172, 184 diversification of, 146–48 hedge funds and, 54n–55n housing and, 86–88, 97, 101 interest rates and, 102–3 regulation and, 225–26 risk and, 5, 68, 70, 88, 103, 144, 146–53, 158, 165, 184, 190 in stocks, 59, 73, 101–7, 111, 146–52, 158, 175, 192 values and, 60–61, 104, 198 investment trusts, 55n Ireland, 15, 169–71, 177 economic contraction in, 170–71, 222–23 housing in, 92, 110, 170–71 Jacobs, Jane, 82 Japan, Japanese, 18, 51–54, 77 banks of, 43, 51, 229 derivatives and, 51–52, 54 Johnson, Simon, 19–20, 185–86 Jorion, Philippe, 156–57, 162 J.P.


pages: 290 words: 82,871

The Hidden Half: How the World Conceals Its Secrets by Michael Blastland

air freight, Alfred Russel Wallace, banking crisis, Bayesian statistics, behavioural economics, Berlin Wall, Brexit referendum, central bank independence, cognitive bias, complexity theory, Deng Xiaoping, Diane Coyle, Donald Trump, epigenetics, experimental subject, full employment, George Santayana, hindsight bias, income inequality, Jeremy Corbyn, manufacturing employment, mass incarceration, meta-analysis, minimum wage unemployment, nudge unit, oil shock, p-value, personalized medicine, phenotype, Ralph Waldo Emerson, random walk, randomized controlled trial, replication crisis, Richard Thaler, selection bias, the map is not the territory, the scientific method, The Wisdom of Crowds, twin studies

Also – because we really have no idea in advance which cultural detail might be relevant – do we ask about work, transport, neighbourhood, community, health and religion in both countries, in meticulous detail, to find out which differ, just in case one of these details turns out to swing it? Even after the programme fails in Bangladesh, we might not initially know why, or where to look for the answer.6 The potential problems are too obscure – at least at the outset – to be within easy reach of discovery. Our self-justifying brains and hindsight bias are two of many reasons we think we are good at spotting causes. In truth, in advance, who could know which one detail – if any – might make knowledge hard-earned in one place fail in another? This is the overwhelming difficulty: how to know what will be relevant to whether knowledge will travel.

Index abstract formulas 141 Academy of Medical Sciences 133 adoption studies 41 aid, economic development 141 aid-effectiveness craze, the 153 alcohol consumption 180 AllTrials campaign 114–5 Altman, Doug 129–30 Amano, Yukiya 185 ambiguity 209–10 Amgen 111–2 Analysis (radio programme) 102 analytic validity 158, 263n18 anarchy 224 aphorisms 68–9, 149 apprenticeships 205–6 argument, beliefs and habits of 186 asthma 135 Attanasio, Orazio 225–9, 230 Autho, David 219–23 average knowledge 173 background influences 23–34 background norms, rejecting 24–5 bacon 161–3, 162–3 Banerjee, Abhijit 150–4, 157 Bangladesh 80–2, 82, 101–2, 158, 261n6 Bank of England 103, 216 Bank of Japan 103 Basbøll, Thomas 244–5 baseline data 165 base-rate neglect 176–7 basic laws 140 Bateson, William 245 BBC 88, 98 Beatles, the 52–3, 259n33 Begley, Glenn 111–7 behaviour context-specific 42–3 environmental cues 65–7 behavioural economics 157 Behavioural Insight Team 155, 156, 232 beliefs 60 contradictory 63–4 inconsistency of 60–6 justification 60–1, 63 manipulation 62–3 power of information on 66–8 self-contradiction 61–2 Berlin, Isaiah 199 betting, on knowledge 236–7 big causes, power of 35 big events causal intricacy 193–6 complexity 185–7 difficulty determining causality 188–96 power of circumstance 196–9 big picture, the 215–6 Bijani, Ladan 40–1 Bijani, Laleh 40–1 biographies 49 biological randomness 43–4 biomedical science, research standards 129–36 Bolsover 217–8 Boorstin, Daniel 17, 136, 138, 264n24 Booth, Charles 146–7 BP 211 brain, the 64 plasticity 56 self-justifying 83 breast cancer 45–6, 46 Brexit referendum 18–9, 20, 90, 214–8, 223–4, 241 Bunnings 77 Burckhardt, Jacob 255n20 Burke, Edmund 269n1 Burns, Terry 102–3 business decisions, failures 210–1 cancer 45–8 breast 45–6, 46 lung 174–5 risk 162–3, 166, 174–5 screening 132–3 Cancer Research UK 133 canned laughter 154–5 capitalism 118 Carillion 211 Carp, Joshua 123–4 Cartwright, Nancy 79, 79–82, 82, 193–4, 195, 202–3, 203–4, 263n18 causal instincts 123 causal interactions, complexity 239 causal intricacy 193–4 causal models 242–4, 243, 269–70n3 causal theorizing 212–4 causality assumption of 212–4 difficulty determining 188–96 existence of 276–7n12 hard 225–9 importance of 212 mechanical models 242–4, 243 in one person 48 cause and effect dependable 203–4 patterns of 23, 25–6, 26 supposed 248 unreliable 204 causes and causal influences 90, 94 competing 248 criminals 29 interaction 193–6 and luck 178 secret life of 8–11 simple 184–5 cells, biographical stories 47–8 certainty, desire for 235 Chadwick, Edwin 146–7 chance 14, 37–8, 247, 281n1 chaos theory 56–7, 276n10 Chater, Nick 59, 60, 63, 64–5, 66–7 Chernobyl disaster 185 child and adolescent development 23–6, 41–2 child mental health 206–7 childhood influences 23–5 delinquent boys 26–34 China, rise of 218–23, 279n19 choice, situated 31–3, 34 choice blindness 62 choices 60 Cialdini, Robert 154–5 Cifu, Adam 131–2 circumstances 70 power of 196–9 claims inflation 130 climate change 238–9 Clinton, Hillary 222 Cochrane Collaboration, the 189–90 cognition 64 cognitive biases 14 cognitive limitations 14, 214 Comaroff, John 107–8 common sense 69–70 comparative cost analysis 173 competence 236–7 complacency 237 complexity adding 244 big events 185–7 facing 15 hidden 184–201 of reality 245 complexity theory 276n10 complexity-avoidance 187 complications, hidden 187 Conan Doyle, Arthur 108 confidence 72 consistency 68–75, 202–4, 260n6, 260n8 constructive realism 17 consumer behaviour 77 context 41–2, 72, 101 context-specific behaviour 72 context-specific learning 42–3 control alternative to 248–9 elusiveness of 85–6 powers of 195 conviction 104 coping strategies 16–7, 225–46 adapting 230–3 betting 236–7 communicate uncertainty 237–9 embracing uncertainty 234–6 exceptions 244–5 experiment 230–3 governing for uncertainty 239–41 managing for uncertainty 241–2 metaphors 242–4 negative capability 234 relax 246 triangulation 233–4 use of probability 242 Corbyn, Jeremy 20 corporate power 241 cost/benefit analysis, cows 117–22 cows, cost/benefit analysis 117–22 Coyle, Diane 216, 262n12 Crabbe, John 85–7 credibility 238–9 credibility crisis 18 crime causes of 142–4 heroes and villains view 142 opportunist 144–5 reduced opportunity 144–5 theory of 142–6, 143 victims and survivors view 142–3 criminals causal influences 29 childhood influences 26–34 desisters 30 high rate chronics 30 life-course persistent offenders 28–9 life-courses 28, 236 variables 31 critical factors 83–5 crowds, wisdom of 149 cultural difference 79–82, 79–85 Daniels, Denise 43–4, 57 Darwin, Charles 50–1 data granularity 216–7 interpretation 98–100 Dawid, Philip 276–7n12 De Rond, Mark 198, 201 de Vries, Ymkje Anna 114 deadweight cost 205–6 debate 98 decision making 58–60 influences 32–3 situated choice 31–3 deep preferences 65 deeper rationale, construction of 60 Deepwater Horizon 211 defining characteristics 43 degrees of freedom 122–9 delinquent boys 26–34 dementia 176–7, 274n16 democracy 20 Deng Xiaoping 219 Denrell, Jerker 199, 201 desires 59 details importance of 49–54 neglecting 151–2 problem of 229 selective 26 determinism 28 development economics 150–3 developmental difference, sources of variation 9–11 developmental noise 10 difference 15 pockets of 214–24 Dilnot, Andrew 237, 275n3 disciplined pluralism 231 disorder 45 forces of 11–3 doubt 238 Down’s syndrome 166 drugs comparative cost analysis 173 impact 171–2 medical effect 167–9, 169, 170–4 non-responders 172 Numbers-Needed-to-Treat (NNTs) 168, 169, 170, 173–4 predictive weakness 170–3 duelling certainties 235 Duflo, Esther 83, 84, 141, 150–3, 157–8, 158–9, 230–1 ecological validity 263n18 economic development, aid 141 economic forecasting 92, 102–7 economic recovery 217–8 economics 233 economy, the 87–100, 91, 93, 94, 95 education 151–2, 206–7, 275– 6n7 Einstein, Albert 140–1 Emerson, Ralph Waldo 68 enigmatic variation 13–6, 48 environment context 72 non-shared 37 shared 35 environmental influences 43–4 epidemiology 181 epigenetics 6–7 erratic influences 60 essential you, the 59–60 estimates 89–91, 96 European Central Bank 103 evidence 21 balance of 114 conclusive 186, 187 the Janus effect 121, 122–9 limitations of 117–22 statistical significance 137 strength of 137 evidence-based medicine 133–4 exceptions 214–24, 244–5 expectations 35 big 196 frustration of 15 of regularity 47, 202–4 unrealistic 182 experience, influence of 33, 34, 55–7 experiment 230–3 expertise, crisis of 18–9 experts, credibility crisis 18–9 external validity 101, 158, 263n18, 264n19 extreme performance 199 failure 204–11 fairness 66–7 false negatives 113–4 false positives 113–4, 122 falsification 245 family, changes of 41 farmer and a chicken, the 202–4 fate 30 fears, exaggerated 46 Financial Times 77 First World War 108 Fitzroy, Robert 50 flat mind, the 60, 60–8 Flaubert, Gustave 139 forecasting 109 former Yugoslavia 108 foxes 199 France 186–7 Freedman, Sir Lawrence 108, 109 freedom 236 Fukushima nuclear power station meltdown 185–7 fundamentals 141 identifying 153 further education 208–9 Galbraith, John Kenneth 110 Gartner, Klaus 87 Gash, Tom 142–3 Gates, Bill 199 GDP data 262n12 growth estimation 88–100, 91, 93, 94, 95, 262–3n14 local 214–5, 216, 218 Gelman, Andrew 124–5, 244 gene–environment interaction 6–7 general principles 140 generalities 174 generalization 76–8, 146, 152, 263n18 genes and genetics influence of 34–7, 39–41, 44, 45–7 overclaiming 134–5 power of 33, 45 genetic risk 45–7 genius, dangerous 212–4 genotype 8 Germany 185, 186, 188 Gillam, John 77 global financial crisis, 2008–9 104, 106, 210, 235 globalization 213 Gove, Michael 18–9 granularity 216–7 ground truth 217 groupthink 149 guarantees, lack of 160 Guardian 207 Gupta, Rajeev 117, 118 Haldane, Andy 216–7, 218 Harford, Tim 156–7, 237 Harris, Judith Rich 40–2, 72 Hayek, Friedrich 105–6 health screening 177 heart disease 163–6 hedgehogs 199 Henry (ex-delinquent) 32 Hensall, Abigail 39–40, 41 Hensall, Brittany 39–40, 41 herd mentality 154–5 hidden causes 35–8 hidden half, the coping strategies 225–46 ignoring 202–24 mystery of 35 power of 44–5 hidden trivia 8–9 hindsight 78 hindsight bias 83 history 107–8 lessons of 109 Homebase 76–7 Honda, US motorcycle market penetration 196–9 hubris 77 human sameness irregularity 45–9 limits of 34–45 human understanding, fundamentals 213 Human Zoo, The (radio programme) 60–6 humility 224, 248–9 IBM 199 ibuprofen 163–5 ideological divide 240 ideologies 9–10 idiosyncratic influence 53–4 ignorance 21, 107 disguising 242 the shock of 7 imagination 138 impulsive judgement, value of 149 incarceration rates, United States of America 222, 240, 280n10 incidentals, effect of 51–2 incoherency problem, the 149 inconsistency beliefs 60–6 justifiable 70–1 incredible certitude 209 Indian Express 117 individual differences 56 individuality conjoined twins 39–42 neurological foundation of 56 industrial policy 208 inflation 102–7 influences background 23–34 childhood 26–34 criminals 26–34 decision making 32–3 environmental 43–4 erratic 60 hidden 204 microenvironmental 8–9, 253–4n12 information power of 66–8 selective 66–7 Institute for Fiscal Studies 205–6 Institute for Government 208–9 intangible differences 253n11 intangible variation 10, 229 interaction, problems of 193–6 internal validity 101–2, 158 International Journal of Epidemiology 43 intuition 54, 204 Ioannidis, John 121, 133–6 irrationality, human 14 irregularity 94 disruptive power of 224 frustration of 15 human 45–9 influence 12 problem of 229 underestimating 214–24 Islamic State 108 it’s-all-because problem 91, 96 James, Henry 29, 56 James, William 141 Janus effect, the 121, 122–9 Johansen, Petter 62 Johnson, Samuel 214 Johnson, Wendy 71–2 Jones, Susannah Mushatt 162–3, 165 journalism 237–8 Juno (film) 193 Kaelin, William 130 Kawashima, Kihachiro 197 Kay, John 16, 68, 197, 231, 232 Keats, John 138–9, 234 Kempermann, Gerd 56, 57 Keynes, John Maynard 107, 271n9 Keynesianism 103 King, Mervyn 103, 104, 106, 110 Kinnell, Galway 28 Knausgaard, Karl Ove 86–7 Knight, Frank 107 Knightian uncertainty 107 knowledge 12–3, 170 advance of 20–1 average 173 betting on 236–7 credibility crisis 18 critical factors 83–5 failures of 19, 76–8, 79–82 fallibility of 248 generalizable 234 generalization 76–8 illusion of 136, 138 lessons of the past 102–7, 107–10 in medicine 182 negative capability 138–9 as obstacle to progress 17 obvious 82 paths to 136–9 plausibility mistaken for 132 practical 30–1 pretence of 105–6 probabilistic 160, 161, 163–4, 172–3 and probability 180 problem of scale 177–80 provenance 116 relevant 82–5 replication crisis 111–7 subverting 76–110 and time variations 87–100, 91, 93, 94, 95 transfer 37, 76–8, 83, 101–2 unknowns 85–7 validity 100–2 validity across time 107–10 weakest-link principle 79–82 Krugman, Paul 210 Lancet 225–6 Langley, Winnie 51, 165, 178 Laub, John 26–34, 42 law-like effects, claims about 21 learning styles 207 Leicester City Football Club 199–201 Leon (ex-delinquent) 31–2 Leyser, Ottoline 114 life, mechanics of 51 life-course persistent offenders 28–9 limits and limitations 16–7, 44, 75 base-rate neglect 176–7 of cleverness 278n14 individual level 174–6, 178–9, 181–3 lack of guarantees 160 marginal probabilistic outcomes 176–7 medical effect 167–9, 169, 170–4 on prediction 165–6 on probability 160–83 problem of scale 161–6, 174– 6, 177–80, 181–3 Liskov Substitution Principle 261n3 Little Britain (TV comedy) 192 Liu, Chengwei 198, 201 lives, understanding 29 location shift 264n20 Loken, Erik 124–5 long-acting reversible contraceptives (LARCS) 190 luck 37–8, 48, 178, 198 lung cancer 174–5 Lyko, Frank 1, 2 machine mode thinking 151–2 Macron, Emmanuel 20 Manski, Charles 209, 235 Mao Zedong 218 marginal probabilistic outcomes 176–7 marmorkrebs 1–9, 4, 10, 12, 12–3, 22, 35, 81, 182, 252n2 Marteau, Theresa 65 Martin, George 52 May, Theresa 208 Mayne, Stephen 77 measurement 99–100 mechanical relationships 212, 242, 244 mechanical thinking 242–4, 243 media stigma 192–3 medical effect, drugs 167–9, 169, 170–4 medical reversal 131–3 medicine comparative cost analysis 173 knowledge in 182 non-responders 172 Numbers-Needed-to-Treat (NNTs) 168, 169, 170, 173–4 personalized 181–3 predictive weakness 170–3 probability and 167–9, 169, 170–4 memory 56, 102–7 Mendelian randomization 233 Menon, Anand 214–5 mental shortcuts 14–5 mere facts 202–3 meta-science 19, 20 methodological revisions 97–8, 120 mice 55 microenvironmental influences 8–9, 253–4n12 micro-irregularity 35–7 micro-particulars 128 Microsoft 147–50, 199 Miller, Helen 66–7, 67 mind, the flat 59–60, 60–8 shape 59 models and modelling 140, 242–4, 243, 269–70n3 moment when, the 52 morality, changing 108 More or Less (radio programme) 237 Munafò, Marcus 234 Nadella, Satya 147–8 National Survey of Family Growth 192 National Surveys of Sexual Attitudes and Lifestyles 191–2 nationalism 108 Nature 2, 112, 136, 168, 174 nature/nurture debate 3, 5–6, 9–10 negative capability 138–9, 234 neurology 58 New England Journal of Medicine 131–2 Newcastle upon Tyne 214 Newton, Isaac 140–1 noise 14 definition 10 developmental 10 as intellectual dross 11 re-appraisal of 11–3 non-shared environment 37 Nosek, Brian 129 noses 49–51 Nottingham 217 Numbers-Needed-to-Treat (NNTs) 168, 169 nurture, influence of 44 O’Connor, Sarah 217–8 Office for National Statistics 89, 92, 98, 99–100, 216 O’Neill, Onora 238 opinions 21, 59 order 11–2, 13 organ donation campaign 155–6 outside influence 44 overclaiming 134–5 overconfidence 21 overseas business expansion 76–8 Oxfam, sexual abuse scandal 210 Paphides, Pete 52–3 parental behaviour 41 parents, impact of 41 Parris, Matthew 63 parthenogenesis 1–2 particularism 271–2n15 particularity problem, the 93 past, the, lessons of 102–7, 107–10 pattern-making instinct 21 patterns 13 pendulums 57 perceptual systems 64 performance 72–5 personalized medicine 181–3 Peto, Richard 47–8 phenotypes 8 physiognomy, and character 50 plausibility 132 Plomin, Robert 43–4, 49, 57 pluralism 231–2 polarization 235 policy making 231–2 appraisal 277n4 chances of success 208 failures 204–9 governing for uncertainty 239–41 and probability 178–9 secret of 209 seminar 207–8 sequential changes 208 political assumptions, fall of 20 political beliefs 60–6 population validity 263n18 populism, rise of 20 poverty 240–1 Prasad, Vinayak 131–2 precision 183 predictability 28 predictive weakness 165–6, 170–3 preferences 59, 62 deep 65 priming 126–8 probabilistic knowledge 160, 161, 163–4, 170, 172–3 probability 54, 70, 107, 258n25, 272n2 advantages 177–80 base-rate neglect 176–7 difference in 30 fear of low probabilities 166 individual level 174–6, 178–9, 181–3 limits and limitations 160–83 marginal 176–7 medical effect 167–9, 169, 170–4 paradox 170 and policy making 178–9 predictive weakness 165–6 problem of scale 161–6, 174– 6, 177–80, 181–3 recognizing significance 161 risk evaluation 161–6 suggestion of knowledge 180 use of 242 usefulness 161 problems, conceptualizing 17 productivity growth 209–10 progress, knowledge as obstacle to 17 psychoanalysis 58 psychology 58 Pullinger, John 278n14 Pullman, Philip 37 quantification, risk and risk-taking 162–5 racism 125–6 radical uncertainty 106, 107 Radio, Andrew 102 rage to conclude, the 139 randomized controlled trials, value of 280n6 randomness, pure 9 Ranieri, Claudio 200–1 rationality 68, 260n6, 260n8 reality 230, 245, 254n14 reciprocity 155 reflection 65–6 regularity 73, 160 assumption of 212–4 expectations of 47, 202–4 search for 212, 230 statistical 240–1 replication crisis 18, 111–7, 117– 22, 129, 136, 138 Replication Project 129 research 111–39 balance of evidence 114 breadth 130 claims inflation 130 confidence in 115–6 credibility crisis 18 decision rules 136–9 depth 130 evidence-based medicine 133–4 false negatives 113–4 false positives 113–4, 122 fragility 128–9 freedom 122–9 half wrong 113, 115–6 the Janus effect 121, 122–9 limitations of 117–22 micro-particulars 128 multiple analyses 125–6 multiple conclusions 117–22 overclaiming 134–5 priming 126–8 redemption 20 replication crisis 111–7, 117– 22, 129, 136, 138 rigour 19 scepticism 115–6 standards 129–36 statistical significance 122 triangulation 138 validity 101–2 research-credibility crisis 18 rigour 19, 246 risk and risk-taking 70–1, 107, 186 alcohol consumption 180 cancer 162–3, 166, 174–5 communication of 133 evaluation 161–6 heart disease 163–6 quantification 162–5, 166 quantified 187 risk-perception 71 Rockhill, Beverly 181 Rolling Stone magazine 23 Rose, Geoffrey 175–6 Rowntree Joseph 146–7 Royal Bank of Scotland 211 Russell, Bertrand 202, 202–3 samples, validity 100–2 Sampson, Robert 26–34, 42, 236 sanitation 225–9 Santayana, George 109 scale, problem of 161–6, 174–6, 177–80, 181–3 scepticism 105, 115–6, 128, 206 schizophrenia 34–6, 256n10 Science 56 Scientific American 55 Scotland, Triple-P parenting programme 206 screening 132–3, 177 searing memory, doctrine of the 102–7 selection bias 244 self-understanding 67 Sense about 115 serendipitous events 43, 52–3 sex education, role of 189–90 short-term gene–environment interaction 7 significance, recognizing 161 Silberzahn, Raphael 125–6 Simmons, Joseph 122–3 situated choice 31–3, 34, 42 situations, appraisal of 72 sliding-doors moments 50 small differences, power of 56–7 small effects, influence of 49–54 small experiences, influence of 35–7 smartphones 97, 191 Smith, George Davey 50, 51, 234, 281n1 social contexts 31, 195 social media 191 social mobility 240–1 social policy 195 social proof 154–6 social reformers 146–7 social science, utility of 146–50 special theory of relativity 140–1 Spiegelhalter, David 180, 244–5 spontaneous interaction 9 stagflation 103 statins 171 statistical regularities 240–1 statistical significance 122, 137 stents, use of 131 stories and storytelling 25–6, 53–4, 244–5, 247, 248, 258n25, 258n27 structural forces 54 Sun, the 51 support factors 194 Surfers Against Sewage 70–1 surgeons, skills 73–4 system 1 thinking 149 systematic forces 54 systems-level thinking 153 Tamil Nadu 79–82, 101–2 Tangiers, Morocco 84 technology, changing 108 Teen Mom (TV show) 193 teenage pregnancy rate decline in 184, 188–96 estimates 275n3 terrible simplifiers 255n20 Tesco 77, 211 Thaler, Richard 157 theories 140–59 analytic validity 158 arguments about 150–4 of crime 142–6, 143 development economics 150–3 fitness 157 implementation 152 limitations 157 and practice 141 refining 156–7 relevance 157–8 social science 146–50 tension in 154–9 using 156–7 ‘thick’ description 86 time, validity across 107–10 Time magazine 193 time variations, and knowledge 87–100, 91, 93, 94, 95 The Times 63 toilets 225–9 Toshiba 211 trade-offs 190–1 trends 54 trials 156 triangulation 138, 233–4 Triple-P parenting programme 206–7 trivia, importance of 84–5 true uncertainty 107 Trump, Donald 20, 218, 222, 223–4 trust 238 trust deficit 218 trustworthiness 238 Tufte, Edward 139 turning points, variety 49–54 TV crime shows 143, 143 twins and twin studies conjoined 39–42 identical 34–7, 39, 256n10 Tyson, Mike 23, 23–6 Tyson, Rodney 24–5, 255n3 Uhlmann, Eric 125–6 uncertainty 89–90, 100, 209– 12, 254n14 admitting 238 communicating 237–9 data 89–91 embracing 234–6 erratic 93 governing for 239–41 Knightian 107 language of 238 managing for 241–2 in medicine 167–9, 169, 170–4 perpetual 230 radical 106, 107 true 107 uncertainty laundering 268n33 understanding hidden half of 13 limiting effects on 14 limits of 54 unemployment 221–2, 263n17 unintended consequences 105, 229 United States of America China trade 220–3 incarceration rates 222, 240, 280n10 labour market 221 minimum wage 266–7n10 unemployment 221–2 universal gravitational attraction, theory of 140–1 unknowns 85–7, 206 unusual, the 195 upbringing 23–5 Uyeno, Lori 47 validity across time 107–10 analytic 158, 263n18 ecological 263n18 external 101, 158, 263n18, 264n19 internal 101–2, 158 knowledge 100–2, 107–10 population 263n18 research 101–2 samples 100–2 values 59, 232 variation, sources of 5–8 Volkswagen, diesel emissions scandal 211 Wall Street Journal 219 Wallace, Alfred Russel 259n33 Walmart 77 Watts, Duncan 68, 69, 147–50 weakest-link principle 79–82 Wedgwood, Josiah 50–1 Wellington, Duke of 51 Wesfarmers 76–7 West Germany, motorcycle thefts 142–4 Western, Bruce 54 Wilson, Harold 99 World Bank Independent Evaluation Group 79 World Health Organization 162 world picture 63–4 Wright, Sewall 253n11


pages: 313 words: 91,098

The Knowledge Illusion by Steven Sloman

Affordable Care Act / Obamacare, Air France Flight 447, attribution theory, bitcoin, Black Swan, Cass Sunstein, combinatorial explosion, computer age, Computing Machinery and Intelligence, CRISPR, crowdsourcing, Dmitri Mendeleev, driverless car, Dunning–Kruger effect, Elon Musk, Ethereum, Flynn Effect, Great Leap Forward, Gregor Mendel, Hernando de Soto, Higgs boson, hindsight bias, hive mind, indoor plumbing, Isaac Newton, John von Neumann, libertarian paternalism, Mahatma Gandhi, Mark Zuckerberg, meta-analysis, Nick Bostrom, obamacare, Peoples Temple, prediction markets, randomized controlled trial, Ray Kurzweil, Richard Feynman, Richard Thaler, Rodney Brooks, Rosa Parks, seminal paper, single-payer health, speech recognition, stem cell, Stephen Hawking, Steve Jobs, technological singularity, The Coming Technological Singularity, The Wisdom of Crowds, Vernor Vinge, web application, Whole Earth Review, Y Combinator

If we tap out a tune, we’re sometimes shocked that others don’t recognize it. It seems so obvious; after all, we can hear it in our heads. If we know the answer to a general knowledge question (who starred in The Sound of Music?), we have a tendency to expect others to know the answer too. The curse of knowledge sometimes comes in the form of a hindsight bias. If our team just won a big game or our candidate just won an election, then we feel like we knew it all along and others should have expected that outcome too. The curse of knowledge is that we tend to think what is in our heads is in the heads of others. In the knowledge illusion, we tend to think what is in others’ heads is in our heads.

Weber (1989). “The Curse of Knowledge in Economic Settings: An Experimental Analysis.” Journal of Political Economy 97(5): 1232–1254. shocked that others don’t recognize it: C. Heath and D. Heath (2007). Made to Stick: Why Some Ideas Survive and Others Die. New York: Random House, 2007. hindsight bias: B. Fischhoff and R. Beyth (1975). “‘I Knew It Would Happen’: Remembered Probabilities of Once-Future Things.” Organizational Behavior and Human Performance 13(1): 1–16. few people today read Alice in Wonderland: A fact bemoaned by Anthony Lane in “Go Ask Alice,” The New Yorker, June 8 and 15, 2015.


pages: 299 words: 92,782

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

Amazon Mechanical Turk, Atul Gawande, Benoit Mandelbrot, Black Swan, Boeing 747, Checklist Manifesto, Clayton Christensen, cognitive bias, commoditize, Daniel Kahneman / Amos Tversky, David Brooks, deliberate practice, disruptive innovation, Emanuel Derman, fundamental attribution error, Gary Kildall, Gini coefficient, hindsight bias, hiring and firing, income inequality, Innovator's Dilemma, John Bogle, Long Term Capital Management, loss aversion, Menlo Park, mental accounting, moral hazard, Network effects, power law, prisoner's dilemma, random walk, Richard Thaler, risk-adjusted returns, shareholder value, Simon Singh, six sigma, Steven Pinker, transaction costs, winner-take-all economy, zero-sum game, Zipf's Law

Make use of counterfactuals One case for learning history is to gain some insight about how the future might unfold. The difficulty with this is that we see only the path that the world followed, while events could have taken many different turns. Once we know what happened, hindsight bias naturally envelops us. This is a bias that allows us to forget how unpredictable the world looked beforehand. So we come up with reasons to explain the outcomes that appear as if they were inevitable. One way to avoid hindsight bias is to engage in counterfactual thinking, a careful consideration of what could have happened but didn't. If we accept that x played a role in causing y, then we have to consider how events would have unfolded had x not happened.


Alpha Trader by Brent Donnelly

Abraham Wald, algorithmic trading, Asian financial crisis, Atul Gawande, autonomous vehicles, backtesting, barriers to entry, beat the dealer, behavioural economics, bitcoin, Boeing 747, buy low sell high, Checklist Manifesto, commodity trading advisor, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, currency risk, deep learning, diversification, Edward Thorp, Elliott wave, Elon Musk, endowment effect, eurozone crisis, fail fast, financial engineering, fixed income, Flash crash, full employment, global macro, global pandemic, Gordon Gekko, hedonic treadmill, helicopter parent, high net worth, hindsight bias, implied volatility, impulse control, Inbox Zero, index fund, inflation targeting, information asymmetry, invisible hand, iterative process, junk bonds, Kaizen: continuous improvement, law of one price, loss aversion, low interest rates, margin call, market bubble, market microstructure, Market Wizards by Jack D. Schwager, McMansion, Monty Hall problem, Network effects, nowcasting, PalmPilot, paper trading, pattern recognition, Peter Thiel, prediction markets, price anchoring, price discovery process, price stability, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Robert Shiller, secular stagnation, Sharpe ratio, short selling, side project, Stanford marshmallow experiment, Stanford prison experiment, survivorship bias, tail risk, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, time dilation, too big to fail, transaction costs, value at risk, very high income, yield curve, you are the product, zero-sum game

That is what traders with no plan do. It is the trading equivalent of Homer Simpson chasing the squirrel. Be aware of this leak and catch yourself chasing price. Step away from your computer and give yourself a firm talking to. Chasing price without a plan is a recipe for persistent losses and frustrating performance. Hindsight bias Humans tend to think they know more than they know and they tend to think that they knew things were going to happen even when they truly had no idea. My first boss was famous inside the bank for walking by, seeing what had moved up or down the most and then saying (for example): “I wanted to be long USDJPY!

After something has moved, you might remember some weak gut feeling you had or some random thought that flitted through your mind about buying that thing. It’s meaningless! It is also annoying. Don’t be one of those people that says woulda, coulda or shoulda on a trading floor. The only thing that matters are the decisions and actions you take in real-time. Hindsight bias is also a constant feature of financial media. When you see a headline like “Stocks rally on higher oil price” remember that the journalist is writing with the benefit of hindsight. If you went back to the start of the day and asked people what stocks would do if oil prices went up, you might get 50% saying stocks would go down and 50% saying stocks would go up.

Roth), 140 health, trader, 459, 481 See also exercise; meditation; mindfulness; yoga hedge fund traders/PMs, 78, 96, 106, 111, 116, 117, 118, 152, 189, 190, 195, 211 annual stop loss, 356, 357 appropriate capital, 149—150 evaluation and compensation, 354 free capital, 356 information asymmetry and, 156 Kelly Criterion and, 375 liquidity and, 283 as poker players, 192 success or failure of, 360 success rate, 40 hedge funds, 40, 78, 114, 150, 273 currency futures and, 421 macro analysis and, 347 pod-based, 162 hedonic treadmill, 128 Hefti, Andreas, 68 Heinke, Steve, 68 herding, 229-240, 483 belief in good stories and, 231 bubbles and, 236—240 conformity and, 230 fear of missing out and, 231—232 how to detect and combat, 234—236 incentives and, 232—233 reasons for market participant, 230—233 versus creative thinking, 232 See also Asch Conformity Experiment Hermann Grid Illusion, 180 Heuer, Richards J., 85—86, 88, 410 hindsight bias, 242 history, trader personal, 462—465 blue collar upbringing, 462—463 money versus happiness curve, 465 perception of money and, 463—464 thinking about, 462—465 Hollins, Peter, 106, 491 hot hand, 245—247 Hour Between Dog and Wolf, The (J. Coates), 132, 491 house money effect, 108, 114, 169—170, 173, 379 Housel, Morgan, 243 How to Lie with Statistics (D.


pages: 416 words: 108,370

Hit Makers: The Science of Popularity in an Age of Distraction by Derek Thompson

Airbnb, Albert Einstein, Alexey Pajitnov wrote Tetris, always be closing, augmented reality, Clayton Christensen, data science, Donald Trump, Downton Abbey, Ford Model T, full employment, game design, Golden age of television, Gordon Gekko, hindsight bias, hype cycle, indoor plumbing, industrial cluster, information trail, invention of the printing press, invention of the telegraph, Jeff Bezos, John Snow's cholera map, Kevin Roose, Kodak vs Instagram, linear programming, lock screen, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Mary Meeker, Menlo Park, Metcalfe’s law, Minecraft, Nate Silver, Network effects, Nicholas Carr, out of africa, planned obsolescence, power law, prosperity theology / prosperity gospel / gospel of success, randomized controlled trial, recommendation engine, Robert Gordon, Ronald Reagan, Savings and loan crisis, Silicon Valley, Skype, Snapchat, social contagion, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, subscription business, TED Talk, telemarketer, the medium is the message, The Rise and Fall of American Growth, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, Vilfredo Pareto, Vincenzo Peruggia: Mona Lisa, women in the workforce

., 163–67, 174, 183–84, 306 Hamlet (Shakespeare), 98, 99 Harmsworth, Alfred, 256, 256n Hayward, Amanda, 187, 200 HBO, 244, 246–48, 252 headlines, Reddit, 66–67 Heidegger, Martin, 29 Hekkert, Paul, 49–50 heroes and hero’s journey, 103–4, 108–11 The Hero with a Thousand Faces (Campbell), 108, 110 The Hidden Fortress (1958), 114–15, 118 high-concept pitches, 61–62 Hill, George Washington, 53 hindsight bias, 171n hip-hop/rap music, 81–82 history, 129 History of Impressionism (Rewald), 24n HitPredictor, 35, 37 Hollywood. See movies and Hollywood homophily, 216–17, 219, 221 Hoover, J. Edgar, 157 horror movies, 112 Hoskins, Valerie, 198, 199 How to Win Friends and Influence People (Carnegie), 93–94 The Hum: Call and Response in African American Preaching (Crawford), 91 Hume, David, 28 humor, 144–49 Huron, David, 82–85, 83n, 84n ideas, spread of, 7–9 Iger, Bob, 299 iHeart Media, 35 imitation, 178–79 impressionists, 19–27, 312n22.

Perhaps scientists like Watts consider many allegedly prescient writers to have similarly dubious talents: They can predict the rain only after their shirts are wet. 43. What Watts is describing here could fall under various categories, discussed in Daniel Kahneman’s Thinking, Fast and Slow, in particular hindsight bias: “I knew it all along” or “If it happened, it was the most likely outcome.” 44. In an advertisement in Weekly Variety, on September 21, 1955, Decca thanked disc jockeys for getting “Rock Around the Clock” to “over two million in record sales.” At the bottom of the page, however, the label also implored DJs to play its new release, “Razzle Dazzle,” which it complained “has been smothered by ‘Rock Around the Clock.’”


pages: 411 words: 108,119

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

"World Economic Forum" Davos, Alan Greenspan, An Inconvenient Truth, Andrei Shleifer, availability heuristic, bank run, behavioural economics, Black Swan, business cycle, Cass Sunstein, classic study, 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, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Isaac Newton, iterative process, Kenneth Arrow, Loma Prieta earthquake, London Interbank Offered Rate, market bubble, market clearing, money market fund, moral hazard, mortgage debt, Oklahoma City bombing, Pareto efficiency, Paul Samuelson, placebo effect, precautionary principle, price discrimination, price stability, RAND corporation, Richard Thaler, Robert Shiller, Robert Solow, Ronald Reagan, Savings and loan crisis, social discount rate, source of truth, statistical model, stochastic process, subprime mortgage crisis, The Wealth of Nations by Adam Smith, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, ultimatum game, University of East Anglia, urban planning, Vilfredo Pareto

This could be due to an attempt to reduce cognitive dissonance, for self-justification, or simply to misremembering. It may also be a variant of hindsight bias, in which knowing the outcome alters an individual’s assessment of how likely it was to have occurred. For example, in a 1975 study by psychologist Baruch Fischhoff, who is also a contributor to this book, subjects were given passages to read about the Gurkha raids on the British in the early 1800s. Some were told how the conflict ended, and others were not. When asked what the probability of occurrence of each outcome was, those who knew the outcome gave it a much higher probability. With such “secondary hindsight bias,” individuals are unaware that the occurrence of an event influences what they believe ex post that they would have estimated ex ante.


pages: 336 words: 113,519

The Undoing Project: A Friendship That Changed Our Minds by Michael Lewis

Albert Einstein, availability heuristic, behavioural economics, Cass Sunstein, choice architecture, complexity theory, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, endowment effect, feminist movement, framing effect, hindsight bias, John von Neumann, Kenneth Arrow, Linda problem, loss aversion, medical residency, Menlo Park, Murray Gell-Mann, Nate Silver, New Journalism, Paul Samuelson, peak-end rule, Richard Thaler, Saturday Night Live, Skinner box, Stanford marshmallow experiment, statistical model, systematic bias, the new new thing, Thomas Bayes, Walter Mischel, Yom Kippur War

The next season, before the trade deadline, Morey got up before his staff and listed on a whiteboard all the biases he feared might distort their judgment: the endowment effect, confirmation bias, and others. There was what people called “present bias”—the tendency, when making a decision, to undervalue the future in relation to the present. There was “hindsight bias”—which he thought of as the tendency for people to look at some outcome and assume it was predictable all along. The model was an antidote to these vagaries of human judgment, but, by 2012, the model seemed to be approaching a limit to the informational edge it would give the Rockets in valuing players.

They greatly overestimated the odds that they had assigned to what had actually happened. That is, once they knew the outcome, they thought it had been far more predictable than they had found it to be before, when they had tried to predict it. A few years after Amos described the work to his Buffalo audience, Fischhoff named the phenomenon “hindsight bias.”† In his talk to the historians, Amos described their occupational hazard: the tendency to take whatever facts they had observed (neglecting the many facts that they did not or could not observe) and make them fit neatly into a confident-sounding story: All too often, we find ourselves unable to predict what will happen; yet after the fact we explain what did happen with a great deal of confidence.


pages: 410 words: 114,005

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

Abraham Wald, Airbus A320, Alfred Russel Wallace, Arthur Eddington, Atul Gawande, Black Swan, Boeing 747, British Empire, call centre, Captain Sullenberger Hudson, Checklist Manifesto, cognitive bias, cognitive dissonance, conceptual framework, corporate governance, creative destruction, credit crunch, crew resource management, deliberate practice, double helix, epigenetics, fail fast, fear of failure, flying shuttle, fundamental attribution error, Great Leap Forward, Gregor Mendel, Henri Poincaré, hindsight bias, Isaac Newton, iterative process, James Dyson, James Hargreaves, James Watt: steam engine, Johannes Kepler, Joseph Schumpeter, Kickstarter, Lean Startup, luminiferous ether, mandatory minimum, meta-analysis, minimum viable product, publication bias, quantitative easing, randomized controlled trial, selection bias, seminal paper, Shai Danziger, Silicon Valley, six sigma, spinning jenny, Steve Jobs, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, Toyota Production System, US Airways Flight 1549, Wall-E, Yom Kippur War

See also medicine adoption rate and, 56–57 cognitive dissonance and, 87–90, 103–7 complexity and, 137 culture of, 16, 49–50, 53, 54–55, 57, 58–59, 105–6 error and, 16–19, 31–32, 49–52, 87–90 nurse unit administration, and blame, 226–27, 230–31 scientific approach to learning from failure and, 49–52 Hearst Foundation, 55–56 Henderson, Mark, 157 heroism, 39, 40 Hidden, Anthony, 232n hierarchy, 25, 28–29, 30, 49–50, 103–7 Hilbert, David, 202 Hilfiker, David, 17, 106 hindsight bias, 232n HIV, 147–49 Holtz-Eakin, Douglas, 97 House of Commons Public Administration Select Committee, 55 Houston, Drew, 138, 142–43, 145 Hume, David, 44 Hungry Ghosts, Mao’s Secret Famine (Becker), 110 iatrogenic injury, 17 illusion of design, 129 incentive, 53–54 Incognito: The Secret Lives of Brains (Eagleman), 200 Industrial Revolution, 132–33 inferiority complex, 43–44 inflation, 95–96 initiation experiment, 75–76, 86–87 Innocence Project, 69, 77–78, 81, 84, 85 reforms and, 115, 116–17 innovation.

It seems almost certain that, with pressure high and time limited, the pilot did not notice that some of the shades were, in fact, up. *As estimated by how often the nursing units were intercepting errors before they became consequential, and other key variables governing self-correction and learning. *“Hindsight bias,” another well-studied psychological tendency, also plays a role here. Once we know the outcome of an event—a patient has died, a plane has crashed, an IT system has malfunctioned—it is notoriously difficult to free one’s mind from that concrete eventuality. It is tough to put oneself in the shoes of the operator, who is often acting in high-pressure circumstances, trying to reconcile different demands, and unaware of how a particular decision might pan out.


pages: 407 words: 114,478

The Four Pillars of Investing: Lessons for Building a Winning Portfolio by William J. Bernstein

Alan Greenspan, asset allocation, behavioural economics, book value, Bretton Woods, British Empire, business cycle, butter production in bangladesh, buy and hold, buy low sell high, carried interest, corporate governance, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dava Sobel, diversification, diversified portfolio, Edmond Halley, equity premium, estate planning, Eugene Fama: efficient market hypothesis, financial engineering, financial independence, financial innovation, fixed income, George Santayana, German hyperinflation, Glass-Steagall Act, high net worth, hindsight bias, Hyman Minsky, index fund, invention of the telegraph, Isaac Newton, John Bogle, John Harrison: Longitude, junk bonds, Long Term Capital Management, loss aversion, low interest rates, market bubble, mental accounting, money market fund, mortgage debt, new economy, pattern recognition, Paul Samuelson, Performance of Mutual Funds in the Period, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk/return, Robert Shiller, Savings and loan crisis, South Sea Bubble, stock buybacks, stocks for the long run, stocks for the long term, survivorship bias, Teledyne, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, the rule of 72, transaction costs, Vanguard fund, yield curve, zero-sum game

Thus, it is highly misleading to rely on the investment performance of history’s most successful nations and empires as indicative of your own future returns. At first glance, it might appear that the above list of winners and losers contradicts the relationship between risk and return. This is an excellent example of “hindsight bias”; in 1913 it was by no means obvious that the U.S., Canada, Sweden, and Switzerland would have the highest returns, and that Germany, Japan, Argentina, and India, the lowest. Going back further, in 1650 France and Spain were the mightiest economic and military powers in Europe, and England an impoverished upstart torn by civil war.

., 57 Emergencies, saving for, 240 Emerging markets, 31, 37, 38, 72, 94, 95, 124, 125, 156, 188, 255, 257, 268, 272, 274, 276, 283 England (See Britain) Enron, 161 Entertainment, investment as, 171–172, 183-184 Equities (See Stocks) ETFs (exchange-traded funds), 216, 217, 254, 255 eToys, 57 Euphoria, and bubbles, 136 European interest rates, historical perspective, 8–13 Exchange-traded funds (ETFs), 216, 217, 254, 255 Expected returns growth stocks, 173–175 long-term, 55, 70, 71 myopic risk aversion, 172-173, 184-185 overconfidence, 167–169, 181–183 vs. real returns, 68–69 Expense ratio (ER) in mutual fund costs, 94–95 Expenses (See Fees and expenses) Extraordinary Popular Delusions and the Madness of Crowds (Mackay), 151 Fair value of stock market, 47-53 Fama, Eugene, 37, 88-89, 120-121, 186, 257 Federal Reserve Bank, 146, 152, 159, 176 Fee-only financial advisors, 294 Fees and expenses, 401(k), 211–213 Fees and expenses, mutual funds differences in funds, 209–211 Forbes Honor Roll, 222 front load, 207 index funds, 245, 250, 254 load, 79, 203–205, 216 management fees, 206 no-load, 205–206, 215 Fidelity Capital Fund, 83 Fidelity Dividend Growth Fund, 207 Fidelity Magellan, 91–93, 97 Fidelity mutual funds, 205, 207–209, 210 Fidelity Select Technology Fund, 207–209 Fidelity Spartan funds, 216 Fiduciary responsibility of broker (lack of), 192 Financial Analysts Journal, 244 Financial calculator, 230, 237 Financial goals, 229, 239–240 First Quadrant, 88 Fisher, Irving, 43–48, 56, 229 Folios, 102 A Fool and His Money (Rothchild), 224 Forbes, Malcolm, 87–88 Forbes Honor Roll, 222 Forecasting Cowles and, 76-79, 87 investment newsletters and, 77, 78, 86, 87 Foreign stocks and returns asset allocation in portfolios, 116–120, 255–257, 256 growth vs. value stocks, 36–37 stability, societal, 29–32 tax efficiency of, 264 Fortune, 213, 221 Fouse, William, 95-97 French, Kenneth, 33–34, 35–37, 120 Fuller, Russell, 174 Galbraith, John Kenneth, 148 Gambling, 171–172 Garzarelli, Elaine, 169 GDP (gross domestic product) and technological diffusion, 132-133 GE (General Electric), 33, 244 General Electric (GE), 33, 244 General Motors, 65 Gibson, Roger, 225 Gillette, 151 Glass-Stegall Act, 193 Glassman, James, 53, 264 Global Investing (Brinson and Ibbotson), 225 Global stocks (See Foreign stocks) GNMA fund, Vanguard, 216 Go-Go years (1960-1970), 83, 148–151 Goetzmann, William, 30 Gold, (precious metals stocks), 123–124, 155 Gold mining, 69 Gold standard, 16–18, 145–146 Goldman Sachs Corporation, 147–148, 169 Goldman Sachs Trading Corporation, 148 Gordon Equation, 53–62, 192 Government securities, 259–260 Graham, Benjamin Depression-era mortgage bonds, 185 Hollerith Corporation, later IBM, 78 on income production, 44 on investor’s chief problem, 165 pre-1929 stock bubble, 57 Security Analysis, 157–158 Graham, John, 87 Grant, James, 224–225 Great company/great stock fallacy, 173–175, 185 Great Depression fear of short-term losses, 172–173 Fisher’s gaffe, 43 Graham on, 157–158 impact of, 5–6 manias, history of, 145–148 societal stability and DR, 64–65 Great Man theory, 95–96 Greenspan, Alan, 246 Gross domestic product (GDP) and technological diffusion, 132–134 Growth stocks (“good” companies) asset allocation, 247, 248–255, 251–253 earnings expectations of, 173–175 Graham on, 158 returns of, 34-38 “Gunning the Fund,” 207-209 Halley, Edmund, 138 Hammurabi, 7 Hard currency (gold), 16-20 Harrison, John, 142–143 Harvey, Campbell, 87 Hassett, Kevin, 53, 264 Hedge funds, 178–179 Herd mentality and overconfidence, 166-176, 181, 182 Hewlett-Packard, 158 High-quality corporate bonds, 260 High Yield bonds, 69–70 “Hindsight bias,” 8 History of investing and returns (Pillar 2), 127–162 about, xi, 296 ancient, 6–9 bonds, 13–22 European, middle ages to present, 9–13 on risk, 11-13, 22-29, 38-39 stocks investing in U.S., 4–6 outside U.S., 29–32 prior to twentieth century, 20 twentieth century, 20–22 summary on risk and return, 38-39 Treasury bills in twentieth century, 20–22, 23 Hollerith Inc., later IBM, 78 House, saving for, 240 Hubbard, Carl M., 231 IAI, 211 Ibbotson, Roger, 225 IBM (International Business Machines), 78, 83, 150, 151 Immediate past as predictive, behavioral economics, 170–171 “Impact cost,” mutual funds, 84, 85, 92, 94, 208, 211 Impatience, societal, and discounted dividend model (DDM), 46 “In-Between Ida,” asset allocation example, 269-271 Income production and discounted dividend model [discounted dividend model (DDM)], 43–73 Index fund advantages of, 95-105 bonds, 257–263, 258–259 defined, 97 exchange-traded funds (ETFs), 216, 217, 254, 255 performance and efficient market hypothesis, 95–98, 102–104 vs. performance of top 10% funds, 81 sectors in portfolio building, 122–124, 250, 251–253 tax efficient, 99 INEPT (investment entertainment pricing theory), 172 Inflation bond performance, 16-20 and gold standard, 16–18 government response to, 19–20 inflation risk, 13 and stocks, 20, 24 Inflation-adjusted returns earnings growth, 60 stocks, bonds and bills, 19, 20–22 young savers, 237–239 Inflation risk, 13 Information speed of transmission, 131 and stock prices, 89–90 Initial public offering (IPO), 134, 172 In Search of Excellence (Peters), 64 Instant gratification and discounted dividend model (DDM), 46 The Intelligent Asset Allocator (Bernstein, W.), vii, 110 Interest-rate risk, 13 Interest rates in ancient world, 6-8 annuity pricing, 10-12, 13 and bond yields, 10, 16-20 bonds and currency, changes from gold to paper (1900-2000), 17–19 as cultural stability barometer, 8–9 European, 8-13 Fisher’s discount rate (DR), 46–47 historic perspective on bills and bonds, 9-15 risk, 13 International Business Machines (IBM), 78, 83, 150, 151 Internet Capital Group, 152 Internet/dot-com as bubble, 151–152, 153, new investment paradigm, 56–58 Invesco mutual funds, 205 Investment vs. purchase, 45 vs. saving, 134, vs. speculation, 44, 157 Investment advisors (See Advisors, investment) Investment and Speculation (Chamberlain), 157 Investment Company Act of 1940, 161, 203, 213, 217 Investment entertainment pricing theory (INEPT), 172 Investment newsletters, 77, 78, 87 Ip, Greg, 167 IPO (initial public offering), 134, 172 iShares, 251-253, 257 Japan dominance in late 1970s, 66–67, 181–182 technical progress and diffusion, 132 Jensen, Michael, 78–80, 214 Johnson, Edward Crosby, II, 83, 91 Johnson, Edward Crosby, III (“Ned”), 194, 207, 208, 210 Jorion, Phillippe, 30 Journal of Finance, 80, 225 Journalist coverage, 219–225 JTS (junk-treasury spread), 70 Junk bonds, 69–70, 150n1, 260, 263, 283, 288-289 Junk-treasury spread (JTS), 70 Kahneman, Daniel, 166 Karr, Alphonse, 162 Kassen, Michael, 207, 219 Kelly, Walt, 179 Kemble, Fanny, 143 Kemper Annuities and Life, 205, 210 Kemper Gateway Incentive Variable Annuity, 205 Kennedy, Joseph P., Sr., 147 Keynes, John Maynard, 41-42, 18, 221 Kindleberger, Charles, 136–137 Kmart, 34–35 Ladies Home Journal, 65 Large company stocks asset allocation, 244–255, and Fidelity Magellan Fund, 92 rebalancing, 289–290 returns, 32-34, 38, 72 Law, John, 137–138 Leinweber, David, 88 Leveraged buyouts, 150n1 Leveraged trusts, 147–148 Lipper, Arthur, 83 Litton, 149–150 Load funds fees, mutual funds, 79, 196, 203–205, 216 Long Term Capital Management, 129, 179 Long-term credit (See Bonds) Long-term returns asset classes, 16-39 bonds, in asset allocation, 113–114 expected, in asset classes, 70, 71 Gordon Equation, 53–62, 192 stocks, 20-39 LTV Inc., 83 Lumpers vs. splitters in asset mix, 247, 248–255, 251–253 Lynch, Peter, 91–93 Mackay, Charles, 151 Malkiel, Burton, 55, 224 Management fees, mutual funds, 206, 209-211 Manhattan Fund, 83–84 Manias, 129–152 about, 129–130 bubbles (See Bubbles) identification, 153 Internet, 151–152, 153 Minsky’s theory of, 136, 140 new technology, impact of, 130–134 1960-1970 (Go-Go years), 148–151 railroads, 143-145, 158, 159–160 Roaring Twenties, 145–148, 153 space race, 149–150 Margin purchases, 147–148 Market bottom, 153–162 about, 153–154 as best time to invest, 66 buying at, 283 “Death of Equities,” 154–157 Graham on Great Depression, 157–161 panic, 161–162 Market capitalization, 33, 123, 245 Market impact, mutual fund costs, 82, 94–95, 208 Market strategists, 87, 169, 176, 186, 219 Market timing, 87–88, 108, 220 Market value formula, 52 McDonald’s, 150, 158 Mean reversion, 170 Mean variance optimizer (MVO), 108 Media, 219–225 Mellon Bank, 96 Mental accounting, 177, 186 Merrill, Charles Edward, 193–194, 213 Merrill Lynch, 88, 193–194, 200 Microsoft, 59, 166, 185 Miller, Merton, 7 “Millionaire,” origin of term, 138 The Millionaire Next Door (Stanley and Danko), 239 Minding Mr.


pages: 587 words: 119,432

The Collapse: The Accidental Opening of the Berlin Wall by Mary Elise Sarotte

anti-communist, Berlin Wall, conceptual framework, Deng Xiaoping, facts on the ground, Fall of the Berlin Wall, hindsight bias, Mikhail Gorbachev, open borders, Prenzlauer Berg, Ronald Reagan, Ronald Reagan: Tear down this wall, urban decay, éminence grise

For analysis of one or both of the superpowers over the course of the entire Cold War, see, to name just a few, Brown, Rise and Fall; Gaddis, Cold War; John Lamberton Harper, The Cold War (Oxford: Oxford University Press, 2011); Tony Judt, Postwar: A History of Europe Since 1945 (New York: Penguin, 2005); Leffler, For the Soul of Mankind; Westad, Global Cold War; and Zubok, Failed Empire. 18. Bloch, Apologie, 160. On Bloch’s life and tragic death, see Carole Fink, Marc Bloch: A Life in History (Cambridge: Cambridge University Press, 1989), 86, 318–322. 19. On hindsight bias and on Tocqueville, see Timothy Garton Ash, “1989!” New York Review of Books, Nov. 5, 2009; Timothy Garton Ash, The Magic Lantern: The Revolution of ’89 Witnessed in Warsaw, updated ed. (New York: Vintage Books, 1999), 142; Maier, “Civil Resistance,” 275–276; and Maier, Dissolution, epigraph.

In the opening lines of Part I, Chapter 1, of Ancien Régime, Tocqueville argues that events that are in fact inevitable appear to be unlikely before they happen. Bloch’s work, however, suggests the opposite: events that are not preordained seem afterward to have been so. This appears to be the definition of hindsight bias as identified by Bloch and described in the introduction. 38. Egon Krenz’s email to the author, Oct. 24, 2013. The original two German quotations, given in my translation in the text above, are as follows: (1) “Als am 9. November 1989 Berliner Bürger zu den Grenzübergängen eilten, weil ein Politbüromitglied sie falsch informiert hatte, waren wir einer bürgerkriegsähnlichen Auseinandersetzung näher als das viele heute wahrhaben wollen”; (2) “Am Abend des 9.


pages: 482 words: 121,672

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing (Eleventh Edition) by Burton G. Malkiel

accounting loophole / creative accounting, Alan Greenspan, Albert Einstein, asset allocation, asset-backed security, beat the dealer, Bernie Madoff, bitcoin, book value, butter production in bangladesh, buttonwood tree, buy and hold, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, Detroit bankruptcy, diversification, diversified portfolio, dogs of the Dow, Edward Thorp, Elliott wave, equity risk premium, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial engineering, financial innovation, financial repression, fixed income, framing effect, George Santayana, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Japanese asset price bubble, John Bogle, junk bonds, Long Term Capital Management, loss aversion, low interest rates, margin call, market bubble, Mary Meeker, money market fund, mortgage tax deduction, new economy, Own Your Own Home, PalmPilot, passive investing, Paul Samuelson, pets.com, Ponzi scheme, price stability, profit maximization, publish or perish, purchasing power parity, RAND corporation, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Salesforce, short selling, Silicon Valley, South Sea Bubble, stock buybacks, stocks for the long run, sugar pill, survivorship bias, Teledyne, the rule of 72, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond, zero-sum game

Two behavioral economists, Terrance Odean and Brad Barber, examined the individual accounts at a large discount broker over a substantial period of time. They found that the more individual investors traded, the worse they did. And male investors traded much more than women, with correspondingly poorer results. This illusion of financial skill may well stem from another psychological finding, called hindsight bias. Such errors are sustained by having a selective memory of success. You remember your successful investments. And in hindsight, it is easy to convince yourself that you “knew Google was going to quintuple right after its initial public offering.” People are prone to attribute any good outcome to their own abilities.

., 114 HAO, 398 Harvard Business School, 109 Harvard University, 161, 163, 384 Hazard Powder Company, 121 hedge funds, 25, 197, 248 in Internet bubble, 250 oil market destabilization by, 250 Hedgehogging (Biggs), 170 hemline indicator, 146–48 Henry IV, Part I (Shakespeare), 219 herd mentality, 231, 239–43, 253–54 Hewlett-Packard, 69 “Higgledy Piggledy Growth,” 161 high-frequency trading (HFT), 184–85 high-technology boom, see Internet; new issues hindsight bias, 234–35 home insurance, 295 Homestore.com, 89–90 Hong, Harrison, 242, 253 Hoover, Herbert, 53 “hot hand” phenomenon, 145 hot streaks, 235–36 hot tips, 258 housing bubble, 97–104, 105–6, 251 random-walk theory and, 105–6 Hulbert, Mark, 148 Hydro-Space Technology, 58 Ibbotson, Roger, 349–50, 351 Ibbotson Associates, 194, 265 IBM (International Business Machines), 57, 68, 69, 74, 161 “If—” (Kipling), 79 income, 311–12, 355, 362–63 income taxes, 158, 298, 300, 311–13, 317–18, 320, 339, 365n, 378 Incredible January Effect, The, 150 index funds, 254, 367, 379–92, 415 advantages of, 181, 261, 357, 382–85, 397, 411 broad definition of, 395–97 float weighted, 397 general equity, data on, 415 international, 398, 417–18 low-cost, 326, 360 specific portfolio of, 389 tax-managed, 390–92 Individual Retirement Accounts (IRAs), 300–303, 304, 365n, 370, 375, 378 see also Roth IRAs Industry Standard, 91 inertia, 247 inflation, 297, 342, 346 core rate of, 337–38 demand-pull, 337 effect of, on bond returns, 319, 333 effects of, on purchasing power, 28–29, 125n, 306–7, 315 as factor in systematic risk, 224 home prices adjusted for, 101–2 interest rates and, 337–38, 346 predictability of, 340 profits during, 339 real estate investment and, 314 information superhighway, 183 initial public offerings (IPOs), 70, 75, 257 in Internet boom, 84–87 see also new issues in-kind redemption, 391 In Search of Excellence (Peters and Waterman), 233 insiders, 182, 185 Institutional Investor, 218, 221 institutional investors, 56–79, 170, 171, 218 odd-lot theory and, 149 in stock market crash (1987), 152–53 Institutional Investors Study Report, 218 insurance, 294–97 Intel, 384 Intelligent Investor, The (Graham), 119 interest rates, 125–26, 306–7, 321–22, 346 compound, 119–20, 292, 365 on money-market mutual funds, 298 on mortgages, 314 see also rate of return Interferon, 71–72 Internal Revenue Service, 316 International Business Machines, see IBM International Flavors and Fragrances, 69 International Monetary Fund, 387 Internet, 36, 172, 254, 296, 375, 393, 406 bubble in, 79–97, 104–5, 126, 172, 177, 239, 241–42, 249, 252–53, 254, 257, 331, 344 cash-burn rate in, 80 CDs and, 299 media and, 91–93 new-issue craze in, 84–87 security analysts’ promotion of, 88–89 stock valuation in, 81–83, 89–90 valuation metrics of, 89–90 Internet banks, 299 intrinsic value of stocks, 31–33, 35 calculating of, impossibility of, 126–27, 129 determinants of, 161 as maximum price to pay, 130–32, 394 investment: as contemporary way of life, 28–30 defined, 28 fun of, 30 lovemaking vs., 409 speculation vs., 28 investment advisers, 407–8 investment banking and securities analysts, 164, 170–73 Investment Guide, Life-Cycle, 366–67 investment objectives, 306–13 investment pools, 49–50 investment theory, see castle-in-the-air theory; firm-foundation theory; new investment technology investors, professional, performance of, 396, 398 Investors.com, 172 IPOs, see initial public offerings IRAs, see Individual Retirement Accounts Irrational Exuberance (Shiller), 35, 80, 242, 285 “irrational exuberance” speech, 285 iShares, 281 Jackson, Don D., 114 Jagannathan, Ravi, 222 January Effect, 150 JDS Uniphase, 81–82, 83 Jedi, 94 “jingle mail,” 102 Johnson and Johnson, 124 JP Morgan, 97 junk-bond market, 320–21 Justice Department, U.S., 60 Kabuto-cho (Japan’s Wall St.), 76 Kahneman, Daniel, 35, 230, 233, 237–38, 243–44 Kaplan, Philip J., 85 Kennedy, John F., 159, 336 Keynes, John M., 33–34, 54, 57, 66, 169, 189, 250 Kindleberger, Charles, 242 King’s College, 33 Kipling, Rudyard, 79 Kirby, Robert, 227 Kleiner Perkins, 87 Kriezer, Lloyd, 168 Krizelman, Todd, 86 Kubik, Jeffrey, 242, 253 La Crosse and Minnesota Steam Packet Company, 120–21 La Rochefoucauld, 109, 409 Law, John, 42 Lay, Ken, 94–95 Le Bon, Gustave, 37 Lehman Brothers, 152 Leinweber, David, 148 Letterman, David, 252 leverage, 39, 104 Liberty University, 74 life-cycle funds, 370 Life-Cycle Investment Guide, 366–67 life cycles of corporations and industries, 120–21 life insurance, 26, 295–96 Lintner, John, 209 Litton Industries, 64 Lo, Andrew, 139, 286 loading fees, 317, 400 loans: in housing bubble, 98–104 looser standards for, 100–101, 104 Lompoc Federal Prison, 74 Long Term Capital Management, 249 loss aversion, 231, 243–45, 256 “lost decade,” 206–7, 331, 411 Lucent, 83, 90, 166 Lynch, Peter, 132, 176, 184 Macaulay, Thomas B., 329 Mackay, Charles, 38–39 MacKinlay, A.


pages: 542 words: 132,010

The Science of Fear: How the Culture of Fear Manipulates Your Brain by Daniel Gardner

Atul Gawande, availability heuristic, behavioural economics, Black Swan, Cass Sunstein, citizen journalism, cognitive bias, cognitive dissonance, Columbine, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, Doomsday Clock, feminist movement, haute couture, hindsight bias, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), lateral thinking, Linda problem, mandatory minimum, medical residency, Mikhail Gorbachev, millennium bug, moral panic, mutually assured destruction, nuclear winter, Oklahoma City bombing, placebo effect, precautionary principle, public intellectual, Ralph Nader, RAND corporation, Ronald Reagan, social intelligence, Stephen Hawking, Steven Levy, Steven Pinker, the long tail, the scientific method, Timothy McVeigh, Tunguska event, uranium enrichment, Y2K, young professional

Literally so: We know, looking back, that this was not the end of the world—when we imagine nineteenth-century Paris, we tend to think of the Moulin Rouge, not plague—and that knowledge removes the uncertainty that was the defining feature of the experience for Heine and the others who lived through it. Simply put, history is an optical illusion: The past always appears more certain than it was, and that makes the future feel more uncertain—and therefore frightening—than ever. The roots of this illusion lie in what psychologists call “hindsight bias.” In a classic series of studies in the early 1970s, Baruch Fischhoff gave Israeli university students detailed descriptions of events leading up to an 1814 war between Great Britain and the Gurkas of Nepal. The description also included military factors that weighed on the outcome of the conflict, such as the small number of Gurka soldiers and the rough terrain the British weren’t used to.

Do you think it occurred? And do you recall how likely you thought it was to occur? “Results showed that subjects remembered having given higher probabilities than they actually had to events believed to have occurred,” Fischhoff wrote, “and lower probabilities to events that hadn’t occurred.” The effect of hindsight bias is to drain the uncertainty out of history. Not only do we know what happened in the past, we feel that what happened was likely to happen. What’s more, we think it was predictable. In fact, we knew it all along. So here we are, standing in the present, peering into the frighteningly uncertain future and imagining all the awful things that could possibly happen.


pages: 178 words: 52,637

Quality Investing: Owning the Best Companies for the Long Term by Torkell T. Eide, Lawrence A. Cunningham, Patrick Hargreaves

air freight, Albert Einstein, asset light, backtesting, barriers to entry, buy and hold, carbon tax, cashless society, cloud computing, commoditize, Credit Default Swap, discounted cash flows, discovery of penicillin, endowment effect, global pandemic, haute couture, hindsight bias, legacy carrier, low cost airline, mass affluent, Network effects, oil shale / tar sands, pattern recognition, price elasticity of demand, proprietary trading, shareholder value, smart grid, sovereign wealth fund, supply-chain management, vertical integration

Forensic self-analysis is not always comfortable, as facing one’s errors seldom is, but it helps reduce mistakes. A final, and vital, practice is attempting to recognize and combat biases. As summarized by Daniel Kahneman in Thinking, Fast and Slow, cognitive errors such as confirmation bias, hindsight bias, and outcome bias are rife in the investing world.53 Many assessments of quality are steeped in them. Tackling such biases is a tough and ceaseless task, among the greatest challenges for any investor. A primary technique for mitigating the influence of biases is to focus as far as possible on the process rather than the outcome: adhering to fundamental investment principles in the face of inevitable market gyrations.


pages: 589 words: 147,053

The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson

8-hour work day, artificial general intelligence, augmented reality, Berlin Wall, bitcoin, blockchain, brain emulation, business cycle, business process, Clayton Christensen, cloud computing, correlation does not imply causation, creative destruction, deep learning, demographic transition, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, Future Shock, Herman Kahn, hindsight bias, information asymmetry, job automation, job satisfaction, John Markoff, Just-in-time delivery, lone genius, Machinery of Freedom by David Friedman, market design, megaproject, meta-analysis, Nash equilibrium, new economy, Nick Bostrom, pneumatic tube, power law, prediction markets, quantum cryptography, rent control, rent-seeking, reversible computing, risk tolerance, Silicon Valley, smart contracts, social distancing, statistical model, stem cell, Thomas Malthus, trade route, Turing test, Tyler Cowen, Vernor Vinge, William MacAskill

For example, we might like a story about a future where people work very few hours per week, as a way to indirectly comment on current changes in work hours. But as most events described here are not projections of current trends, this book is less useful for this purpose. Considering what our best theories suggest about future societies can also help us to test these theories. Today, we social scientists too easily succumb to hindsight bias and assume that the patterns we see around us are clearly implied by our theories of how society works. Thinking about future societies where such patterns are much less visible can force us to consider more carefully what our theories about how the world works actually imply. Such a thought experiment can help us to calibrate the confidence we should place in these theories, and to spot theoretical holes that we might work to fill.

Spurs could also be used to test for biases. Today, psychologists show common biases by randomly splitting experimental subjects into subgroups that are given different prompts. For example, a question might be worded two different ways, resulting in different answers on average. Or an “I knew it all along” hindsight bias might be shown via telling different subgroups different outcomes, and asking subjects what chance they would have assigned before to seeing their chosen outcome. Because of random fluctuations that influence individual decisions, however, such experiments today usually require large groups of experimental subjects to see subtle effects.


pages: 598 words: 134,339

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

23andMe, Airbnb, airport security, AltaVista, Anne Wojcicki, AOL-Time Warner, augmented reality, behavioural economics, Benjamin Mako Hill, Black Swan, Boris Johnson, Brewster Kahle, Brian Krebs, call centre, Cass Sunstein, Chelsea Manning, citizen journalism, Citizen Lab, cloud computing, congestion charging, data science, digital rights, disintermediation, drone strike, Eben Moglen, Edward Snowden, end-to-end encryption, Evgeny Morozov, experimental subject, failed state, fault tolerance, Ferguson, Missouri, Filter Bubble, Firefox, friendly fire, Google Chrome, Google Glasses, heat death of the universe, hindsight bias, informal economy, information security, Internet Archive, Internet of things, Jacob Appelbaum, James Bridle, Jaron Lanier, John Gilmore, John Markoff, Julian Assange, Kevin Kelly, Laura Poitras, license plate recognition, lifelogging, linked data, Lyft, Mark Zuckerberg, moral panic, Nash equilibrium, Nate Silver, national security letter, Network effects, Occupy movement, operational security, Panopticon Jeremy Bentham, payday loans, pre–internet, price discrimination, profit motive, race to the bottom, RAND corporation, real-name policy, recommendation engine, RFID, Ross Ulbricht, satellite internet, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, smart cities, smart grid, Snapchat, social graph, software as a service, South China Sea, sparse data, stealth mode startup, Steven Levy, Stuxnet, TaskRabbit, technological determinism, telemarketer, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, undersea cable, unit 8200, urban planning, Wayback Machine, WikiLeaks, workplace surveillance , Yochai Benkler, yottabyte, zero day

connect-the-dots metaphor: Spencer Ackerman (13 Dec 2013), “NSA review to leave spying programs largely unchanged, reports say,” Guardian, http://www.theguardian.com/world/2013/dec/13/nsa-review-to-leave-spying-programs-largely-unchanged-reports-say. 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.

., 160 fiduciary responsibility, data collection and, 204–5 50 Cent Party, 114 FileVault, 215 filter bubble, 114–15 FinFisher, 81 First Unitarian Church of Los Angeles, 91 FISA (Foreign Intelligence Surveillance Act; 1978), 273 FISA Amendments Act (2008), 171, 273, 275–76 Section 702 of, 65–66, 173, 174–75, 261 FISA Court, 122, 171 NSA misrepresentations to, 172, 337 secret warrants of, 174, 175–76, 177 transparency needed in, 177 fishing expeditions, 92, 93 Fitbit, 16, 112 Five Eyes, 76 Flame, 72 FlashBlock, 49 flash cookies, 49 Ford Motor Company, GPS data collected by, 29 Foreign Intelligence Surveillance Act (FISA; 1978), 273 see also FISA Amendments Act Forrester Research, 122 Fortinet, 82 Fox-IT, 72 France, government surveillance in, 79 France Télécom, 79 free association, government surveillance and, 2, 39, 96 freedom, see liberty Freeh, Louis, 314 free services: overvaluing of, 50 surveillance exchanged for, 4, 49–51, 58–59, 60–61, 226, 235 free speech: as constitutional right, 189, 344 government surveillance and, 6, 94–95, 96, 97–99 Internet and, 189 frequent flyer miles, 219 Froomkin, Michael, 198 FTC, see Federal Trade Commission, US fusion centers, 69, 104 gag orders, 100, 122 Gamma Group, 81 Gandy, Oscar, 111 Gates, Bill, 128 gay rights, 97 GCHQ, see Government Communications Headquarters Geer, Dan, 205 genetic data, 36 geofencing, 39–40 geopolitical conflicts, and need for surveillance, 219–20 Georgia, Republic of, cyberattacks on, 75 Germany: Internet control and, 188 NSA surveillance of, 76, 77, 122–23, 151, 160–61, 183, 184 surveillance of citizens by, 350 US relations with, 151, 234 Ghafoor, Asim, 103 GhostNet, 72 Gill, Faisal, 103 Gmail, 31, 38, 50, 58, 219 context-sensitive advertising in, 129–30, 142–43 encryption of, 215, 216 government surveillance of, 62, 83, 148 GoldenShores Technologies, 46–47 Goldsmith, Jack, 165, 228 Google, 15, 27, 44, 48, 54, 221, 235, 272 customer loyalty to, 58 data mining by, 38 data storage capacity of, 18 government demands for data from, 208 impermissible search ad policy of, 55 increased encryption by, 208 as information middleman, 57 linked data sets of, 50 NSA hacking of, 85, 208 PageRank algorithm of, 196 paid search results on, 113–14 search data collected by, 22–23, 31, 123, 202 transparency reports of, 207 see also Gmail Google Analytics, 31, 48, 233 Google Calendar, 58 Google Docs, 58 Google Glass, 16, 27, 41 Google Plus, 50 real name policy of, 49 surveillance by, 48 Google stalking, 230 Gore, Al, 53 government: checks and balances in, 100, 175 surveillance by, see mass surveillance, government Government Accountability Office, 30 Government Communications Headquarters (GCHQ): cyberattacks by, 149 encryption programs and, 85 location data used by, 3 mass surveillance by, 69, 79, 175, 182, 234 government databases, hacking of, 73, 117, 313 GPS: automobile companies’ use of, 29–30 FBI use of, 26, 95 police use of, 26 in smart phones, 3, 14 Grayson, Alan, 172 Great Firewall (Golden Shield), 94, 95, 150–51, 187, 237 Greece, wiretapping of government cell phones in, 148 greenhouse gas emissions, 17 Greenwald, Glenn, 20 Grindr, 259 Guardian, Snowden documents published by, 20, 67, 149 habeas corpus, 229 hackers, hacking, 42–43, 71–74, 216, 313 of government databases, 73, 117, 313 by NSA, 85 privately-made technology for, 73, 81 see also cyberwarfare Hacking Team, 73, 81, 149–50 HAPPYFOOT, 3 Harris Corporation, 68 Harris Poll, 96 Hayden, Michael, 23, 147, 162 health: effect of constant surveillance on, 127 mass surveillance and, 16, 41–42 healthcare data, privacy of, 193 HelloSpy, 3, 245 Hewlett-Packard, 112 Hill, Raquel, 44 hindsight bias, 322 Hobbes, Thomas, 210 Home Depot, 110, 116 homosexuality, 97 Hoover, J. Edgar, attempted intimidation of King by, 98, 102–3 hop searches, 37–38 HTTPS Everywhere, 215, 216 Huawei, 74, 86, 182 Human Rights Watch, 96, 178 IBM, 104, 122 iCloud, 58 ICREACH, 67 identification, anonymity and, 131–33 identity theft, 116–19 iMacs, 58 imperfection, systemic, resilience and, 163–64 IMSI-catchers, 68, 165–66 independence, oversight and, 162–63, 169, 177–78 India, 76 individuals, data rights of, 192–93, 200–203, 211, 232 data storage by, 18–19 see also mass surveillance, individual defenses against inferences, from data mining, 34–35, 258, 259 and correlation of data sets, 40–42 error rates in, 34, 54, 136–37, 269 information fiduciaries, 204–5 information middlemen: Internet’s empowering of, 57–58 monopolistic nature of, 57 Information Technology and Innovation Foundation, 121–22 InfoUSA, 53 Initiate Systems, 41 Instagram, 58 intelligence community, US, 67 budget of, 64–65, 80 fear and, 228 international partnerships of, 76–77 private contractors in, 80, 228 revolving door in, 80 see also specific agencies Internal Revenue Service, US (IRS), 137, 159 International Association of Privacy Professionals, 124 International Principles on the Application of Human Rights to Communications Surveillance, 167, 168–69 International Telecommunications Union, 106, 187 Internet: anonymity on, 43–44, 131–33 benefits of, 8 commons as lacking on, 188–89 cyberattacks on, see cyberwarfare deliberate insecurity of, 7, 146–50, 182 early history of, 119 fee-based vs. ad-based business model of, 50, 56, 206 freedom of, 107, 188 government censorship and control of, 94–95, 106–7, 187–88, 237 identification and, 131–33 information middlemen and, see information middlemen international nature of, 6–7, 187–88, 209, 220–21 laws and, 220–21 as media source, 15 physical wiring of, 64 privacy and, 203–4, 230–31 traditional corporate middlemen eliminated by, 56–57 trust and, 181–82 Internet companies, transparency reports of, 207–8 Internet Movie Database, 43 Internet of Things, 15–17 Internet searches, NSA collection of data on, 22 Internet surveillance, 47–51 advertising and, see advertising, personalized cable companies and, 48–49 cookies and, 47–48, 49 global, 69–71 NSA and, 62, 64–65, 78, 122, 149–50, 188, 207 ubiquity of, 32 see also mass surveillance, corporate iPads, 58 iPhones, 31, 42, 58 Iran: government surveillance in, 71–72 Stuxnet cyberattack on, 75, 132, 146, 150 Iraq War, 65 IRC, 119 Israel: mass surveillance by, 182 Stuxnet cyberattack by, 75, 132, 146, 150 US intelligence data shared with, 77 Israeli assassination team, identification of, 43 ISS (Intelligence Support Systems) World, 81 iTunes store, 57 Jawbone, 16 Jay-Z, 48 Joint Terrorism Task Forces, 69 journalists, government surveillance and, 96 JPMorgan Chase, 116 judiciary, surveillance and, 168, 170, 179–80 justice, as core American value, 230 Justice Department, US, 184, 186 Kerry, John, 101 keyboard loggers, 25 key escrow, 120–21 keyword searches, 28, 261 Kindle, 28, 59 King, Martin Luther, Jr., 237 Hoover’s attempted intimidation of, 98, 102–3 Kinsey, Alfred, database of, 44 Klein, Mark, 250, 288 Kunstler, James, 206 Kurds, 76 Lanier, Jaron, 201 Lavabit, 83–84, 209 law enforcement, state and local: abuse of power by, 135, 160 IMSI-catchers used by, 68 location data and, 2, 243 militarization of, 184 predictive algorithms used by, 98–99, 100, 137, 159 racism in, 184 secrecy of, 100, 160 transparency and, 170 lawyers, government surveillance and, 96 legal system: as based on human judgment, 98–99 government surveillance and, 168, 169 secrecy and, 100 Lenddo, 111, 113 Level 3 Communications, 85 Levison, Ladar, 84 liberty: commons and, 189 as core American value, 230 social norms and, 227 liberty, government surveillance and, 6, 91–107, 184 abuses of power in, 101–5, 160, 234–35 anonymity and, 133 censorship and, 94–95, 106–7, 187–88 and changing definition of “wrong,” 92–93, 97–98 discrimination and, 103–4 fear and, 4, 7, 95–97, 135, 156–57, 171, 182–83, 222, 226, 227–30 Internet freedom and, 106–7, 188 political discourse and, 97–99 secrecy and, 99–101 security and, 135, 157–59, 361–62 ubiquitous surveillance and, 92, 97 Library of Congress, 199 Libya, 81 license plate scanners, 26–27, 40 storage of data from, 36 lifelogging, 16 Lincoln, Abraham, 229 Little Brother (Doctorow), 217 location data, 1–3, 28, 39, 62, 243, 339 advertising and, 39–40 de-anonymizing with, 44 embedded in digital photos, 14–15, 42–43 selling of, 2 Locke, John, 210 Los Angeles Police Department, 160 LOVEINT, 102, 177 Lower Merion School District, 104 LulzSec hacker movement, 42 MAC addresses, 29 MacKinnon, Rachel, 210, 212 Madrid Privacy Declaration (2009), 211–12 Magna Carta, information age version of, 210–12 manipulation, surveillance-based, 113–16 Manning, Chelsea, 101 marijuana use, 97 MARINA, 36 Mask, The, 72 Massachusetts Group Insurance Commission, 263 mass surveillance: algorithmic-based, 129–31, 159, 196 as automated process, 5, 129–31 dangers of, 4–5, 6 economic harms from, 6–7, 121–22, 151 false positives in, 137, 138, 140, 323–24 fatalism and, 224–25 lack of consent in, 5, 20, 51 metadata in, 20–23 minimum necessary, 158–59, 176, 211 moratorium urged on new technologies of, 211 noticing, 223 security harmed by, 7, 146–50 social norms and, 226–38 society’s bargains with, 4, 8–9, 47, 49–51, 58–59, 60–61, 158, 226, 235–38 speaking out about, 223–24 targeted surveillance vs., 5, 26, 139–40, 174, 179–80, 184, 186 transparency and, 159–61, 169, 170–71, 176 ubiquity of, 5, 26–28, 32, 40, 53, 92, 97, 224, 233 urgency of fight against, 233–35 see also data collection; data mining mass surveillance, corporate, 46–61, 86–87 advertising and, see advertising, personalized business competitiveness and, 119–24 cost of, to US businesses, 123–24 customers as products in, 53, 58 customer service and, 47 data brokers and, see data brokers discrimination and, 109–13 error rates in, 54 feudal nature of, 58–59, 61, 210–12 free services and convenience exchanged for, 4, 49–51, 58–59, 60–61, 226, 235–36 growth of, 23–24 harms from, 108–18 lobbying and, 233 manipulation and, 113–16 manipulation through, 6 market research and, 47 privacy breaches and, 116–18, 142, 192, 193–95 secrecy and, 194 see also mass surveillance, public-private partnership in mass surveillance, corporate, solutions for, 7, 190–212 accountability and liability in, 192, 193–95, 196–97, 202 data quality assurance and, 181, 192, 194, 202 government regulation in, 192, 196–99, 210 individual participation and, 192 and limits on data collection, 191, 192, 199–200, 202, 206 and limits on data use, 191, 192, 194, 195–97, 206 lobbying and, 209, 222–23 and resistance to government surveillance, 207–10 and respect for data context, 202 rights of individuals and, 192, 200–203, 211 salience and, 203–4 security safeguards and, 192, 193–95, 202, 211 specification of purpose and, 192 transparency and, 192, 194, 196, 202, 204, 207–8 mass surveillance, government, 5–6, 62–77 chilling effects of, 95–97 in China, 70, 86, 140, 209 cloud computing and, 122 corporate nondisclosure agreements and, 100 corporate resistance to, 207–10 cost of, 91 cost of, to US businesses, 121–23 democracy and, 6, 95, 97–99 discrimination and, 4, 6, 93 encryption technology and, 119–23 fear-based justification for, 4, 7, 95–97, 135, 156–57, 171, 182–83, 222, 226, 227–30, 246 fishing expeditions in, 92, 93 in France, 79 fusion centers in, 69, 104 gag orders in, 100, 122 geopolitical conflicts and, 219–20 global, 69–71 growth of, 24–25 hacking in, 71–74 as harmful to US global interests, 151 as ineffective counterterrorism tool, 137–40, 228 international partnerships in, 76–77, 169 lack of trust in US companies resulting from, 122–23, 181–83 liberty and, see liberty, government surveillance and location data used in intimidation and control by, 2 mission creep and, 104–5 oversight and accountability in, 161–63, 169 in Russia, 70, 187, 188, 237 mass surveillance, government ( continued) secrecy of, 99–101, 121, 122 subversion of commercial systems in, 82–87 in UK, 69, 79 US hypocrisy about, 106 see also mass surveillance, public-private partnership in; specific agencies mass surveillance, government, solutions for, 7, 168–89 adequacy and, 168 and breakup of NSA, 186–87 due process and, 168, 184 illegitimate access and, 169, 177 integrity of systems and, 169, 181–82 international cooperation and, 169, 180, 184 judicial authority and, 168, 179–80 legality and, 168, 169 legitimacy and, 168 limitation of military role in, 185–86 lobbying and, 222 “Necessary and Proportionate” principles of, 167, 168–69 necessity and, 168 oversight and, 169, 172–78 proportionality and, 168 separation of espionage from surveillance in, 183–84 targeted surveillance and, 179–80, 184, 186 transparency and, 169, 170–71, 176 trust and, 181–83 user notification and, 168 whistleblowers and, 169, 178–79 mass surveillance, individual defenses against, 7, 213–25 avoidance in, 214 blocking technologies in, 214–17 breaking surveillance technologies, 218–19 distortion in, 217–18 fatalism as enemy of, 224–25 political action and, 213, 222–24, 237–38 mass surveillance, public-private partnership in, 6, 25, 78–87, 207 government subversion of commercial systems in, 82–87 nondisclosure agreements and, 100 privately-made technology in, 81–82, 100 sale of government data in, 79–80 and value neutrality of technology, 82 material witness laws, 92 McCarthyism, 92–93, 229, 234 McConnell, Mike, 80 McNealy, Scott, 4 media: fear and, 229 pre-Internet, 15 medical devices, Internet-enabled, 16 medical research, collection of data and, 8 Medtronic, 200 memory, fallibility of, 128, 320 Merkel, Angela, 151, 160–61, 183, 184 metadata, 216 from cell phones, see cell phone metadata data vs., 17, 23, 35, 251 from Internet searches, 22–23 in mass surveillance, 20–23, 67 from tweets, 23 Michigan, 2, 39 Microsoft, 49, 59–60, 84, 148, 221, 272, 359 customer loyalty to, 58 government demands for data from, 208, 359 increased encryption by, 208 transparency reports of, 207 Mijangos, Luis, 117 military, US: ban on domestic security role of, 185–86 Chinese cyberattacks against, 73 “Don’t Ask Don’t Tell” policy of, 197 drone strikes by, 94 see also Army, US; Cyber Command, US; Defense Department, US MINARET, 175 Minority Report (film), 98 mission creep, 104–5, 163 Mitnick, Kevin, 116 Moglen, Eben, 95, 318 money transfer laws, 35–36 Monsegur, Hector, 42 Mori, Masahiro, 55 MS Office, 60 Multiprogram Research Facility, 144 Muslim Americans, government surveillance of, 103–4 MYSTIC, 36 Napolitano, Janet, 163 Narent, 182 narrative fallacy, 136 Nash equilibrium, 237 Natanz nuclear facility, Iran, 75 National Academies, 344 National Counterterrorism Center, 68 National Health Service, UK, 79 National Institute of Standards and Technology (NIST), proposed takeover of cryptography and computer security programs by, 186–87 National Reconnaissance Office (NRO), 67 National Security Agency, US (NSA): backdoors inserted into software and hardware by, 147–48 Bermuda phone conversations recorded by, 23 “Black Budget” of, 65 cell phone metadata collected by, 20–21, 36, 37, 62, 138, 339 “collect” as defined by, 129, 320 “collect it all” mentality of, 64–65, 138 COMSEC (communications security) mission of, 164–65, 346 congressional oversight of, 172–76 “connect-the-dots” metaphor of, 136, 139 cost to US businesses of surveillance by, 121–22, 151 counterterrorism mission of, 63, 65–66, 184, 222 counterterrorism successes claimed by, 325 cryptanalysis by, 144 cyberattacks by, 149–50 drug smugglers surveilled by, 105 economic espionage by, 73 encryption programs and, 85–86, 120–21 encryption standards deliberately undermined by, 148–49 expanding role of, 24, 165 FISA Amendments Act and, 174–75, 273 foreign eavesdropping (SIGINT) by, 62–63, 76, 77, 122–23, 164–65, 186, 220 Germany surveilled by, 76, 77, 122–23, 151, 160–61, 183, 184 Gmail user data collected by, 62 historical data stored by, 36 history of, 62–63 inadequate internal auditing of, 303 innocent people surveilled by, 66–67 insecure Internet deliberately fostered by, 146–50, 182 international partnerships of, 76–77 Internet surveillance by, 22, 62, 64–65, 78, 86–87, 122–23, 149–50, 188, 207 keyword searches by, 38, 261 legal authority for, 65–66 location data used by, 3, 339 Multiprogram Research Facility of, 144 Muslim Americans surveilled by, 103 parallel construction and, 105, 305 Presidential Policy Directives of, 99–100 PRISM program of, 78, 84–85, 121, 208 proposed breakup of, 186–87 QUANTUM program of, 149–50, 329–30 relationship mapping by, 37–38 remote activation of cell phones by, 30 secrecy of, 99–100, 121, 122 SIGINT Enabling Project of, 147–49 Snowden leaks and, see Snowden, Edward SOMALGET program of, 65 Syria’s Internet infrastructure penetrated by, 74, 150 Tailored Access Operations (TAO) group of, 72, 85, 144, 149, 187 UN communications surveilled by, 102, 183 National Security Agency, US (NSA) ( continued) Unitarian Church lawsuit against, 91 US citizens surveilled by, 64, 66, 175 US global standing undermined by, 151 Utah Data Center of, 18, 36 vulnerabilities stockpiled by, 146–47 National Security Letters (NSLs), 67, 84, 100, 207–8 Naval Criminal Investigative Service, 69 Naval Research Laboratory, US, 158 Nest, 15–16 Netcom, 116 Netflix, 43 Netsweeper, 82 New Digital Age, The (Schmidt and Cohen), 4 newsgroups, 119 New York City Police Department, 103–4 New York State, license plate scanning data stored by, 36 New York Times, Chinese cyberattack on, 73, 132, 142 New Zealand, in international intelligence partnerships, 76 Nigeria, 81 9/11 Commission Report, 139, 176 Nineteen Eighty-Four (Orwell), 59, 225 NinthDecimal, 39–40 NIST, see National Institute of Standards and Technology Nixon, Richard, 230 NOBUS (nobody but us) vulnerabilities, 147, 181 Nokia, 81 nondisclosure agreements, 100 North, Oliver, 127–28 Norway, 2011 massacre in, 229–30 NSA, see National Security Agency, US Oak Ridge, Tenn., 144 Obama, Barack, 33, 175 NSA review group appointed by, 176–77, 181 Obama administration: Internet freedom and, 107 NSA and, 122 whistleblowers prosecuted by, 100–101, 179 obfuscation, 217–18 Occupy movement, 104 Ochoa, Higinio (w0rmer), 42–43 OECD Privacy Framework, 191–92, 197 Office of Foreign Assets Control, 36 Office of Personnel Management, US, 73 Off the Record, 83, 215 Olympics (2014), 70, 77 Onionshare, 216 openness, see transparency opt-in vs. opt-out consent, 198 Orange, 79 Orbitz, 111 Organized Crime Drug Enforcement Task Forces, 69 Orwell, George, 59, 225 oversight, of corporate surveillance, see mass surveillance, corporate, solutions for, government regulation in oversight, of government surveillance, 161–63, 169, 172–78 Oyster cards, 40, 262 packet injection, 149–50 PageRank algorithm, 196 Palmer Raids, 234 Panetta, Leon, 133 panopticon, 32, 97, 227 panoptic sort, 111 parallel construction, 105, 305 Pariser, Eli, 114–15 Parker, Theodore, 365 PATRIOT Act, see USA PATRIOT Act pen registers, 27 Peoria, Ill., 101 personalized advertising, see advertising, personalized personally identifying information (PII), 45 Petraeus, David, 42 Petrobras, 73 Pew Research Center, 96 PGP encryption, 215, 216 photographs, digital, data embedded in, 14–15, 42–43 Pirate Party, Iceland, 333 Placecast, 39 police, see law enforcement, state and local police states, as risk-averse, 229 political action, 7, 213, 222–24, 237–38 political campaigns: data mining and, 33, 54 personalized marketing in, 54, 115–16, 233 political discourse, government surveillance and, 97–99 politics, politicians: and fear of blame, 222, 228 technology undermined by, 213 Posse Comitatus Act (1878), 186 Postal Service, US, Isolation Control and Tracking program of, 29 Presidential Policy Directives, 99–100 prices, discrimination in, 109–10 PRISM, 78, 84–85, 121, 208 privacy, 125–33 algorithmic surveillance and, 129–31, 204 as basic human need, 7, 126–27 breaches of, 116–18, 192, 193–95 as fundamental right, 67, 92, 126, 201, 232, 238, 318, 333, 363–64 of healthcare data, 193 Internet and, 203–4, 230–31 loss of, 4, 7, 50–51, 96, 126 and loss of ephemerality, 127–29 “nothing to hide” fallacy and, 125 and proposed Consumer Privacy Bill of Rights, 201, 202 security and, 155–57 social norms and, 227, 230–33 third-party doctrine and, 67–68, 180 as trumped by fear, 228 undervaluing of, 7–8, 50, 156, 194, 203–4 Privacy and Civil Liberties Oversight Board, 176, 177 privacy enhancing technologies (PETs), 215–16, 217 Privacy Impact Notices, 198, 211 probable cause, 184 Protect America Act (2007), 275 public-private partnership, see mass surveillance, public-private partnership in Qualcomm, 122 QUANTUM packet injection program, 149–50, 329–30 radar, high-frequency, 30 “ratters,” 117 Reagan, Ronald, 230 redlining, 109 Red October, 72 Regulation of Investigatory Powers Act (UK; 2000), 175 relationships, mapping of, 37–38 remote access Trojans (RATs), 117 resilience, systemic imperfections and, 163–64 retailers, data collected by, 14, 24, 51–52 revenge porn, 231 RFID chips, 29, 211 Richelieu, Cardinal, 92 rights, of consumers, see consumer rights risk, police states as averse to, 229 risk management, 141–42 Robbins, Blake, 104 robotics, 54–55 Rogers, Michael, 75 Roosevelt, Franklin D., 229, 230 Rousseff, Dilma, 151 RSA Security, 73, 84 rule of law, 210, 212 Russia: cyberwarfare and, 180 mandatory registration of bloggers in, 95 mass surveillance by, 70, 187, 188, 237 salience, 203–4 San Diego Police Department, 160 Sarkozy, Nicolas, 96 Saudi Arabia, 76, 187, 209 Saudi Aramco, 75 Schmidt, Eric, 4, 22, 57, 86, 125 schools, surveillance abuse in, 104 Schrems, Max, 19, 200 search engines, business model of, 113–14, 206 secrecy: corporate surveillance and, 194 of government surveillance, 99–101, 121, 122, 170–71 legitimate, transparency vs., 332–33 security, 135–51 airplane, 93, 158 attack vs. defense in, 140–43 balance between civil liberties and, 135 complexity as enemy of, 141 cost of, 142 data mining as unsuitable tool for, 136–40 and deliberate insecurity of Internet, 146–50 encryption and, see encryption fear and, 4, 7, 95–97, 135, 156–57, 171, 182–83, 222, 226, 227–30 hindsight and, 136 mass surveillance as harmful to, 7, 146–50 and misguided focus on spectacular events, 135 narrative fallacy in, 136 privacy and, 155–57 random vs. targeted attacks and, 142–43 risk management and, 141–42 social norms and, 227 surveillance and, 157–59 vulnerabilities and, 145–46 security cameras, see surveillance technology self-censorship, 95 Senate, US, Intelligence Committee of, 102, 172, 339 Sensenbrenner, Jim, 174 Sense Networks, 2, 40 September 11, 2001, terrorist attacks, 63, 65, 136, 156, 169, 184, 207, 227, 229 SHAMROCK, 175 Shirky, Clay, 228, 231 Shutterfly, 269 Siemens, 81 SIGINT (signals intelligence), see National Security Agency, US, foreign eavesdropping by SIGINT Enabling Project, 147–49 Silk Road, 105 Skype, 84, 148 SmartFilter, 82 smartphones: app-based surveillance on, 48 cameras on, 41 as computers, 14 GPS tracking in, 3, 14, 216–17 MAC addresses and Bluetooth IDs in, 29 Smith, Michael Lee, 67–68 Snowden, Edward, 177, 178, 217 e-mail of, 94 Espionage Act and, 101 EU Parliament testimony of, 76 NSA and GCHQ documents released by, 6, 20, 40–41, 62, 65, 66, 67, 72, 74, 78, 96, 99–100, 121, 129, 144, 149, 150, 160–61, 172, 175, 182, 207, 223, 234, 238 Sochi Olympics, 70, 77 Socialists, Socialism, 92–93 social networking: apps for, 51 customer scores and, 111 customer tracking and, 123 data collected in, 200–201 government surveillance of, 295–96 see also specific companies social norms: fear and, 227–30 liberty and, 227 mass surveillance and, 226–38 privacy and, 227, 230–33 security and, 227 software: security of, 141, 146 subscription vs. purchase models for, 60 Solove, Daniel, 93 SOMALGET, 65 Sophos, 82 Sotomayor, Sonia, 95, 342 South Korea, cyberattack on, 75 spy gadgets, 25–26 SSL encryption, 85–86 SSL (TLS) protocol, 215 Standard Chartered Bank, 35–36 Staples, 110 Stasi, 23 Steinhafel, Gregg, 142 strategic oversight, 162, 172–77 StingRay surveillance system, 100, 165 Stross, Charles, 128 Stuxnet, 75, 132, 146 collateral damage from, 150 Supreme Court, US, 26, 180, 361–62 third-party doctrine and, 68 surveillance: automatic, 31–32 benefits of, 8, 190 as business model, 50, 56, 113–14, 206 cell phones as devices for, 1–3, 14, 28, 39, 46–47, 62, 100, 216–17, 219, 339 constant, negative health effects of, 127 cost of, 23–26 espionage vs., 170, 183–84 government abuses of, 101–5 government-on-government, 63, 73, 74, 75, 76, 158 hidden, 28–30 legitimate needs for, 219–20 as loaded term, 4 mass, see mass surveillance oversight and accountability in, 161–63, 169, 172–78 overt, 28, 30 perception of, 7–8 personal computers as devices for, 3–4, 5 politics and, 213 pre-Internet, 64, 71 principles of, 155–66 targeted, see targeted surveillance transparency and, 159–61, 169, 170–71, 176 surveillance technology: cameras, 14, 17, 31–32 cost of, 25–26 shrinking size of, 29 Suspicious Activity Reports (SAR), 138 Sweeney, Latanya, 44, 263–64 SWIFT banking system, 73 Swire, Peter, 160 Syria, 81 NSA penetration of Internet infrastructure in, 74, 150 System for Operative Investigative Measures (SORM; Russia), 70 tactical oversight, 162, 177–79 Tailored Access Operations group (TAO), 72, 85, 144, 149, 187 Taleb, Nassim, 136 Target, 33, 34, 55 security breach of, 142, 193 targeted advertising, see advertising, personalized targeted surveillance: mass surveillance vs., 5, 26, 139–40, 174, 179–80, 184, 186 PATRIOT Act and, 174 tax fraud, data mining and, 137 technology: benefits of, 8, 190–91 political undermining of, 213 privacy enhancing (PETs), 215–16, 217 see also surveillance technology telephone companies: FBI demands for databases of, 27, 67 historical data stored by, 37, 67 NSA surveillance and, 122 transparency reports of, 207–8 see also cell phone metadata; specific companies Teletrack, 53 TEMPORA, 79 Terrorism Identities Datamart Environment, 68, 136 terrorists, terrorism: civil liberties vs., 135 government databases of, 68–69 as justification for mass surveillance, 4, 7, 170–71, 226, 246 mass surveillance as ineffective tool for detection of, 137–40, 228 and NSA’s expanded mission, 63, 65–66 terrorists, terrorism ( continued) overly broad definition of, 92 relative risk of, 332 Uighur, 219, 287 uniqueness of, 138 see also counterterrorism; security; September 11, 2001, terrorist attacks thermostats, smart, 15 third-party doctrine, 67–68, 180 TLS (SSL) protocol, 215 TOM-Skype, 70 Tor browser, 158, 216, 217 Torch Concepts, 79 trade secrets, algorithms as, 196 transparency: algorithmic surveillance and, 196 corporate surveillance and, 192, 194, 196, 202, 207–8 legitimate secrecy vs., 332–33 surveillance and, 159–61, 169, 170–71, 176 Transparent Society, The (Brin), 231 Transportation Security Administration, US (TSA), screening by, 136, 137, 159, 231, 321 Treasury, US, 36 Truman, Harry, 62, 230 trust, government surveillance and, 181–83 truth in lending laws, 196 Tsarnaev, Tamerlan, 69, 77, 139 Turkey, 76 Turla, 72 Twitter, 42, 58, 199, 208–9 metadata collected by, 23 Uber, 57 Uighur terrorists, 219, 287 Ukraine, 2, 39 Ulbricht, Ross (Dread Pirate Roberts), 105 “uncanny valley” phenomenon, 54–55 Underwear Bomber, 136, 139 UN High Commissioner on Human Rights, 96 Unit 8200, 77 United Kingdom: anti-discrimination laws in, 93 data retention law in, 222 GCHQ of, see Government Communications Headquarters in international intelligence partnerships, 76 Internet censorship in, 95 license plate scanners in, 27 mission creep in, 105 Regulation of Investigatory Powers Act (2000) of, 175 United Nations: digital privacy resolution of, 232, 363–64 NSA surveillance of, 102, 183 United States: data protection laws as absent from, 200 economic espionage by, 73 Germany’s relations with, 151, 234 intelligence budget of, 64–65, 80 NSA surveillance as undermining global stature of, 151 Stuxnet cyberattack by, 75, 132, 146, 150 Universal Declaration of Human Rights, 232 USA PATRIOT Act (2001), 105, 221, 227 Section 215 of, 65, 173–74, 208 Section 505 of, 67 US Cellular, 177 Usenet, 189 VASTech, 81 Verint, 2–3, 182 Verizon, 49, 67, 122 transparency reports of, 207–8 Veterans for Peace, 104 Vigilant Solutions, 26, 40 Vodafone, 79 voiceprints, 30 vulnerabilities, 145–46 fixing of, 180–81 NSA stockpiling of, 146–47 w0rmer (Higinio Ochoa), 42–43 Wall Street Journal, 110 Wanamaker, John, 53 “warrant canaries,” 208, 354 warrant process, 92, 165, 169, 177, 180, 183, 184, 342 Constitution and, 92, 179, 184 FBI and, 26, 67–68 NSA evasion of, 175, 177, 179 third-party doctrine and, 67–68, 180 Watson, Sara M., 55 Watts, Peter, 126–27 Waze, 27–28, 199 weapons of mass destruction, overly broad definition of, 92, 295 weblining, 109 WebMD, 29 whistleblowers: as essential to democracy, 178 legal protections for, 162, 169, 178–79, 342 prosecution of, 100–101, 178, 179, 222 Wickr, 124 Wi-Fi networks, location data and, 3 Wi-Fi passwords, 31 Wilson, Woodrow, 229 Windows 8, 59–60 Wired, 119 workplace surveillance, 112 World War I, 229 World War II, 229 World Wide Web, 119, 210 writers, government surveillance and, 96 “wrong,” changing definition of, 92–93 Wyden, Ron, 172, 339 XKEYSCORE, 36 Yahoo, 84, 207 Chinese surveillance and, 209 government demands for data from, 208 increased encryption by, 208 NSA hacking of, 85 Yosemite (OS), 59–60 YouTube, 50 Zappa, Frank, 98 zero-day vulnerabilities, 145–46 NSA stockpiling of, 146–47, 180–81 ZTE, 81 Zuckerberg, Mark, 107, 125, 126 Praise for DATA AND GOLIATH “Data and Goliath is sorely needed.


pages: 511 words: 151,359

The Asian Financial Crisis 1995–98: Birth of the Age of Debt by Russell Napier

Alan Greenspan, Asian financial crisis, asset allocation, bank run, banking crisis, banks create money, Berlin Wall, book value, Bretton Woods, business cycle, Buy land – they’re not making it any more, capital controls, central bank independence, colonial rule, corporate governance, COVID-19, creative destruction, credit crunch, crony capitalism, currency manipulation / currency intervention, currency peg, currency risk, debt deflation, Deng Xiaoping, desegregation, discounted cash flows, diversification, Donald Trump, equity risk premium, financial engineering, financial innovation, floating exchange rates, Fractional reserve banking, full employment, Glass-Steagall Act, hindsight bias, Hyman Minsky, If something cannot go on forever, it will stop - Herbert Stein's Law, if you build it, they will come, impact investing, inflation targeting, interest rate swap, invisible hand, Japanese asset price bubble, Jeff Bezos, junk bonds, Kickstarter, laissez-faire capitalism, lateral thinking, Long Term Capital Management, low interest rates, market bubble, mass immigration, means of production, megaproject, Mexican peso crisis / tequila crisis, Michael Milken, Money creation, moral hazard, Myron Scholes, negative equity, offshore financial centre, open borders, open economy, Pearl River Delta, price mechanism, profit motive, quantitative easing, Ralph Waldo Emerson, regulatory arbitrage, rent-seeking, reserve currency, risk free rate, risk-adjusted returns, Ronald Reagan, Savings and loan crisis, savings glut, Scramble for Africa, short selling, social distancing, South China Sea, The Wealth of Nations by Adam Smith, too big to fail, yield curve

They are not periods in which the behaviour of investors fits readily into the academic models that have much greater validity in periods when more rational, if not irrational, behaviour pervades. As this book is composed of daily writings from the period when an illusion ended, it is also a guide as to what happens when illusions end and the search for a new certainty begins. Much of this book, written between 1995 and 1998, is uncontaminated by hindsight bias. Along with these views from the investment trenches you will also find new commentary that I have now added written with the benefit of hindsight. That commentary seeks to distil the key lessons for investors from the crisis era from a long and hopefully somewhat detached distance. This is then not a history of the Asian financial crisis per se, but a story of what it was like to guess “what was at the other side of the hill”, much as the Duke of Wellington explained to be “all the business of life”: All the business of war, and indeed all the business of life, is to endeavour to find out what you don’t know by what you do; that’s what I called “guessing what was at the other side of the hill”.

It was in their common errors, often paraded as wisdom in the Wall Street Journal, that Anatomy of the Bear sought answers to the question as to how one might invest at the bottom of a US equity bear market. This book seeks to do something similar but this time relying upon my own contemporaneous opinion regarding the future. While hindsight bias must slip into any edited version of contemporaneous opinion, this book is an attempt to show forecasting ‘warts and all’ rather than being covered with the heavy makeup of hindsight. As I think Anatomy of the Bear showed, it is in the mistakes commonly made that prices are diverted from their true value and thus opportunities created for long-term investors.


pages: 244 words: 58,247

The Gone Fishin' Portfolio: Get Wise, Get Wealthy...and Get on With Your Life by Alexander Green

Alan Greenspan, Albert Einstein, asset allocation, asset-backed security, backtesting, behavioural economics, borderless world, buy and hold, buy low sell high, cognitive dissonance, diversification, diversified portfolio, Elliott wave, endowment effect, Everybody Ought to Be Rich, financial independence, fixed income, framing effect, hedonic treadmill, high net worth, hindsight bias, impulse control, index fund, interest rate swap, Johann Wolfgang von Goethe, John Bogle, junk bonds, Long Term Capital Management, means of production, mental accounting, Michael Milken, money market fund, Paul Samuelson, Ponzi scheme, risk tolerance, risk-adjusted returns, short selling, statistical model, stocks for the long run, sunk-cost fallacy, transaction costs, Vanguard fund, yield curve

Shermer correctly points out, “Rationally, we should just compute the odds of succeeding from this point forward.” Yet investors who have sunk a lot into a stock—including a fair amount of ego—have trouble doing this. Mental accounting and the sunk-cost fallacy are just the tip of the iceberg. Shermer shows that consumers and investors also fall prey to cognitive dissonance, hindsight bias, self-justification, inattentional blindness, confirmation bias, the introspection illusion, the availability fallacy, self-serving bias, the representative fallacy, the law of small numbers, attribution bias, the low aversion effect, framing effects, the anchoring fallacy, the endowment effect, and blind spot bias.


100 Baggers: Stocks That Return 100-To-1 and How to Find Them by Christopher W Mayer

Alan Greenspan, asset light, bank run, Bear Stearns, Bernie Madoff, book value, business cycle, buy and hold, Carl Icahn, cloud computing, disintermediation, Dissolution of the Soviet Union, dumpster diving, Edward Thorp, Henry Singleton, hindsight bias, housing crisis, index fund, Jeff Bezos, market bubble, Network effects, new economy, oil shock, passive investing, peak oil, Pershing Square Capital Management, shareholder value, Silicon Valley, SimCity, Stanford marshmallow experiment, Steve Jobs, stock buybacks, survivorship bias, Teledyne, The Great Moderation, The Wisdom of Crowds, tontine

This would be the main population of stocks I poked and prodded in the six months after we created the database. I want to say a few words about what I set out to do—and what I don’t want to do. There are severe limitations or problems with a study like this. For one thing, I’m only looking at these extreme successes. There is hindsight bias, in that things can look obvious now. And there is survivorship bias, in that other companies may have looked similar at one point but failed to deliver a hundredfold gain. I am aware of these issues and others. They are hard to correct. I had a statistician, a newsletter reader, kindly offer to help.


pages: 512 words: 165,704

Traffic: Why We Drive the Way We Do (And What It Says About Us) by Tom Vanderbilt

Albert Einstein, autonomous vehicles, availability heuristic, Berlin Wall, Boeing 747, call centre, cellular automata, Cesare Marchetti: Marchetti’s constant, cognitive dissonance, computer vision, congestion charging, congestion pricing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, Donald Shoup, endowment effect, extreme commuting, fundamental attribution error, Garrett Hardin, Google Earth, hedonic treadmill, Herman Kahn, hindsight bias, hive mind, human-factors engineering, if you build it, they will come, impulse control, income inequality, Induced demand, invisible hand, Isaac Newton, Jane Jacobs, John Nash: game theory, Kenneth Arrow, lake wobegon effect, loss aversion, megacity, Milgram experiment, Nash equilibrium, PalmPilot, power law, Sam Peltzman, Silicon Valley, SimCity, statistical model, the built environment, The Death and Life of Great American Cities, Timothy McVeigh, traffic fines, Tragedy of the Commons, traumatic brain injury, ultimatum game, urban planning, urban sprawl, women in the workforce, working poor

Did they “just happen” or were there things that could have been done to prevent them, or at least greatly reduce the chances of their happening? Humans are humans, things will go wrong, there are instances of truly bad luck. And psychologists have argued that humans tend to exaggerate, in retrospect, just how predictable things were (the “hindsight bias”). The word accident, however, has been sent skittering down a slippery slope, to the point where it seems to provide protective cover for the worst and most negligent driving behaviors. This in turn suggests that so much of the everyday carnage on the road is mysteriously out of our hands and can be stopped or lessened only by adding more air bags (pedestrians, unfortunately, lack this safety feature).

killed a motorcyclist: Information on the Janklow case comes from the Argus Leader, August 31, 2003. “more unintentional than others”: See Teresa L. Kramer, Brenda M. Booth, Han Xiaotong, and Keith D. Williams, “Some Crashes Are More Unintentional Than Others: A Reply to Blanchard, Hicking, and Kuhn,” Journal of Traumatic Stress, vol. 16, no. 5 (October 2003), pp. 529–30. “hindsight bias”: For a seminal account, see Baruch Fischoff, “Hindsight Is Not Equal to Foresight: The Effect of Outcome Knowledge on Judgment Under Uncertainty,” Journal of Experimental Psychology: Human Perception and Performance, vol. 1, no. 2 (1975), pp. 288–99. intentional or not: In 1958, this number was said to be 88 out of 100.


Designing the Mind: The Principles of Psychitecture by Designing the Mind, Ryan A Bush

Abraham Maslow, adjacent possible, Albert Einstein, algorithmic bias, augmented reality, butterfly effect, carbon footprint, cognitive bias, cognitive load, correlation does not imply causation, data science, delayed gratification, deliberate practice, drug harm reduction, effective altruism, Elon Musk, en.wikipedia.org, endowment effect, fundamental attribution error, hedonic treadmill, hindsight bias, impulse control, Kevin Kelly, Lao Tzu, lifelogging, longitudinal study, loss aversion, meta-analysis, Own Your Own Home, pattern recognition, price anchoring, randomized controlled trial, Silicon Valley, Stanford marshmallow experiment, Steven Pinker, systems thinking, Walter Mischel

The feelings of confusion, surprise, and lack of clarity surrounding some views should sound alarm bells, triggering you to further investigate. Consider the reasons why your initial judgments might be flawed before forming important views. Simply adding the prompt “consider the opposite” to the default algorithm has been found to counter anchoring, overconfidence, and hindsight bias.21 Some biases can be mitigated or eliminated by finding outside factual information, so take every opportunity to distance yourself and put your reasoning and beliefs to objective tests to remove as much human bias from the equation as possible. Not-knowing is true knowledge. Presuming to know is a disease.


pages: 272 words: 19,172

Hedge Fund Market Wizards by Jack D. Schwager

asset-backed security, backtesting, banking crisis, barriers to entry, Bear Stearns, beat the dealer, Bernie Madoff, Black-Scholes formula, book value, British Empire, business cycle, buy and hold, buy the rumour, sell the news, Claude Shannon: information theory, clean tech, cloud computing, collateralized debt obligation, commodity trading advisor, computerized trading, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, do what you love, Edward Thorp, family office, financial independence, fixed income, Flash crash, global macro, hindsight bias, implied volatility, index fund, intangible asset, James Dyson, Jones Act, legacy carrier, Long Term Capital Management, managed futures, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, merger arbitrage, Michael Milken, money market fund, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, proprietary trading, quantitative easing, quantitative trading / quantitative finance, Reminiscences of a Stock Operator, Right to Buy, risk free rate, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Rubik’s Cube, Savings and loan crisis, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve

For example, it makes sense that price-derived data series, such as volatility or price acceleration, might provide important information. The list of secondary variables derived from price is the part I built manually. Then I have a framework for combining the secondary variables in all sorts of combinations to see what works. I wanted to hand that work off to the computer, but I knew how important it was to have the hindsight bias and overfit problem figured out. As an aside, I am still trying to reverse engineer some of the models that we have come up with that are so interesting and amazing. What do these patterns say about the psychology of the marketplace? Frankly, I’m not sure yet. You are constructing models by selecting combinations of secondary variables formed from a list of hundreds of possible secondary variables.

When searching very large numbers of combinations of past price data for patterns, it is easy to come up with many patterns that worked well in the past simply by chance, but have no predictive value. This common pitfall of applying data mining to price data is the reason why the term often has derogatory connotations in reference to trading systems. 6To avoid hindsight bias error in developing trading systems, the available past data is segmented into seen data (i.e., “in-sample”) that is used for system development and unseen data (i.e., “out-of-sample”) that is used for system testing. Any results on the in-sample data are ignored because they are hindsight-biased.


pages: 290 words: 76,216

What's Wrong With Economics: A Primer for the Perplexed by Robert Skidelsky

additive manufacturing, agricultural Revolution, behavioural economics, Black Swan, Bretton Woods, business cycle, carbon tax, Cass Sunstein, central bank independence, cognitive bias, conceptual framework, Corn Laws, corporate social responsibility, correlation does not imply causation, creative destruction, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, degrowth, disruptive innovation, Donald Trump, Dr. Strangelove, full employment, George Akerlof, George Santayana, global supply chain, global village, Gunnar Myrdal, happiness index / gross national happiness, hindsight bias, Hyman Minsky, income inequality, index fund, inflation targeting, information asymmetry, Internet Archive, invisible hand, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kenneth Arrow, knowledge economy, labour market flexibility, loss aversion, Mahbub ul Haq, Mark Zuckerberg, market clearing, market friction, market fundamentalism, Martin Wolf, means of production, Modern Monetary Theory, moral hazard, paradox of thrift, Pareto efficiency, Paul Samuelson, Philip Mirowski, Phillips curve, precariat, price anchoring, principal–agent problem, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Solow, Ronald Coase, shareholder value, Silicon Valley, Simon Kuznets, sunk-cost fallacy, survivorship bias, technoutopianism, The Chicago School, The Market for Lemons, The Nature of the Firm, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Thorstein Veblen, Tragedy of the Commons, transaction costs, transfer pricing, Vilfredo Pareto, Washington Consensus, Wolfgang Streeck, zero-sum game

Sunk cost fallacy This is a combination of anchoring and loss aversion. People will keep on ploughing money into a failed investment, because they can’t face the psychological pain of admitting that it had failed, or carry on waging a war that they should have abandoned long ago, because they cannot bring themselves to admit that it was in vain. 7. Hindsight bias This is central to human thinking and makes the social and economic worlds appear much more predictable and less erratic than they really are. No prominent economist predicted the financial crisis. Yet almost the next day commentators were rushing in to explain why it ‘must’ have happened when and how it did.


pages: 261 words: 86,905

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

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, asset allocation, Basel III, behavioural economics, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamond, Bretton Woods, BRICs, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, commoditize, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, fear index, financial engineering, financial innovation, Flash crash, forward guidance, Garrett Hardin, Gini coefficient, Glass-Steagall Act, global reserve currency, high net worth, High speed trading, hindsight bias, hype cycle, income inequality, inflation targeting, interest rate swap, inverted yield curve, Isaac Newton, Jaron Lanier, John Perry Barlow, joint-stock company, joint-stock limited liability company, junk bonds, Kodak vs Instagram, Kondratiev cycle, Large Hadron Collider, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, low interest rates, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, negative equity, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Nixon triggered the end of the Bretton Woods system, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, plutocrats, Ponzi scheme, precautionary principle, proprietary trading, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Right to Buy, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, sovereign wealth fund, Steve Jobs, survivorship bias, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, trickle-down economics, two and twenty, Two Sigma, Tyler Cowen, Washington Consensus, wealth creators, working poor, yield curve

The fact that people don’t always behave rationally may not come as news in the wider world, but the intellectual challenge provided to conventional economics by behavioral economics is big and important. It’s also a field that offers useful takeaways for the ordinary person, because you can catch yourself doing some of the things described by behavioral economists, such as loss aversion and “hindsight bias,” i.e., the tendency to explain things that happened in terms of how they turned out, rather than how they seemed at the time. Some practical applications of behavioral economics are in fields such as the “nudge,” which involves prompting individuals to behave in a certain way. The prompting is usually on the part of businesses or governments.


pages: 309 words: 95,495

Foolproof: Why Safety Can Be Dangerous and How Danger Makes Us Safe by Greg Ip

Affordable Care Act / Obamacare, Air France Flight 447, air freight, airport security, Alan Greenspan, Asian financial crisis, asset-backed security, bank run, banking crisis, Bear Stearns, behavioural economics, Boeing 747, book value, break the buck, Bretton Woods, business cycle, capital controls, central bank independence, cloud computing, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, Daniel Kahneman / Amos Tversky, diversified portfolio, double helix, endowment effect, Exxon Valdez, Eyjafjallajökull, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, foreign exchange controls, full employment, global supply chain, hindsight bias, Hyman Minsky, Joseph Schumpeter, junk bonds, Kenneth Rogoff, lateral thinking, Lewis Mumford, London Whale, Long Term Capital Management, market bubble, Michael Milken, money market fund, moral hazard, Myron Scholes, Network effects, new economy, offshore financial centre, paradox of thrift, pets.com, Ponzi scheme, proprietary trading, quantitative easing, Ralph Nader, Richard Thaler, risk tolerance, Ronald Reagan, Sam Peltzman, savings glut, scientific management, subprime mortgage crisis, tail risk, technology bubble, TED Talk, The Great Moderation, too big to fail, transaction costs, union organizing, Unsafe at Any Speed, value at risk, William Langewiesche, zero-sum game

In aviation, the fear of disaster is a powerful motivator, she says, quoting a Japanese peer: “If you think you are safe, you are dangerous. If you think you are dangerous, you are safe.” Near-miss reporting is qualitatively different from accident reporting. Since by definition no accident occurred, it is free of “hindsight bias,” the tendency to assume a certain explanation since you already know the outcome. Near misses also occur much more frequently than accidents and thus are more likely to generate patterns worthy of action. Anonymity is central to the system; incident reporters need to know their candor will not get them disciplined or sued.


pages: 316 words: 94,886

Decisive: How to Make Better Choices in Life and Work by Chip Heath, Dan Heath

behavioural economics, billion-dollar mistake, call centre, Captain Sullenberger Hudson, Cass Sunstein, classic study, Daniel Kahneman / Amos Tversky, en.wikipedia.org, endowment effect, Great Leap Forward, hindsight bias, index fund, it is difficult to get a man to understand something, when his salary depends on his not understanding it, job satisfaction, Kevin Kelly, loss aversion, Max Levchin, medical residency, mental accounting, meta-analysis, Mikhail Gorbachev, PalmPilot, Paradox of Choice, pattern recognition, Peter Thiel, pets.com, Richard Thaler, Ronald Reagan, shareholder value, Silicon Valley, unpaid internship, Upton Sinclair, US Airways Flight 1549, young professional

Considering the opposite has been shown to reduce several biases that have been regarded as especially thorny: the overconfident conclusions that we highlight in chapter 10 (and that were demonstrated by CEOs in the hubris study in this chapter) and other, quite different, biases ranging from a hindsight bias that leads us to see anything that happens as inevitable to a tendency to anchor too strongly on a specific numerical value (e.g., basing this year’s budget allocation heavily on last year’s, even if the situation has changed dramatically). 14 Schoemaker’s deliberate mistake. The RFP deliberate mistake story is told in Paul J.


pages: 292 words: 94,660

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back by Jacob Ward

2021 United States Capitol attack, 4chan, Abraham Wald, AI winter, Albert Einstein, Albert Michelson, Amazon Mechanical Turk, assortative mating, autonomous vehicles, availability heuristic, barriers to entry, Bayesian statistics, Benoit Mandelbrot, Big Tech, bitcoin, Black Lives Matter, Black Swan, blockchain, Broken windows theory, call centre, Cass Sunstein, cloud computing, contact tracing, coronavirus, COVID-19, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, data science, deep learning, Donald Trump, drone strike, endowment effect, George Akerlof, George Floyd, hindsight bias, invisible hand, Isaac Newton, Jeffrey Epstein, license plate recognition, lockdown, longitudinal study, Lyft, mandelbrot fractal, Mark Zuckerberg, meta-analysis, natural language processing, non-fungible token, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, QAnon, RAND corporation, Richard Thaler, Robert Shiller, selection bias, self-driving car, seminal paper, shareholder value, smart cities, social contagion, social distancing, Steven Levy, survivorship bias, TikTok, Turing test

Paul Meehl, a University of Minnesota professor of psychology, had just published, in 1973, a paper titled “Why I Do Not Attend Case Conferences,” a twelve-point critique of the tendencies of experts gathered at professional meetings to make a hash of their subject after endless arguing and schmoozing. “I thought I could build some science out of that idea,” Fischhoff says. “So I came up with a handful of experiments, and they stood up ridiculously well over time.” What Fischhoff established was another systematic error: the hindsight bias. In his paper, “Hindsight ≠ Foresight: The Effect of Outcome Knowledge on Judgment Under Uncertainty,” Fischhoff coined the term “creeping determinism,” which he described as “the tendency to perceive reported outcomes as having been relatively inevitable.” In the study, when Fischhoff described the outcome of a historical situation to participants—Nixon’s visit to China, a military engagement between British and Nepalese forces—they said that the outcome was the most probable, and they also perceived the odds of it happening as roughly double what people who didn’t know the outcome would estimate.


pages: 831 words: 98,409

SUPERHUBS: How the Financial Elite and Their Networks Rule Our World by Sandra Navidi

"World Economic Forum" Davos, activist fund / activist shareholder / activist investor, Alan Greenspan, Anthropocene, assortative mating, bank run, barriers to entry, Bear Stearns, Bernie Sanders, Black Swan, Blythe Masters, Bretton Woods, butterfly effect, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, commoditize, conceptual framework, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, digital divide, diversification, Dunbar number, East Village, eat what you kill, Elon Musk, eurozone crisis, fake it until you make it, family office, financial engineering, financial repression, Gini coefficient, glass ceiling, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Google bus, Gordon Gekko, haute cuisine, high net worth, hindsight bias, income inequality, index fund, intangible asset, Jaron Lanier, Jim Simons, John Meriwether, junk bonds, Kenneth Arrow, Kenneth Rogoff, Kevin Roose, knowledge economy, London Whale, Long Term Capital Management, longitudinal study, Mark Zuckerberg, mass immigration, McMansion, mittelstand, Money creation, money market fund, Myron Scholes, NetJets, Network effects, no-fly zone, offshore financial centre, old-boy network, Parag Khanna, Paul Samuelson, peer-to-peer, performance metric, Peter Thiel, plutocrats, Ponzi scheme, power law, public intellectual, quantitative easing, Renaissance Technologies, rent-seeking, reserve currency, risk tolerance, Robert Gordon, Robert Shiller, rolodex, Satyajit Das, search costs, shareholder value, Sheryl Sandberg, Silicon Valley, social intelligence, sovereign wealth fund, Stephen Hawking, Steve Jobs, subprime mortgage crisis, systems thinking, tech billionaire, The Future of Employment, The Predators' Ball, The Rise and Fall of American Growth, too big to fail, Tyler Cowen, women in the workforce, young professional

When occurrences are too complex to be understood, we tend to weave narratives and assign blame to a perceived higher power—often exploitative financial masterminds colluding at the expense of the rest of society. This kind of thinking is influenced by confirmation bias, which means seeking support for an existing belief, or hindsight bias, the subsequent fabrication of explanations for something that already took place. Conspiracy theories are dangerous because at best they dumb down the population and at worst they prevent finding real solutions; such theories deflect the relevant facts and, therefore, avoid a proper analysis.


pages: 289 words: 95,046

Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis by Scott Patterson

"World Economic Forum" Davos, 2021 United States Capitol attack, 4chan, Alan Greenspan, Albert Einstein, asset allocation, backtesting, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, bitcoin, Bitcoin "FTX", Black Lives Matter, Black Monday: stock market crash in 1987, Black Swan, Black Swan Protection Protocol, Black-Scholes formula, blockchain, Bob Litterman, Boris Johnson, Brownian motion, butterfly effect, carbon footprint, carbon tax, Carl Icahn, centre right, clean tech, clean water, collapse of Lehman Brothers, Colonization of Mars, commodity super cycle, complexity theory, contact tracing, coronavirus, correlation does not imply causation, COVID-19, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, decarbonisation, disinformation, diversification, Donald Trump, Doomsday Clock, Edward Lloyd's coffeehouse, effective altruism, Elliott wave, Elon Musk, energy transition, Eugene Fama: efficient market hypothesis, Extinction Rebellion, fear index, financial engineering, fixed income, Flash crash, Gail Bradbrook, George Floyd, global pandemic, global supply chain, Gordon Gekko, Greenspan put, Greta Thunberg, hindsight bias, index fund, interest rate derivative, Intergovernmental Panel on Climate Change (IPCC), Jeff Bezos, Jeffrey Epstein, Joan Didion, John von Neumann, junk bonds, Just-in-time delivery, lockdown, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Mark Spitznagel, Mark Zuckerberg, market fundamentalism, mass immigration, megacity, Mikhail Gorbachev, Mohammed Bouazizi, money market fund, moral hazard, Murray Gell-Mann, Nick Bostrom, off-the-grid, panic early, Pershing Square Capital Management, Peter Singer: altruism, Ponzi scheme, power law, precautionary principle, prediction markets, proprietary trading, public intellectual, QAnon, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Nader, Ralph Nelson Elliott, random walk, Renaissance Technologies, rewilding, Richard Thaler, risk/return, road to serfdom, Ronald Reagan, Ronald Reagan: Tear down this wall, Rory Sutherland, Rupert Read, Sam Bankman-Fried, Silicon Valley, six sigma, smart contracts, social distancing, sovereign wealth fund, statistical arbitrage, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, systematic trading, tail risk, technoutopianism, The Chicago School, The Great Moderation, the scientific method, too big to fail, transaction costs, University of East Anglia, value at risk, Vanguard fund, We are as Gods, Whole Earth Catalog

“But before Taleb, everyone assumed that how you managed day-to-day events was the main thing; only after you got that squared away did you devote maybe five percent of your attention to the ‘outliers.’ Taleb demonstrated that if you got the outliers right you did well, and if you got them wrong you didn’t survive long enough for your performance to matter.” Another key concept in the book was hindsight bias—the tendency people have to claim, after a Black Swan event, that they could see it coming all along. The political scientist Philip Tetlock illustrated the concept in his 2016 book Superforecasting: The Art and Science of Prediction. He recounted how in 1988, when Soviet president Mikhail Gorbachev was implementing a series of major reforms such as glasnost (a more open society), he asked experts to estimate the odds that the Communist Party would lose its grip on the country in the next five years.


pages: 477 words: 106,069

The Sense of Style: The Thinking Person's Guide to Writing in the 21st Century by Steven Pinker

butterfly effect, carbon footprint, cognitive load, crowdsourcing, Douglas Hofstadter, feminist movement, functional fixedness, hindsight bias, illegal immigration, index card, invention of the printing press, invention of the telephone, language acquisition, lolcat, McMansion, meta-analysis, moral panic, Nelson Mandela, off-the-grid, profit maximization, quantitative easing, quantum entanglement, race to the bottom, Ralph Waldo Emerson, Richard Feynman, short selling, Steven Pinker, the market place, theory of mind, Turing machine

The inability to set aside something that you know but that someone else does not know is such a pervasive affliction of the human mind that psychologists keep discovering related versions of it and giving it new names. There is egocentrism, the inability of children to imagine a simple scene, such as three toy mountains on a tabletop, from another person’s vantage point.4 There’s hindsight bias, the tendency of people to think that an outcome they happen to know, such as the confirmation of a disease diagnosis or the outcome of a war, should have been obvious to someone who had to make a prediction about it before the fact.5 There’s false consensus, in which people who make a touchy personal decision (like agreeing to help an experimenter by wearing a sandwich board around campus with the word REPENT) assume that everyone else would make the same decision.6 There’s illusory transparency, in which observers who privately know the backstory to a conversation and thus can tell that a speaker is being sarcastic assume that the speaker’s naïve listeners can somehow detect the sarcasm, too.7 And there’s mindblindness, a failure to mentalize, or a lack of a theory of mind, in which a three-year-old who sees a toy being hidden while a second child is out of the room assumes that the other child will look for it in its actual location rather than where she last saw it.8 (In a related demonstration, a child comes into the lab, opens a candy box, and is surprised to find pencils in it.


pages: 412 words: 115,266

The Moral Landscape: How Science Can Determine Human Values by Sam Harris

Albert Einstein, banking crisis, Bayesian statistics, behavioural economics, cognitive bias, cognitive load, end world poverty, endowment effect, energy security, experimental subject, framing effect, higher-order functions, hindsight bias, impulse control, John Nash: game theory, language acquisition, longitudinal study, loss aversion, meta-analysis, mirror neurons, Monty Hall problem, out of africa, Paradox of Choice, pattern recognition, peak-end rule, placebo effect, Ponzi scheme, public intellectual, Richard Feynman, risk tolerance, scientific worldview, stem cell, Stephen Hawking, Steven Pinker, TED Talk, the scientific method, theory of mind, traumatic brain injury, trolley problem, ultimatum game, World Values Survey

There are many factors that bias our judgment, including: arbitrary anchors on estimates of quantity, availability biases on estimates of frequency, insensitivity to the prior probability of outcomes, misconceptions of randomness, nonregressive predictions, insensitivity to sample size, illusory correlations, overconfidence, valuing of worthless evidence, hindsight bias, confirmation bias, biases based on ease of imaginability, as well as other nonnormative modes of thinking. See Baron, 2008; J. S. B. T. Evans, 2005; Kahneman, 2003; Kahneman, Krueger, Schkade, Schwarz, & Stone, 2006; Kahneman, Slovic, & Tversky, 1982; Kahneman & Tversky, 1996; Stanovich & West, 2000; Tversky & Kahneman, 1974. 37.


pages: 416 words: 118,592

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing by Burton G. Malkiel

accounting loophole / creative accounting, Alan Greenspan, Albert Einstein, asset allocation, asset-backed security, backtesting, Bear Stearns, beat the dealer, Bernie Madoff, book value, BRICs, butter production in bangladesh, buy and hold, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, dogs of the Dow, Edward Thorp, Elliott wave, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial engineering, financial innovation, fixed income, framing effect, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Japanese asset price bubble, John Bogle, junk bonds, Long Term Capital Management, loss aversion, low interest rates, margin call, market bubble, Mary Meeker, money market fund, mortgage tax deduction, new economy, Own Your Own Home, PalmPilot, passive investing, Paul Samuelson, pets.com, Ponzi scheme, price stability, profit maximization, publish or perish, purchasing power parity, RAND corporation, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, short selling, Silicon Valley, South Sea Bubble, stock buybacks, stocks for the long run, sugar pill, survivorship bias, The Myth of the Rational Market, the rule of 72, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond

Two behavioral economists, Terrance Odean and Brad Barber, examined the individual accounts at a large discount broker over a substantial period of time. They found that the more individual investors traded, the worse they did. And male investors traded much more than women, with correspondingly poorer results. This illusion of financial skill may well stem from another psychological finding, called hindsight bias. Such errors are sustained by having a selective memory of success. You remember your successful investments. And in hindsight, it is easy to convince yourself that you “knew Google was going to quintuple right after its initial public offering.” People are prone to attribute any good outcome to their own abilities.


pages: 358 words: 119,272

Anatomy of the Bear: Lessons From Wall Street's Four Great Bottoms by Russell Napier

Alan Greenspan, Albert Einstein, asset allocation, banking crisis, Bear Stearns, behavioural economics, book value, Bretton Woods, business cycle, buy and hold, collective bargaining, Columbine, cuban missile crisis, desegregation, diversified portfolio, fake news, financial engineering, floating exchange rates, Fractional reserve banking, full employment, Glass-Steagall Act, global macro, hindsight bias, Kickstarter, Long Term Capital Management, low interest rates, market bubble, Michael Milken, military-industrial complex, Money creation, mortgage tax deduction, Myron Scholes, new economy, Nixon triggered the end of the Bretton Woods system, oil shock, price stability, reserve currency, risk free rate, Robert Gordon, Robert Shiller, Ronald Reagan, short selling, stocks for the long run, yield curve, Yogi Berra

For those who accept that human judgement and decision-making cannot be divined by equations, financial market history is a guide to understanding the future. The particular value in financial market history comes from its insight into the operation of human judgement under uncertainty, in particular its examination of contemporaneous opinion. While any historian is liable to hindsight bias, a focus on contemporary comments and reactions at least reduces the risks of projecting one’s own order on things. As a historical source, newspapers offer an efficient daily collation of events and, in the financial press, with a focus on the markets, this has been the best practical repository of contemporary opinion for the past century or more.


pages: 471 words: 124,585

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

Admiral Zheng, Alan Greenspan, An Inconvenient Truth, Andrei Shleifer, Asian financial crisis, asset allocation, asset-backed security, Atahualpa, bank run, banking crisis, banks create money, Bear Stearns, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, BRICs, British Empire, business cycle, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, classic study, collateralized debt obligation, colonial exploitation, commoditize, Corn Laws, corporate governance, creative destruction, 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, equity risk premium, financial engineering, financial innovation, financial intermediation, fixed income, floating exchange rates, Fractional reserve banking, Francisco Pizarro, full employment, Future Shock, German hyperinflation, Greenspan put, Herman Kahn, Hernando de Soto, high net worth, hindsight bias, Home mortgage interest deduction, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, iterative process, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", John Meriwether, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kenneth Rogoff, knowledge economy, labour mobility, Landlord’s Game, liberal capitalism, London Interbank Offered Rate, Long Term Capital Management, low interest rates, market bubble, market fundamentalism, means of production, Mikhail Gorbachev, Modern Monetary Theory, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, Naomi Klein, National Debt Clock, negative equity, Nelson Mandela, Nick Bostrom, Nick Leeson, Northern Rock, Parag Khanna, 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, risk free rate, Robert Shiller, rolling blackouts, Ronald Reagan, Savings and loan crisis, savings glut, seigniorage, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spice trade, stocks for the long run, structural adjustment programs, subprime mortgage crisis, tail risk, technology bubble, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Thorstein Veblen, tontine, too big to fail, transaction costs, two and twenty, undersea cable, value at risk, W. E. B. Du Bois, Washington Consensus, Yom Kippur War

A loss has about two and a half times the impact of a gain of the same magnitude.10 This ‘failure of invariance’ is only one of many heuristic biases (skewed modes of thinking or learning) that distinguish real human beings from the homo oeconomicus of neoclassical economic theory, who is supposed to make his decisions rationally, on the basis of all the available information and his expected utility. Other experiments show that we also succumb too readily to such cognitive traps as:1. Availability bias, which causes us to base decisions on information that is more readily available in our memories, rather than the data we really need; 2. Hindsight bias, which causes us to attach higher probabilities to events after they have happened (ex post) than we did before they happened (ex ante); 3. The problem of induction, which leads us to formulate general rules on the basis of insufficient information; 4. The fallacy of conjunction (or disjunction), which means we tend to overestimate the probability that seven events of 90 per cent probability will all occur, while underestimating the probability that at least one of seven events of 10 per cent probability will occur; 5.


pages: 467 words: 154,960

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

Albert Einstein, Alvin Toffler, Atul Gawande, backtesting, Bear Stearns, beat the dealer, Bernie Madoff, Black Swan, buy and hold, buy low sell high, California energy crisis, capital asset pricing model, Carl Icahn, Clayton Christensen, commodity trading advisor, computerized trading, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Edward Thorp, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, fiat currency, fixed income, Future Shock, game design, global macro, hindsight bias, housing crisis, index fund, Isaac Newton, Jim Simons, John Bogle, John Meriwether, John Nash: game theory, linear programming, Long Term Capital Management, managed futures, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, Market Wizards by Jack D. Schwager, mental accounting, money market fund, Myron Scholes, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, survivorship bias, systematic trading, Teledyne, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, William of Occam, zero-sum game

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.


pages: 450 words: 144,939

Unthinkable: Trauma, Truth, and the Trials of American Democracy by Jamie Raskin

2021 United States Capitol attack, affirmative action, Affordable Care Act / Obamacare, back-to-the-land, Bernie Sanders, Black Lives Matter, clean water, coronavirus, COVID-19, cuban missile crisis, defund the police, desegregation, disinformation, Donald Trump, failed state, fake news, George Floyd, hindsight bias, Johann Wolfgang von Goethe, Lyft, mandatory minimum, opioid epidemic / opioid crisis, public intellectual, QAnon, race to the bottom, Ronald Reagan, Silicon Valley, social distancing, Steve Bannon, traumatic brain injury, trolley problem

He was stage acting. He was acting out the theme of I am not depressed and everything is fine because he did not want to alert us or activate us to undo his plans. A stranger might have met Tommy that week and found him witty, beguiling, and dry. But when I look back on it—and undoubtedly there is some hindsight bias here (the tendency to see prior events as far more predictable and inevitable than they were)—I see him that week as being strangely affectless and not truly present with us. His girlfriend, Sarah, agreed, saying he was “withholding—emotionally. The lights were on, but nobody was home. You couldn’t break down what he was really saying.


pages: 687 words: 189,243

A Culture of Growth: The Origins of the Modern Economy by Joel Mokyr

Andrei Shleifer, barriers to entry, Berlin Wall, business cycle, classic study, clockwork universe, cognitive dissonance, Copley Medal, creative destruction, David Ricardo: comparative advantage, delayed gratification, deliberate practice, Deng Xiaoping, Edmond Halley, Edward Jenner, epigenetics, Fellow of the Royal Society, financial independence, flying shuttle, framing effect, germ theory of disease, Haber-Bosch Process, Herbert Marcuse, hindsight bias, income inequality, information asymmetry, invention of movable type, invention of the printing press, invisible hand, Isaac Newton, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, Johannes Kepler, John Harrison: Longitude, Joseph Schumpeter, knowledge economy, labor-force participation, land tenure, law of one price, Menlo Park, moveable type in China, new economy, phenotype, price stability, principal–agent problem, rent-seeking, Republic of Letters, Robert Solow, Ronald Reagan, seminal paper, South Sea Bubble, statistical model, survivorship bias, tacit knowledge, the market place, the strength of weak ties, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, ultimatum game, World Values Survey, Wunderkammern

As noted, the growing consensus that characterized Enlightenment Europe was a mechanistic view of the universe. There were fixed and clear rules by which nature operated, and humankind’s challenge was to discover these knowable rules and take advantage of them. Yet the view that these differences somehow handicapped the Chinese and caused a “failure” can be criticized as an example of the hindsight bias that just because Europe created what became known as “modern science,” this was the only way that technological progress and economic growth could have occurred. Evolutionary theory suggests that the actual outcomes we observe are but a small fraction of the outcomes that are feasible, and we simply have no way of imagining how Chinese useful knowledge would have evolved in the long run had it not been exposed to Western culture and whether it would not have produced a material culture comparable to the one produced by the European Industrial Enlightenment.


pages: 701 words: 199,010

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

affirmative action, Alan Greenspan, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bear Stearns, Bernie Madoff, Black-Scholes formula, Bob Litterman, business cycle, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, Glass-Steagall Act, global macro, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, John Meriwether, Kickstarter, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low interest rates, low skilled workers, managed futures, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, National best bid and offer, negative equity, Northern Rock, Occupy movement, oil shock, price stability, proprietary trading, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ronald Reagan, Sam Peltzman, Savings and loan crisis, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, tail risk, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

Morgan, Barclays, Bank of America, HSBC, Citibank, Fitch, Moody’s, S&P, and others. In total, the examiner collected more than five million documents and interviewed many of the principal people at Lehman, the Fed, the Treasury, and other institutions. Unfortunately, many of these interviews were done ex-post, so the material may have hindsight bias. 15. Lehman Brothers had subsidiaries that focused on mortgage origination, such as BNC Mortgage Inc. and Aurora Loan Services, LLC. 16. “Double down” comes from the gambling game blackjack. In blackjack, after receiving the first card, a player can double the initial bet by committing to accept only one more card.