56 results back to index
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, 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, feminist movement, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, mental accounting, meta analysis, meta-analysis, p-value, pattern recognition, Paul Samuelson, Ponzi scheme, price anchoring, price stability, quantitative trading / quantitative ﬁnance, Ralph Nelson Elliott, random walk, retrograde motion, revision control, risk tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Robert Shiller, Sharpe ratio, short selling, source of truth, statistical model, stocks for the long run, 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. They claim that the seeds of Nazism were obvious in various writings that preceded the Third Reich.
Other experimental evidence shows that strategies aimed speciﬁcally at reducing the hindsight bias are not effective.54 Even when people are warned about hindsight bias and told to avoid it, it still occurs. It appears to be beyond rational control. Not even professional expertise is helpful. In one study, a group of doctors were asked to evaluate the diagnostic errors made by other doctors. The doctors doing the evaluations were armed with the knowledge of the disease that was ultimately conﬁrmed by a pathology report. The evaluators were unable to understand how such errors could have been made by a trained physician. Again, outcome knowledge makes the past appear as if it should have been more predictable than it really was. What cognitive processes are responsible for hindsight bias? Though The Illusory Validity of Subjective Technical Analysis 57 the matter is not settled, it seems to go beyond a desire to see ourselves as smart and in control.
The various methods used become familiar as they are studied and used regularly. The ﬁfth factor, early success, is a matter of chance. 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.
Thinking in Bets by Annie Duke
banking crisis, Bernie Madoff, Cass Sunstein, cognitive bias, cognitive dissonance, Daniel Kahneman / Amos Tversky, delayed gratification, Donald Trump, en.wikipedia.org, endowment effect, Estimating the Reproducibility of Psychological Science, Filter Bubble, 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, the scientific method, The Signal and the Noise by Nate Silver, urban planning, Walter Mischel, Yogi Berra, zero-sum game
He obviously felt a lot of anguish and regret because of the decision. 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.
In addressing the punitive damages against West Side, Judge Easterbrook, writing for the court, pointed out that Illinois law required a “gross deviation” from the standard of care to award punitive damages. 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. The members of the jury had a conflict of interest. When they heard the story of the men entering the tunnel to retrieve the tools, they knew the outcome.
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. I had graduated from Columbia University with degrees in English and psychology.
Super Thinking: The Big Book of Mental Models by Gabriel Weinberg, Lauren McCann
affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Gödel, Escher, Bach, hindsight bias, housing crisis, Ignaz Semmelweis: hand washing, illegal immigration, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, lateral thinking, loss aversion, Louis Pasteur, Lyft, mail merge, Mark Zuckerberg, meta analysis, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nuclear winter, offshore financial centre, p-value, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, survivorship bias, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, Vilfredo Pareto, wikimedia commons
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. Hindsight bias is not a factor when you are reviewing the many instances of predictable errors out there. The key is distinguishing between the two situations. Self-serving bias (see Chapter 1) suggests that you will be more inclined to say that your own or your group’s mistakes could not have been predicted (“Who could have known?”) and you are more likely to apply hindsight bias to be critical of others. The mental models from this section can help correct psychological mischaracterizations (e.g., impostor syndrome), artificial roadblocks (e.g., fixed mindset), and misinformation (e.g., hindsight bias), all in the service of helping people, including yourself, think objectively about current performance and ways to improve.
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. Think of the 2007/2008 financial crisis or the U.S. 2016 election cycles. Hindsight bias arises in many other situations: judges weighing evidence in court cases, historians analyzing past events, and physicians assessing earlier clinical decisions.
For example, in negligence cases, for guilt to be found, it must be shown that the person who committed the negligent act would have known that their actions would endanger others. 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.
Retirementology: Rethinking the American Dream in a New Economy by Gregory Brandon Salsbury
Albert Einstein, asset allocation, 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, negative equity, new economy, RFID, Richard Thaler, risk tolerance, Robert Shiller, 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. If you were making an investment in a commodity such as grain, and a grain investment that year turned out to be lousy because of weather, you may say after the fact that you should have known the weather would not be good for your investment.
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!
The Retirement Brain Game Number numbness—The tendency for a person to be simply overwhelmed by numbers presented, mainly because the numbers are so big that the person can’t comprehend exactly how big they are. 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. Number Numbness Multiplied by Three In their book Why Smart People Make Big Money Mistakes and How to Correct Them, authors Gary Belsky and Thomas Gilovich report the three ways that number numbness can affect long-term financial plans.
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, security theater, the medium is the message, traffic fines, urban planning
Suppose the police do a dragnet search for drugs, but they don’t find any in your house. 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. This is why warrants are issued in advance.
In many cases, warrants aren’t inconsistent with the prevention of crime. 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. They can arrest you and search your body. Warrants prevent law-enforcement officials from doing these things at their mere whim, for entertainment, because they harbor personal animus toward you, because they’re prejudiced against your race, religion, or ethnicity, because they don’t like your beliefs or what you say, or because they don’t like things you’ve done, or your career, or people you’re friendly with.
This is why warrants are issued in advance. The court knows what the police know. 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. After he was confirmed, 78 percent claimed to have thought beforehand that he would be confirmed.16 In another study, people were told about a train with toxic chemicals about to embark on a treacherous route through the mountains.
Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray, Tobias E. Carlisle
activist fund / activist shareholder / activist investor, Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, beat the dealer, Black Swan, business cycle, butter production in bangladesh, buy and hold, capital asset pricing model, Checklist Manifesto, cognitive bias, compound rate of return, corporate governance, correlation coefficient, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, Edward Thorp, Eugene Fama: efficient market hypothesis, forensic accounting, hindsight bias, intangible asset, Louis Bachelier, p-value, passive investing, performance metric, quantitative hedge fund, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, statistical model, survivorship bias, systematic trading, The Myth of the Rational Market, time value of money, transaction costs
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. 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.
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?” or “What is the probability that process A will generate outcome B?” The neglect-of-the-base-case bias is caused by a heuristic called representativeness. It is called the representativeness heuristic because we answer the questions by determining how much A represents—or resembles—B, rather than determining the likelihood of A given 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.
Superforecasting: The Art and Science of Prediction by Philip Tetlock, Dan Gardner
Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, availability heuristic, Black Swan, butterfly effect, buy and hold, cloud computing, cuban missile crisis, Daniel Kahneman / Amos Tversky, desegregation, drone strike, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, index fund, Jane Jacobs, Jeff Bezos, Kenneth Arrow, Laplace demon, longitudinal study, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, Nelson Mandela, obamacare, pattern recognition, performance metric, Pierre-Simon Laplace, place-making, placebo effect, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, scientific worldview, Silicon Valley, Skype, statistical model, stem cell, Steve Ballmer, Steve Jobs, Steven Pinker, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!
The second big barrier to feedback is time lag. 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. So perhaps you thought his chances were pretty good, but, obviously, he also stood a pretty good chance of losing.
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.
So in 1992–93 I returned to the experts, reminded them of the question in 1988, and asked them to recall their estimates. 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. The only feedback they get are sounds—the clang of the ball hitting metal, the thunk of the ball hitting the backboard, the swish of the ball brushing against the net.
More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded) by Michael J. Mauboussin
Albert Einstein, Andrei Shleifer, Atul Gawande, availability heuristic, beat the dealer, Benoit Mandelbrot, Black Swan, Brownian motion, butter production in bangladesh, buy and hold, capital asset pricing model, Clayton Christensen, clockwork universe, complexity theory, corporate governance, creative destruction, Daniel Kahneman / Amos Tversky, deliberate practice, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, dogs of the Dow, Drosophila, Edward Thorp, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, fixed income, framing effect, functional fixedness, hindsight bias, hiring and firing, Howard Rheingold, index fund, information asymmetry, intangible asset, invisible hand, Isaac Newton, Jeff Bezos, Kenneth Arrow, Laplace demon, Long Term Capital Management, loss aversion, mandelbrot fractal, margin call, market bubble, Menlo Park, mental accounting, Milgram experiment, Murray Gell-Mann, Nash equilibrium, new economy, Paul Samuelson, Pierre-Simon Laplace, quantitative trading / quantitative ﬁnance, random walk, Richard Florida, Richard Thaler, Robert Shiller, Robert Shiller, shareholder value, statistical model, Steven Pinker, stocks for the long run, survivorship bias, The Wisdom of Crowds, transaction costs, traveling salesman, value at risk, wealth creators, women in the workforce, zero-sum game
Certainly, there’s nothing in the horizon that would indicate that we will have rising interest rates for a minimum of three years.” 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.
Deductive rationality, a building block of neoclassical economics, breaks down in the real world because human logical reasoning can’t handle situations that are too complicated (i.e., 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. These latter, uncertain events include the magnitude and type of investment, as well as demand fluctuations.
I would add that, on the whole, a full ecology of strategies is sufficient to generate efficient markets. 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, Bill Joy: nanobots, Black Swan, carbon-based life, cognitive bias, complexity theory, computer age, coronavirus, corporate governance, cosmic microwave background, cosmological constant, cosmological principle, cuban missile crisis, dark matter, death of newspapers, demographic transition, Deng Xiaoping, distributed generation, Doomsday Clock, Drosophila, endogenous growth, Ernest Rutherford, failed state, feminist movement, framing effect, friendly AI, Georg Cantor, global pandemic, global village, Gödel, Escher, Bach, hindsight bias, Intergovernmental Panel on Climate Change (IPCC), invention of agriculture, Kevin Kelly, Kuiper Belt, Law of Accelerating Returns, life extension, means of production, meta analysis, meta-analysis, Mikhail Gorbachev, millennium bug, mutually assured destruction, nuclear winter, P = NP, peak oil, phenotype, planetary scale, Ponzi scheme, prediction markets, RAND corporation, Ray Kurzweil, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, Singularitarianism, social intelligence, South China Sea, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, Tunguska event, twin studies, uranium enrichment, Vernor Vinge, War on Poverty, Westphalian system, Y2K
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. Given the account as background knowledge, five groups of students were asked what they would have predicted as the probability for each of four outcomes: British victory, Gurkha victory, stalemate with a peace settlement, or stalemate with no peace settlement.
Instructions stated that the city was negligent if the foreseeable probability of flooding was greater than 10%. 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). There were warning signs of a problem with the 0-rings.
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. We learned not to allow hijacked planes to overfly our cities. We did not learn the lesson: 'Black Swans do occur; do what you can to prepare for the unanticipated.'
Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts
active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, business cycle, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, coherent worldview, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Laplace demon, Long Term Capital Management, loss aversion, medical malpractice, meta analysis, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, social intelligence, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize
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. 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). 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.
The Matthew Effect: How Advantage Begets Further Advantage. New York: Columbia University Press. Robbins, Jordan M., and Joachim I. Krueger. 2005. “Social Projection to Ingroups and Outgroups: A Review and Meta-analysis.” Personality and Social Psychology Review 9:32–47. Rogers, Everett M. 1995. Diffusion of Innovations, 4th ed. New York: Free Press. Roese, Neal J., and James M. Olson. 1996. “Counterfactuals, Causal Attributions, and the Hindsight Bias: A Conceptual Integration.” Journal of Experimental Social Psychology 32 (3):197–227. Rosen, Emmanuel. 2000. The Anatomy of Buzz: How to Create Word-of-Mouth Marketing. New York: Doubleday. Rosenbloom, Stephanie. 2009. “Retailers See Slowing Sales in Key Season.” New York Times, Aug. 15. Rosenzweig, Phil. 2007. The Halo Effect. New York: Free Press. Rothschild, David, and Justin Wolfers. 2008.
Big Mistakes: The Best Investors and Their Worst Investments by Michael Batnick
activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, asset allocation, bitcoin, Bretton Woods, buy and hold, buy low sell high, cognitive bias, cognitive dissonance, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, endowment effect, financial innovation, fixed income, hindsight bias, index fund, invention of the wheel, Isaac Newton, John Meriwether, Kickstarter, Long Term Capital Management, loss aversion, mega-rich, merger arbitrage, Myron Scholes, Paul Samuelson, quantitative easing, Renaissance Technologies, Richard Thaler, Robert Shiller, Robert Shiller, Snapchat, Stephen Hawking, Steve Jobs, Steve Wozniak, stocks for the long run, transcontinental railway, value at risk, Vanguard fund, Y Combinator
Players with undamaged brain wiring, however, were more cautious and reactive during the game, and wound up with less money at the end.3 Even if we were told that a loaded coin would land on heads 60% of the time, seeing four tails in a row would alter some people's decisions, despite knowing that they should bet on heads every single time. “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.”
There are errors of omission, Buffett and Munger not buying Walmart, and errors of commission, Stanley Druckenmiller buying tech stocks as they reached their peak in early 2000. 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. This makes intuitive sense. Others take a different approach and study stories of failure and try to avoid whatever it is that tripped that person or company up.
., 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.
Irrational Exuberance: With a New Preface by the Author by Robert J. Shiller
Andrei Shleifer, asset allocation, banking crisis, Benoit Mandelbrot, 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, 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, Ponzi scheme, price anchoring, random walk, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, Small Order Execution System, spice trade, statistical model, stocks for the long run, survivorship bias, the market place, Tobin tax, transaction costs, tulip mania, 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.
.), Knowledge and Cognition (Potomac, Md.: Lawrence Erlbaum Associates, 1975), pp. 29–41. 15. 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.
Misbehaving: The Making of Behavioral Economics by Richard H. Thaler
"Robert Solow", 3Com Palm IPO, Albert Einstein, Alvin Roth, Amazon Mechanical Turk, Andrei Shleifer, Apple's 1984 Super Bowl advert, Atul Gawande, Berlin Wall, Bernie Madoff, Black-Scholes formula, 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, 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, market clearing, Mason jar, mental accounting, meta analysis, meta-analysis, money market fund, More Guns, Less Crime, mortgage debt, Myron Scholes, Nash equilibrium, Nate Silver, New Journalism, nudge unit, Paul Samuelson, payday loans, Ponzi scheme, presumed consent, pre–internet, principal–agent problem, prisoner's dilemma, profit maximization, random walk, randomized controlled trial, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Coase, Silicon Valley, South Sea Bubble, Stanford marshmallow experiment, statistical model, Steve Jobs, Supply of New York City Cabdrivers, 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
As we drove, Fisch-hoff told me he had completed a PhD in psychology at the Hebrew University in Israel. 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. Baruch suggested that I might enjoy reading some of the work of his advisors. The next day, when I was back in my office in Rochester, I headed over to the library. Having spent all my time in the economics section, I found myself in a new part of the library.
Although this depiction is often apt, in many cases the real culprit is the boss, not the worker. 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. The misbehavior is in failing to create an environment in which employees feel that they can take good risks and not be punished if the risks fail to pay off.
Thinking, Fast and Slow by Daniel Kahneman
Albert Einstein, Atul Gawande, availability heuristic, Bayesian statistics, Black Swan, Cass Sunstein, Checklist Manifesto, choice architecture, cognitive bias, complexity theory, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, delayed gratification, demand response, endowment effect, experimental economics, experimental subject, Exxon Valdez, feminist movement, framing effect, hedonic treadmill, hindsight bias, index card, information asymmetry, job satisfaction, John von Neumann, Kenneth Arrow, libertarian paternalism, loss aversion, medical residency, mental accounting, meta analysis, meta-analysis, nudge unit, pattern recognition, Paul Samuelson, pre–internet, price anchoring, quantitative trading / quantitative ﬁnance, random walk, Richard Thaler, risk tolerance, Robert Metcalfe, Ronald Reagan, Shai Danziger, Supply of New York City Cabdrivers, The Chicago School, The Wisdom of Crowds, Thomas Bayes, transaction costs, union organizing, Walter Mischel, Yom Kippur War
This task turns out to be surprisingly difficult. 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? Might the United States grant diplomatic recognition to China? After decades of enmity, could the United States and the Soviet Union agree on anything significant?
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. The jury will be prone to believe, after the fact, that the operation was actually risky and that the doctor who ordered it should have known better.
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. George Tenet, director of the CIA, brought the information not to President George W. Bush but to National Security Adviser Condoleezza Rice.
Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Nicholas Taleb
Antoine Gombaud: Chevalier de Méré, availability heuristic, backtesting, Benoit Mandelbrot, Black Swan, commoditize, complexity theory, corporate governance, corporate raider, currency peg, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, endowment effect, equity premium, fixed income, global village, hedonic treadmill, hindsight bias, Kenneth Arrow, Long Term Capital Management, loss aversion, mandelbrot fractal, mental accounting, meta analysis, meta-analysis, Myron Scholes, Paul Samuelson, quantitative trading / quantitative ﬁnance, QWERTY keyboard, random walk, Richard Feynman, road to serfdom, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, Steven Pinker, stochastic process, survivorship bias, too big to fail, Turing test, Yogi Berra
If they were made for us to understand things, then we would have a machine in it that would run the past history as in a VCR, with a correct chronology, and it would slow us down so much that we would have trouble operating. 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. This is why events like those of September 11, 2001, never teach us that we live in a world where important events are not predictable—even the Twin Towers’ collapse appears to have been predictable then.
Another phenomenon: the transformation of the author by his own book. 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. Living in the real world may be painful particularly if one finds statements more informative about the people making them than the intended message: I picked up Newsweek this morning at the dentist’s office and read a journalist’s discussion of a prominent business figure, particularly his ability in “timing moves” and realized how I was making a list of the biases in the journalist’s mind rather than getting the intended information in the article itself, which I could not possibly take seriously.
For a long time we traders were totally ignorant of the behavioral research and saw situations where there was with strange regularity a wedge between the simple probabilistic reasoning and people’s perception of things. 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. This is the most significant distortion and the one that carries the most consequences.
Infotopia: How Many Minds Produce Knowledge by Cass R. Sunstein
affirmative action, Andrei Shleifer, availability heuristic, 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, 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, Robert Shiller, Ronald Reagan, slashdot, stem cell, The Wisdom of Crowds, winner-take-all economy
When asked what percentage of other people watch television on Saturday night, enjoy Bob Dylan, favor a particular political party, or believe that the latest Brad Pitt movie will win the Oscar, most of us show a bias in the direction that we ourselves favor. 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.
., “Bias in Judgment,” 691. 14. Ibid., 692 (citing studies). 15. Ibid. 16. 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.
The Personal MBA: A World-Class Business Education in a Single Volume by Josh Kaufman
Albert Einstein, Atul Gawande, Black Swan, business cycle, business process, buy low sell high, capital asset pricing model, Checklist Manifesto, cognitive bias, correlation does not imply causation, Credit Default Swap, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, David Ricardo: comparative advantage, Dean Kamen, delayed gratification, discounted cash flows, Donald Knuth, double entry bookkeeping, Douglas Hofstadter, en.wikipedia.org, Frederick Winslow Taylor, George Santayana, Gödel, Escher, Bach, high net worth, hindsight bias, index card, inventory management, iterative process, job satisfaction, Johann Wolfgang von Goethe, Kevin Kelly, Kickstarter, Lao Tzu, lateral thinking, loose coupling, loss aversion, Marc Andreessen, market bubble, Network effects, Parkinson's law, Paul Buchheit, Paul Graham, place-making, premature optimization, Ralph Waldo Emerson, rent control, side project, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, subscription business, telemarketer, the scientific method, time value of money, Toyota Production System, tulip mania, Upton Sinclair, Vilfredo Pareto, Walter Mischel, Y Combinator, Yogi Berra
Seeking disconfirming evidence will either show you the error of your ways or provide additional evidence for why your position is actually correct—as long as you suspend judgment long enough to learn from the experience. 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.” If the price of a particular stock you own drops 80 percent overnight, you “should have sold it.”
Don’t feel bad about things that you “should have seen” or “should have done.” 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. CONGRESSIONAL LIAISON FOR THE NATIONAL AERONAUTICS AND SPACE ADMINISTRATION (NASA) Being busy is better than being bored, but it’s possible to be too busy for your own good.
Garbage in, garbage out Gas tank, stress and recovery Gates, Bill Generosity, and reciprocation Goals and objectives determining, five-fold methods framing next action and priming throughput as measure Godin, Seth Golden trifecta Goldsmith, Marshall Goleman, Daniel Google Graham, Paul Grandchild rule Greene, Robert Grenny, Joseph Group interaction. 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.
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, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, attribution theory, availability heuristic, Branko Milanovic, Capital in the Twenty-First Century by Thomas Piketty, carried interest, Daniel Kahneman / Amos Tversky, David Brooks, deliberate practice, en.wikipedia.org, endowment effect, experimental subject, framing effect, full employment, 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, minimum wage unemployment, Network effects, 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 Wealth of Nations by Adam Smith, Tim Cook: Apple, ultimatum game, Vincenzo Peruggia: Mona Lisa, winner-take-all economy
I stress the possibility of effective government in the hope of encouraging skeptics to keep an open mind about my claim that we could easily bequeath a much better society to our children. 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 twist was that the study Lazarsfeld described was a complete fabrication. 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. If any of those earlier steps had been different, the entire trajectory would almost surely be different, too.
., 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.
Think Twice: Harnessing the Power of Counterintuition by Michael J. Mauboussin
affirmative action, asset allocation, Atul Gawande, availability heuristic, Benoit Mandelbrot, Bernie Madoff, Black Swan, butter production in bangladesh, Cass Sunstein, choice architecture, Clayton Christensen, cognitive dissonance, collateralized debt obligation, Daniel Kahneman / Amos Tversky, deliberate practice, disruptive innovation, Edward Thorp, experimental economics, financial innovation, framing effect, fundamental attribution error, Geoffrey West, Santa Fe Institute, George Akerlof, hindsight bias, hiring and firing, information asymmetry, libertarian paternalism, Long Term Capital Management, loose coupling, loss aversion, mandelbrot fractal, Menlo Park, meta analysis, meta-analysis, money market fund, Murray Gell-Mann, Netflix Prize, pattern recognition, Philip Mirowski, placebo effect, Ponzi scheme, prediction markets, presumed consent, Richard Thaler, Robert Shiller, Robert Shiller, statistical model, Steven Pinker, The Wisdom of Crowds, ultimatum game
After his unlikely ascent to the White House, Lincoln appointed a number of his eminent foes to cabinet positions. 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. I listened to the traffic report, heard both were clear, and picked Interstate 95. A few minutes later, we ran into traffic caused by an accident.
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. But just as it’s hard to make good decisions during emotional upheaval, it’s also hard to make good decisions in the absence of emotion.
Likely? I wouldn’t bet my money on it. 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. Put Yourself in the Shoes of Others. Considering the point of view or experience of other people is one of the most powerful ways to facilitate better decisions.
Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen
Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, G4S, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative ﬁnance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stocks for the long run, survivorship bias, systematic trading, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game
Thus it enables self-deception rather than accurate self-assessment. 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. Thus the inevitable disappointments that befall investors from time to time are seen, mistakenly, as having been avoidable.
However, I concede from the outset that the magic of view-based alpha generation cannot be conveyed in a book. • 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). McCulley (2010) has been one of Minsky’s main posthumous ambassadors.
The Confidence Game: The Psychology of the Con and Why We Fall for It Every Time by Maria Konnikova
attribution theory, Bernie Madoff, British Empire, Cass Sunstein, cognitive dissonance, coherent worldview, Daniel Kahneman / Amos Tversky, endowment effect, epigenetics, hindsight bias, lake wobegon effect, lateral thinking, libertarian paternalism, Milgram experiment, placebo effect, Ponzi scheme, post-work, publish or perish, Richard Thaler, risk tolerance, side project, Skype, Steven Pinker, the scientific method, tulip mania, Walter Mischel
Over time, the psychologists found, memory got even worse: fully 84 percent of those in the three-to-six-month group showed faulty recall. 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. Stetson’s show of indignity made it all the stronger: it activated the memory of their shared Masonic bond, of everything that came with fellowship and trust.
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. We see what we expect to see. As Princeton University psychologist Susan Fiske puts it, “Instead of a naïve scientist entering the environment in search of the truth, we find the rather unflattering picture of a charlatan trying to make the data come out in a manner most advantageous to his or her already-held theories.”
., ref1 Demara, Mary McNelly ref1, ref2 determinism, creeping ref1 Deveraux, Jude ref1 De Védrines, Christine ref1 De Védrines, Ghislaine ref1, ref2 “Diddling” (Poe), ref1 disasters ref1 disrupt-then-reframe ref1 Dittisham Lady, ref1, ref2 door-in-the-face ref1, ref2 Drake, Francis ref1, ref2, ref3, ref4, ref5 Dunbar, Robin ref1, ref2, ref3 Dunning, David ref1 Dutch tulip mania ref1 Dylan, Bob ref1 Ebola crisis ref1 Egan, Michael ref1 Eiffel Tower ref1 Ekman, Paul ref1, ref2, ref3 elaboration likelihood model ref1 elder fraud ref1 Elizabeth I, Queen ref1 Emler, Nicholas ref1, ref2 emotions ref1, ref2, ref3, ref4, ref5, ref6, ref7 anticipation of ref1 donations and ref1 stories and ref1, ref2, ref3, ref4, ref5, ref6 endowment effect ref1, ref2 entrapment effect ref1 environment ref1 Epley, Nicholas ref1, ref2, ref3 Epstein, Seymour ref1, ref2 Erdely, Sabrina Rubin ref1 Evans, Elizabeth Glendower ref1 even-a-penny scenario ref1, ref2 exceptionalism ref1, ref2, ref3, ref4 expectancies ref1, ref2 exposure ref1, ref2 Extraordinary Popular Delusions and the Madness of Crowds (Mackay), ref1 Eyal, Tal ref1 Facebook ref1, ref2, ref3, ref4, ref5, ref6, ref7 facial expressions ref1, ref2, ref3 Fallon, James ref1 familiarity ref1, ref2, ref3, ref4 Farms Not Factories ref1 FBI ref1, ref2, ref3 fear ref1 Feldman, Robert ref1 Fenimore, Karin ref1 Festinger, Leon ref1, ref2, ref3 Fetzer, Barbara ref1 Figes, Orlando ref1 Fischhoff, Baruch ref1, ref2 Fiske, Susan ref1 Fitzgerald, Alan and Eilis ref1 Fitzgerald, Elizabeth (Madame Zingara), ref1, ref2 fix ref1 Folt, Carol ref1 football ref1 foot-in-the-door ref1, ref2, ref3, ref4 Frampton, Anne-Marie ref1, ref2, ref3 Frampton, Paul ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9 Frank, Jerome ref1 Franklin, Benjamin ref1, ref2 Franklin Syndicate ref1, ref2, ref3, ref4 Fraser, Scott ref1 Freedman, Ann ref1, ref2, ref3, ref4, ref5, ref6, ref7 Freeman, Jonathan ref1 French, John ref1, ref2 Fund for the New American Century ref1 future ref1 predicting ref1, ref2, ref3, ref4 Galinsky, Adam ref1 gambler’s fallacy ref1, ref2 Gant, Robert ref1 Geis, Florence ref1 genetics ref1 Gerard, Harold ref1 Gerhartsreiter, Christian ref1 Gifford, Adam Lord ref1 Gilbert, Daniel ref1, ref2 Gilligan, Andrew ref1 Gilovich, Thomas ref1 Glass, Stephen ref1, ref2 Goetzinger, Charles ref1 Gondorf, Fred and Charles ref1 Goodrich, Judge ref1 Gordon, John Steel ref1 gorilla experiment ref1 gossip ref1, ref2, ref3 Goya, Francisco ref1 Grazioli, Stefano ref1 Great Imposter, The (Crichton), ref1, ref2, ref3 Green, Melanie ref1, ref2 Green Dot cards ref1 Greg ref1 grifter ref1 grooming ref1 groups, belonging to ref1 Guillotin, Joseph ref1 Gur, Ruben ref1 Gurney, Edmund ref1 Hancock, Jeffrey ref1 Hansen, Chris ref1 Hanson, Robert ref1 happiness ref1, ref2, ref3 Hare, Robert ref1 Harley, Richard ref1 Harlow, E. T., 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.
Rationality: From AI to Zombies by Eliezer Yudkowsky
Albert Einstein, Alfred Russel Wallace, anthropic principle, anti-pattern, anti-work, Arthur Eddington, artificial general intelligence, availability heuristic, Bayesian statistics, Berlin Wall, Build a better mousetrap, Cass Sunstein, cellular automata, cognitive bias, cognitive dissonance, correlation does not imply causation, cosmological constant, creative destruction, Daniel Kahneman / Amos Tversky, dematerialisation, different worldview, discovery of DNA, Douglas Hofstadter, Drosophila, effective altruism, experimental subject, Extropian, friendly AI, fundamental attribution error, Gödel, Escher, Bach, hindsight bias, index card, index fund, Isaac Newton, John Conway, John von Neumann, Long Term Capital Management, Louis Pasteur, mental accounting, meta analysis, meta-analysis, money market fund, Nash equilibrium, Necker cube, NP-complete, P = NP, pattern recognition, Paul Graham, 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, Solar eclipse in 1919, speech recognition, statistical model, Steven Pinker, strong AI, technological singularity, The Bell Curve by Richard Herrnstein and Charles Murray, the map is not the territory, the scientific method, Turing complete, Turing machine, ultimatum game, X Prize, Y Combinator, zero-sum game
This wouldn’t even require special-purpose code, just correct bookkeeping of the belief network. (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. You’ve got to predict for tomorrow, not yesterday.
The “Noticing Confusion” sequence asks why it’s useful to base one’s behavior on “rational” expectations, and what it feels like to do so. “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.
Buehler, Griffin, and Ross, “Inside the Planning Fallacy.” 4. Ian R. Newby-Clark et al., “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, Bernie Sanders, British Empire, Cass Sunstein, colonial rule, crony capitalism, desegregation, diversified portfolio, Donald Trump, Francis Fukuyama: the end of history, hindsight bias, illegal immigration, immigration reform, income inequality, old-boy network, race to the bottom, Republic of Letters, reserve currency, Ronald Coase, transaction costs, Washington Consensus, wealth creators
Rehabilitating James Buchanan President James Buchanan (1857–61), pompous and dithering, was wholly incapable of solving the secession crisis. 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. Buchanan delivered his fourth and last State of the Union address on December 3, 1860.
We forgive it, mostly, because Lincoln won the war and freed the slaves. 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? It’s not 1861 anymore. Back then, the southern secession ordinances proclaimed that the issue was slavery.17 And as Lincoln said in his second inaugural address, everyone knew that slavery was somehow the cause of the war.
Inside the Nudge Unit: How Small Changes Can Make a Big Difference by David Halpern
Affordable Care Act / Obamacare, availability heuristic, carbon footprint, Cass Sunstein, centre right, choice architecture, cognitive dissonance, collaborative consumption, correlation does not imply causation, Daniel Kahneman / Amos Tversky, different worldview, endowment effect, happiness index / gross national happiness, hedonic treadmill, hindsight bias, IKEA effect, illegal immigration, job satisfaction, Kickstarter, libertarian paternalism, light touch regulation, longitudinal study, market design, meta analysis, meta-analysis, Milgram experiment, nudge unit, peer-to-peer lending, pension reform, presumed consent, QR code, quantitative easing, randomized controlled trial, Richard Thaler, Right to Buy, Ronald Reagan, Rory Sutherland, Simon Kuznets, skunkworks, 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.
Smarter Than You Think: How Technology Is Changing Our Minds for the Better by Clive Thompson
4chan, A Declaration of the Independence of Cyberspace, augmented reality, barriers to entry, Benjamin Mako Hill, butterfly effect, citizen journalism, Claude Shannon: information theory, conceptual framework, corporate governance, crowdsourcing, Deng Xiaoping, discovery of penicillin, disruptive innovation, Douglas Engelbart, Douglas Engelbart, drone strike, Edward Glaeser, Edward Thorp, en.wikipedia.org, experimental subject, Filter Bubble, Freestyle chess, Galaxy Zoo, Google Earth, Google Glasses, Gunnar Myrdal, Henri Poincaré, hindsight bias, hive mind, Howard Rheingold, information retrieval, iterative process, jimmy wales, Kevin Kelly, Khan Academy, knowledge worker, lifelogging, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Netflix Prize, Nicholas Carr, Panopticon Jeremy Bentham, patent troll, pattern recognition, pre–internet, Richard Feynman, Ronald Coase, Ronald Reagan, Rubik’s Cube, sentiment analysis, Silicon Valley, Skype, Snapchat, Socratic dialogue, spaced repetition, superconnector, telepresence, telepresence robot, The Nature of the Firm, the scientific method, The Wisdom of Crowds, theory of mind, transaction costs, 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.
Giving the Devil His Due: Reflections of a Scientific Humanist by Michael Shermer
Alfred Russel Wallace, anthropic principle, anti-communist, barriers to entry, Berlin Wall, Boycotts of Israel, Chelsea Manning, clean water, clockwork universe, cognitive dissonance, Colonization of Mars, Columbine, cosmological constant, cosmological principle, creative destruction, dark matter, Donald Trump, Edward Snowden, Elon Musk, Flynn Effect, germ theory of disease, gun show loophole, Hans Rosling, hedonic treadmill, helicopter parent, hindsight bias, illegal immigration, income inequality, invisible hand, Johannes Kepler, Joseph Schumpeter, laissez-faire capitalism, Laplace demon, luminiferous ether, McMansion, means of production, mega-rich, Menlo Park, moral hazard, moral panic, More Guns, Less Crime, Peter Singer: altruism, phenotype, positional goods, race to the bottom, Richard Feynman, Ronald Coase, Silicon Valley, Skype, social intelligence, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, the scientific method, The Wealth of Nations by Adam Smith, 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).
If you wake up in the morning full of vim and vigor, bounding out the door and into the world to take your shot, you didn’t choose to be that way. 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.
I.O.U.: Why Everyone Owes Everyone and No One Can Pay by John Lanchester
asset-backed security, bank run, banking crisis, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black-Scholes formula, Blythe Masters, Celtic Tiger, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, diversified portfolio, double entry bookkeeping, Exxon Valdez, Fall of the Berlin Wall, financial deregulation, financial innovation, fixed income, George Akerlof, greed is good, hedonic treadmill, hindsight bias, housing crisis, Hyman Minsky, intangible asset, interest rate swap, invisible hand, Jane Jacobs, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Kickstarter, laissez-faire capitalism, light touch regulation, liquidity trap, Long Term Capital Management, loss aversion, 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, Own Your Own Home, Ponzi scheme, quantitative easing, reserve currency, Right to Buy, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, South Sea Bubble, statistical model, The Great Moderation, the payments system, too big to fail, tulip mania, 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.
The Hidden Half: How the World Conceals Its Secrets by Michael Blastland
air freight, Alfred Russel Wallace, banking crisis, Bayesian statistics, Berlin Wall, central bank independence, cognitive bias, complexity theory, Deng Xiaoping, Diane Coyle, Donald Trump, epigenetics, experimental subject, full employment, George Santayana, hindsight bias, income inequality, manufacturing employment, mass incarceration, meta analysis, 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. It’s called a framing problem, and it’s a catch-22. We don’t want to consider the potential effect of everything.
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
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, crowdsourcing, Dmitri Mendeleev, Elon Musk, Ethereum, Flynn Effect, Hernando de Soto, hindsight bias, hive mind, indoor plumbing, Isaac Newton, John von Neumann, libertarian paternalism, Mahatma Gandhi, Mark Zuckerberg, meta analysis, meta-analysis, obamacare, prediction markets, randomized controlled trial, Ray Kurzweil, Richard Feynman, Richard Thaler, Rodney Brooks, Rosa Parks, 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
When we know about something, we find it hard to imagine that someone else doesn’t know it. 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. In both cases, we fail to discern who knows what. Because we live inside a hive mind, relying heavily on others and the environment to store our knowledge, most of what is in our heads is quite superficial.
George Bernard Shaw quote: gutenberg.net.au/ebooks02/0200811h.html. curse of knowledge: C. Camerer, G. Loewenstein, and M. 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. SEVEN. THINKING WITH TECHNOLOGY commuting a little less: www.governing.com/topics/transportation-infrastructure/how-america-stopped-commuting.html.
The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing by Michael J. Mauboussin
Amazon Mechanical Turk, Atul Gawande, Benoit Mandelbrot, Black Swan, Checklist Manifesto, Clayton Christensen, cognitive bias, commoditize, Daniel Kahneman / Amos Tversky, David Brooks, deliberate practice, disruptive innovation, Emanuel Derman, fundamental attribution error, Gini coefficient, hindsight bias, hiring and firing, income inequality, Innovator's Dilemma, Long Term Capital Management, loss aversion, Menlo Park, mental accounting, moral hazard, Network effects, prisoner's dilemma, random walk, Richard Thaler, risk-adjusted returns, shareholder value, Simon Singh, six sigma, Steven Pinker, transaction costs, winner-take-all economy, zero-sum game, Zipf's Law
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. History is largely a narrative of cause and effect.
The Irrational Economist: Making Decisions in a Dangerous World by Erwann Michel-Kerjan, Paul Slovic
"Robert Solow", Andrei Shleifer, availability heuristic, bank run, Black Swan, business cycle, Cass Sunstein, clean water, cognitive dissonance, collateralized debt obligation, complexity theory, conceptual framework, corporate social responsibility, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-subsidies, Daniel Kahneman / Amos Tversky, endowment effect, experimental economics, financial innovation, Fractional reserve banking, George Akerlof, hindsight bias, incomplete markets, 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, Pareto efficiency, Paul Samuelson, placebo effect, price discrimination, price stability, RAND corporation, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, source of truth, statistical model, stochastic process, 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.
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, Donald Trump, Downton Abbey, full employment, game design, Gordon Gekko, hindsight bias, indoor plumbing, industrial cluster, information trail, invention of the printing press, invention of the telegraph, Jeff Bezos, John Snow's cholera map, Kodak vs Instagram, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Metcalfe’s law, Minecraft, Nate Silver, Network effects, Nicholas Carr, out of africa, randomized controlled trial, recommendation engine, Robert Gordon, Ronald Reagan, Silicon Valley, Skype, Snapchat, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, subscription business, telemarketer, the medium is the message, The Rise and Fall of American Growth, 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.
Pressed for evidence of her gift, she clarified: “Well, they [the breasts] can tell when it’s raining.” 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.’”
The Four Pillars of Investing: Lessons for Building a Winning Portfolio by William J. Bernstein
asset allocation, 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 independence, financial innovation, fixed income, George Santayana, German hyperinflation, high net worth, hindsight bias, Hyman Minsky, index fund, invention of the telegraph, Isaac Newton, John Harrison: Longitude, Long Term Capital Management, loss aversion, market bubble, mental accounting, money market fund, mortgage debt, new economy, pattern recognition, Paul Samuelson, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk/return, Robert Shiller, Robert Shiller, South Sea Bubble, stocks for the long run, stocks for the long term, survivorship bias, 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
Investors in tumultuous Germany, Japan, Argentina, and India were not so lucky; they obtained far smaller rewards. 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. The interest rate bottom of 4% reached in Rome is particularly relevant to the modern audience.
., 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.
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, 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. On the significance of agency, see Clark, Sleepwalkers, xxix. 20. In these notes, I have listed when possible multiple pieces of evidence to support any given citation, in order of significance for that particular citation: audio, video, or written materials from the original time period; later interviews and other autobiographical narratives; and the relevant secondary literature and/or journalism.
Bloch’s interpretation of this phenomenon appears to be the opposite of Tocqueville’s. 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.
Black Box Thinking: Why Most People Never Learn From Their Mistakes--But Some Do by Matthew Syed
Airbus A320, Alfred Russel Wallace, Arthur Eddington, Atul Gawande, Black Swan, British Empire, call centre, Captain Sullenberger Hudson, Checklist Manifesto, cognitive bias, cognitive dissonance, conceptual framework, corporate governance, creative destruction, credit crunch, crew resource management, deliberate practice, double helix, epigenetics, fear of failure, fundamental attribution error, Henri Poincaré, hindsight bias, Isaac Newton, iterative process, James Dyson, James Hargreaves, James Watt: steam engine, Johannes Kepler, Joseph Schumpeter, Kickstarter, Lean Startup, mandatory minimum, meta analysis, meta-analysis, minimum viable product, publication bias, quantitative easing, randomized controlled trial, selection bias, Shai Danziger, Silicon Valley, six sigma, spinning jenny, Steve Jobs, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, Toyota Production System, US Airways Flight 1549, Wall-E, Yom Kippur War
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.
*One issue that was never fully resolved with Libyan Arab Airlines Flight 114 is why, according to the pilot of one of the Israeli Phantoms, all the window shades were down. 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.
The Undoing Project: A Friendship That Changed Our Minds by Michael Lewis
Albert Einstein, availability heuristic, 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, loss aversion, medical residency, Menlo Park, Murray Gell-Mann, Nate Silver, New Journalism, Paul Samuelson, Richard Thaler, Saturday Night Live, Stanford marshmallow experiment, statistical model, the new new thing, Thomas Bayes, Walter Mischel, Yom Kippur War
To combat the endowment effect, he forced his scouts and his model to establish, going into the draft, the draft pick value of each of their own players. 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. “Every year we talk about what to take out and what to put in the model,” said Morey. “And every year it gets a little more depressing.”
They all believed that they had assigned higher probabilities to what happened than they actually had. 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.
A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing (Eleventh Edition) by Burton G. Malkiel
accounting loophole / creative accounting, Albert Einstein, asset allocation, asset-backed security, beat the dealer, Bernie Madoff, bitcoin, 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, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial innovation, financial repression, fixed income, framing effect, George Santayana, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Long Term Capital Management, loss aversion, margin call, market bubble, money market fund, mortgage tax deduction, new economy, Own Your Own Home, 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 tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, short selling, Silicon Valley, South Sea Bubble, stocks for the long run, survivorship bias, 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. They tend to rationalize bad outcomes as resulting from unusual external events.
., 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.
The Science of Fear: How the Culture of Fear Manipulates Your Brain by Daniel Gardner
Atul Gawande, availability heuristic, 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, mandatory minimum, medical residency, Mikhail Gorbachev, millennium bug, moral panic, mutually assured destruction, nuclear winter, placebo effect, Ralph Nader, RAND corporation, Ronald Reagan, social intelligence, Stephen Hawking, Steven Levy, Steven Pinker, the scientific method, 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.
Months after Nixon’s trip, he went back to each student and asked them about each event. 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. And when we look back? It looks so much more settled, so much more predictable.
Quality Investing: Owning the Best Companies for the Long Term by Torkell T. Eide, Lawrence A. Cunningham, Patrick Hargreaves
air freight, Albert Einstein, backtesting, barriers to entry, buy and hold, cashless society, cloud computing, commoditize, Credit Default Swap, discounted cash flows, discovery of penicillin, endowment effect, global pandemic, haute couture, hindsight bias, low cost airline, mass affluent, Network effects, oil shale / tar sands, pattern recognition, shareholder value, smart grid, sovereign wealth fund, supply-chain management
Such autopsies are most effective if they address a wide range of mistakes, realized and unrealized: for example by assessing both purchase and sale decisions that should, or should not, have been made. 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.
Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Bruce Schneier
23andMe, Airbnb, airport security, AltaVista, Anne Wojcicki, augmented reality, Benjamin Mako Hill, Black Swan, Boris Johnson, Brewster Kahle, Brian Krebs, call centre, Cass Sunstein, Chelsea Manning, citizen journalism, cloud computing, congestion charging, disintermediation, drone strike, Edward Snowden, experimental subject, failed state, fault tolerance, Ferguson, Missouri, Filter Bubble, Firefox, friendly fire, Google Chrome, Google Glasses, hindsight bias, informal economy, Internet Archive, Internet of things, Jacob Appelbaum, Jaron Lanier, John Markoff, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, linked data, Lyft, Mark Zuckerberg, moral panic, Nash equilibrium, Nate Silver, national security letter, Network effects, Occupy movement, Panopticon Jeremy Bentham, payday loans, pre–internet, price discrimination, profit motive, race to the bottom, RAND corporation, recommendation engine, RFID, Ross Ulbricht, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, smart cities, smart grid, Snapchat, social graph, software as a service, South China Sea, stealth mode startup, Steven Levy, Stuxnet, TaskRabbit, telemarketer, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, undersea cable, urban planning, WikiLeaks, 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.
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, demographic transition, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, hindsight bias, information asymmetry, job automation, job satisfaction, John Markoff, Just-in-time delivery, lone genius, Machinery of Freedom by David Friedman, market design, meta analysis, meta-analysis, Nash equilibrium, new economy, prediction markets, rent control, rent-seeking, reversible computing, risk tolerance, Silicon Valley, smart contracts, statistical model, stem cell, Thomas Malthus, trade route, Turing test, Vernor Vinge
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.
In such a simulation, almost everything else about the situation could be held constant. 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. In contrast, em spurs could directly demonstrate such biases in individuals, and not just in large groups.
100 Baggers: Stocks That Return 100-To-1 and How to Find Them by Christopher W Mayer
bank run, Bernie Madoff, business cycle, buy and hold, cloud computing, disintermediation, Dissolution of the Soviet Union, dumpster diving, Edward Thorp, hindsight bias, housing crisis, index fund, Jeff Bezos, market bubble, Network effects, new economy, oil shock, passive investing, peak oil, shareholder value, Silicon Valley, Stanford marshmallow experiment, Steve Jobs, survivorship bias, The Great Moderation, The Wisdom of Crowds
The Study: 365 Stocks That Turned $10,000 into $1+ Million Our study also turned up 365 stocks (by coincidence, the same number Phelps found in his study covering a different time period). 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. I shared the 100-bagger data with him. He was aghast. He related his concerns using a little story.
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, call centre, cellular automata, Cesare Marchetti: Marchetti’s constant, cognitive dissonance, computer vision, congestion charging, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, endowment effect, extreme commuting, fundamental attribution error, Google Earth, hedonic treadmill, hindsight bias, hive mind, 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, Sam Peltzman, Silicon Valley, statistical model, the built environment, The Death and Life of Great American Cities, traffic fines, ultimatum game, urban planning, urban sprawl, women in the workforce, working poor
They were certainly unintentional, but are “some crashes more unintentional than others”? 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).
Investigators learned: Associated Press, May 5, 2007. 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. This figure, taken from a National Safety Council study, comes from H.
Hedge Fund Market Wizards by Jack D. Schwager
asset-backed security, backtesting, banking crisis, barriers to entry, beat the dealer, Bernie Madoff, Black-Scholes formula, British Empire, business cycle, buy and hold, Claude Shannon: information theory, 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, Edward Thorp, family office, financial independence, fixed income, Flash crash, hindsight bias, implied volatility, index fund, intangible asset, James Dyson, Jones Act, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, money market fund, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, quantitative easing, quantitative trading / quantitative ﬁnance, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Rubik’s Cube, 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.
Although data mining techniques can uncover patterns in data that would be impossible for humans to find empirically or by prior hypotheses, it can also identify meaningless patterns that are nothing more than chance occurrences or the product of flaws in the analytical process. 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. Although segmenting the data to reserve unseen data for testing is a necessary condition to avoid misleading results, it is not a sufficient condition as Woodriff goes on to explain. 7In 2011, QIM changed the exact calculation it used to reduce leverage during periods of poor performance, but the new formulation was similar in both conceptual and practical terms.
What's Wrong with Economics? by Robert Skidelsky
"Robert Solow", additive manufacturing, agricultural Revolution, Black Swan, Bretton Woods, business cycle, 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, disruptive innovation, Donald Trump, 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, Mark Zuckerberg, market clearing, market friction, market fundamentalism, Martin Wolf, means of production, moral hazard, paradox of thrift, Pareto efficiency, Paul Samuelson, Philip Mirowski, precariat, price anchoring, principal–agent problem, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, shareholder value, Silicon Valley, Simon Kuznets, survivorship bias, technoutopianism, The Chicago School, The Market for Lemons, The Nature of the Firm, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Thorstein Veblen, transaction costs, transfer pricing, Vilfredo Pareto, Washington Consensus, Wolfgang Streeck, zero-sum game
Airplane crashes have happened because pilots trusted their faulty navigation systems rather than the evidence of their eyes. 6. 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. It was the same with Brexit and the election of Trump. An analogy in everyday life is when a seemingly happy couple suddenly split up.
How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester
asset allocation, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamonds, 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, financial innovation, Flash crash, forward guidance, Gini coefficient, global reserve currency, high net worth, High speed trading, hindsight bias, income inequality, inflation targeting, interest rate swap, Isaac Newton, Jaron Lanier, joint-stock company, joint-stock limited liability company, Kodak vs Instagram, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, negative equity, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, plutocrats, Plutocrats, Ponzi scheme, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, 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, trickle-down economics, 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. Some of this is benign, some less so. Example: a famous-to-economists finding in behavioral economics concerns pricing, and the fact that people have a provable bias towards the middle of three prices.
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, Asian financial crisis, asset-backed security, bank run, banking crisis, 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, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, global supply chain, hindsight bias, Hyman Minsky, Joseph Schumpeter, Kenneth Rogoff, lateral thinking, London Whale, Long Term Capital Management, market bubble, money market fund, moral hazard, Myron Scholes, Network effects, new economy, offshore financial centre, paradox of thrift, pets.com, Ponzi scheme, quantitative easing, Ralph Nader, Richard Thaler, risk tolerance, Ronald Reagan, Sam Peltzman, savings glut, technology bubble, The Great Moderation, too big to fail, transaction costs, union organizing, Unsafe at Any Speed, value at risk, William Langewiesche, zero-sum game
She passionately believes that the culture of how mistakes are dealt with is critical to safety. 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. Thus, every report that comes into ASRS is examined by at least two investigators, who are usually retired pilots or controllers.
SUPERHUBS: How the Financial Elite and Their Networks Rule Our World by Sandra Navidi
activist fund / activist shareholder / activist investor, assortative mating, bank run, barriers to entry, 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, diversification, East Village, Elon Musk, eurozone crisis, family office, financial repression, Gini coefficient, glass ceiling, Goldman Sachs: Vampire Squid, Google bus, Gordon Gekko, haute cuisine, high net worth, hindsight bias, income inequality, index fund, intangible asset, Jaron Lanier, John Meriwether, Kenneth Arrow, Kenneth Rogoff, knowledge economy, London Whale, Long Term Capital Management, longitudinal study, Mark Zuckerberg, mass immigration, McMansion, mittelstand, money market fund, Myron Scholes, NetJets, Network effects, offshore financial centre, old-boy network, Parag Khanna, Paul Samuelson, peer-to-peer, performance metric, Peter Thiel, plutocrats, Plutocrats, Ponzi scheme, quantitative easing, Renaissance Technologies, rent-seeking, reserve currency, risk tolerance, Robert Gordon, Robert Shiller, Robert Shiller, rolodex, Satyajit Das, shareholder value, Silicon Valley, social intelligence, sovereign wealth fund, Stephen Hawking, Steve Jobs, The Future of Employment, The Predators' Ball, The Rise and Fall of American Growth, too big to fail, women in the workforce, young professional
Therefore, in an attempt to create order in this world, we try to match specific events with specific causes. 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. That is not to say that there may never be an occasional—at least attempted—conspiracy, but they are more often the exception than the rule.
The Sense of Style: The Thinking Person's Guide to Writing in the 21st Century by Steven Pinker
butterfly effect, carbon footprint, crowdsourcing, Douglas Hofstadter, feminist movement, functional fixedness, hindsight bias, illegal immigration, index card, invention of the printing press, invention of the telephone, McMansion, meta analysis, meta-analysis, moral panic, Nelson Mandela, profit maximization, quantitative easing, race to the bottom, Ralph Waldo Emerson, Richard Feynman, short selling, Steven Pinker, the market place, theory of mind, Turing machine
The curse of knowledge is far more than a curiosity in economic theory. 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.
The Moral Landscape: How Science Can Determine Human Values by Sam Harris
Albert Einstein, banking crisis, Bayesian statistics, cognitive bias, end world poverty, endowment effect, energy security, experimental subject, framing effect, hindsight bias, impulse control, John Nash: game theory, longitudinal study, loss aversion, meta analysis, meta-analysis, out of africa, pattern recognition, placebo effect, Ponzi scheme, Richard Feynman, risk tolerance, scientific worldview, stem cell, Stephen Hawking, Steven Pinker, the scientific method, theory of mind, ultimatum game, World Values Survey
This result fits very well with our own, as the uncertainty provoked by our stimuli would have taken the form of “ambiguity” rather than “risk.” 36. 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. Stanovich & West, 2000. 38. Fong et al., 1986/07. Once again, asking whether something is rationally or morally normative is distinct from asking whether it has been evolutionarily adaptive.
A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing by Burton G. Malkiel
accounting loophole / creative accounting, Albert Einstein, asset allocation, asset-backed security, backtesting, beat the dealer, Bernie Madoff, 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 innovation, fixed income, framing effect, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Long Term Capital Management, loss aversion, margin call, market bubble, money market fund, mortgage tax deduction, new economy, Own Your Own Home, 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 tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, short selling, Silicon Valley, South Sea Bubble, stocks for the long run, 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. They tend to rationalize bad outcomes as resulting from unusual external events.
Anatomy of the Bear: Lessons From Wall Street's Four Great Bottoms by Russell Napier
Albert Einstein, asset allocation, banking crisis, Bretton Woods, business cycle, buy and hold, collective bargaining, Columbine, cuban missile crisis, desegregation, diversified portfolio, floating exchange rates, Fractional reserve banking, full employment, hindsight bias, Kickstarter, Long Term Capital Management, market bubble, mortgage tax deduction, Myron Scholes, new economy, oil shock, price stability, reserve currency, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, short selling, stocks for the long run, yield curve, Yogi Berra
If psychology is a soft science, then using financial history to assess human decision-making in times of uncertainty is softer still. 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. The boom in press coverage of the stock market dates from around the birth of the railway, when the emerging middle classes found investing in the new technology almost irresistible.
The Ascent of Money: A Financial History of the World by Niall Ferguson
Admiral Zheng, Andrei Shleifer, Asian financial crisis, asset allocation, asset-backed security, Atahualpa, bank run, banking crisis, banks create money, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, BRICs, British Empire, business cycle, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, 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, financial innovation, financial intermediation, fixed income, floating exchange rates, Fractional reserve banking, Francisco Pizarro, full employment, German hyperinflation, Hernando de Soto, high net worth, hindsight bias, Home mortgage interest deduction, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, iterative process, John Meriwether, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, labour mobility, Landlord’s Game, liberal capitalism, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, means of production, Mikhail Gorbachev, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, Naomi Klein, negative equity, Nelson Mandela, 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, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, seigniorage, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spice trade, stocks for the long run, structural adjustment programs, technology bubble, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Thorstein Veblen, too big to fail, transaction costs, undersea cable, value at risk, 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.
Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel
Albert Einstein, Atul Gawande, backtesting, beat the dealer, Bernie Madoff, Black Swan, buy and hold, buy low sell high, capital asset pricing model, 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, game design, hindsight bias, housing crisis, index fund, Isaac Newton, John Meriwether, John Nash: game theory, linear programming, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, mental accounting, money market fund, Myron Scholes, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, survivorship bias, systematic trading, 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. “Our risk machinery is designed to run away from tigers; it is not designed for the information-laden modern world.”
A Culture of Growth: The Origins of the Modern Economy by Joel Mokyr
"Robert Solow", Andrei Shleifer, barriers to entry, Berlin Wall, business cycle, clockwork universe, cognitive dissonance, Copley Medal, creative destruction, David Ricardo: comparative advantage, delayed gratification, deliberate practice, Deng Xiaoping, Edmond Halley, epigenetics, Fellow of the Royal Society, financial independence, framing effect, germ theory of disease, Haber-Bosch Process, 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, Ronald Reagan, South Sea Bubble, statistical model, survivorship bias, the market place, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, ultimatum game, World Values Survey, Wunderkammern
It is perhaps telling that although a considerable number of Chinese inventions and techniques found their way to the West in one form or another, there are comparatively fewer instances of Chinese propositional knowledge (not to mention science proper) being adopted in Europe. 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.
The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini
affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, 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, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, 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 skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative ﬁnance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sam Peltzman, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond
The examiner had documents from Lehman and from numerous third parties and government agencies, including the Department of the Treasury, the SEC, the Federal Reserve, FRBNY, the Office of Thrift Supervision, the SIPA Trustee, Ernst & Young, J.P. 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. This is a typical move for a player who has a card with a face value of 10 showing.