A Flaw in Human Judgment


By Daniel Kahneman

By Olivier Sibony

By Cass R. Sunstein

Formats and Prices




$40.00 CAD

This item is a preorder. Your payment method will be charged immediately, and the product is expected to ship on or around May 18, 2021. This date is subject to change due to shipping delays beyond our control.

From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones—"a tour de force” (New York Times). 

Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical.
In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions.
Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it.



Finding Noise

It is not acceptable for similar people, convicted of the same offense, to end up with dramatically different sentences—say, five years in jail for one and probation for another. And yet in many places, something like that happens. To be sure, the criminal justice system is pervaded by bias as well. But our focus in chapter 1 is on noise—and in particular, on what happened when a famous judge drew attention to it, found it scandalous, and launched a crusade that in a sense changed the world (but not enough). Our tale involves the United States, but we are confident that similar stories can be (and will be) told about many other nations. In some of those nations, the problem of noise is likely to be even worse than it is in the United States. We use the example of sentencing in part to show that noise can produce great unfairness.

Criminal sentencing has especially high drama, but we are also concerned with the private sector, where the stakes can be large, too. To illustrate the point, we turn in chapter 2 to a large insurance company. There, underwriters have the task of setting insurance premiums for potential clients, and claims adjusters must judge the value of claims. You might predict that these tasks would be simple and mechanical and that different professionals would come up with roughly the same amounts. We conducted a carefully designed experiment—a noise audit—to test that prediction. The results surprised us, but more importantly they astonished and dismayed the company’s leadership. As we learned, the sheer volume of noise is costing the company a great deal of money. We use this example to show that noise can produce large economic losses.

Both of these examples involve studies of a large number of people making a large number of judgments. But many important judgments are singular rather than repeated: how to handle an apparently unique business opportunity, whether to launch a whole new product, how to deal with a pandemic, whether to hire someone who just doesn’t meet the standard profile. Can noise be found in decisions about unique situations like these? It is tempting to think that it is absent there. After all, noise is unwanted variability, and how can you have variability with singular decisions? In chapter 3, we try to answer this question. The judgment that you make, even in a seemingly unique situation, is one in a cloud of possibilities. You will find a lot of noise there as well.

The theme that emerges from these three chapters can be summarized in one sentence, which will be a key theme of this book: wherever there is judgment, there is noise—and more of it than you think. Let’s start to find out how much.


Crime and Noisy Punishment

Suppose that someone has been convicted of a crime—shoplifting, possession of heroin, assault, or armed robbery. What is the sentence likely to be?

The answer should not depend on the particular judge to whom the case happens to be assigned, on whether it is hot or cold outside, or on whether a local sports team won the day before. It would be outrageous if three similar people, convicted of the same crime, received radically different penalties: probation for one, two years in jail for another, and ten years in jail for another. And yet that outrage can be found in many nations—not only in the distant past but also today.

All over the world, judges have long had a great deal of discretion in deciding on appropriate sentences. In many nations, experts have celebrated this discretion and have seen it as both just and humane. They have insisted that criminal sentences should be based on a host of factors involving not only the crime but also the defendant’s character and circumstances. Individualized tailoring was the order of the day. If judges were constrained by rules, criminals would be treated in a dehumanized way; they would not be seen as unique individuals entitled to draw attention to the details of their situation. The very idea of due process of law seemed, to many, to call for open-ended judicial discretion.

In the 1970s, the universal enthusiasm for judicial discretion started to collapse for one simple reason: startling evidence of noise. In 1973, a famous judge, Marvin Frankel, drew public attention to the problem. Before he became a judge, Frankel was a defender of freedom of speech and a passionate human rights advocate who helped found the Lawyers’ Committee for Human Rights (an organization now known as Human Rights First).

Frankel could be fierce. And with respect to noise in the criminal justice system, he was outraged. Here is how he describes his motivation:

If a federal bank robbery defendant was convicted, he or she could receive a maximum of 25 years. That meant anything from 0 to 25 years. And where the number was set, I soon realized, depended less on the case or the individual defendant than on the individual judge, i.e., on the views, predilections, and biases of the judge. So the same defendant in the same case could get widely different sentences depending on which judge got the case.

Frankel did not provide any kind of statistical analysis to support his argument. But he did offer a series of powerful anecdotes, showing unjustified disparities in the treatment of similar people. Two men, neither of whom had a criminal record, were convicted for cashing counterfeit checks in the amounts of $58.40 and $35.20, respectively. The first man was sentenced to fifteen years, the second to 30 days. For embezzlement actions that were similar to one another, one man was sentenced to 117 days in prison, while another was sentenced to 20 years. Pointing to numerous cases of this kind, Frankel deplored what he called the “almost wholly unchecked and sweeping powers” of federal judges, resulting in “arbitrary cruelties perpetrated daily,” which he deemed unacceptable in a “government of laws, not of men.”

Frankel called on Congress to end this “discrimination,” as he described those arbitrary cruelties. By that term, he mainly meant noise, in the form of inexplicable variations in sentencing. But he was also concerned about bias, in the form of racial and socioeconomic disparities. To combat both noise and bias, he urged that differences in treatment of criminal defendants should not be allowed unless the differences could be “justified by relevant tests capable of formulation and application with sufficient objectivity to ensure that the results will be more than the idiosyncratic ukases of particular officials, justices, or others.” (The term idiosyncratic ukases is a bit esoteric; by it, Frankel meant personal edicts.) Much more than that, Frankel argued for a reduction in noise through a “detailed profile or checklist of factors that would include, wherever possible, some form of numerical or other objective grading.”

Writing in the early 1970s, he did not go quite so far as to defend what he called “displacement of people by machines.” But startlingly, he came close. He believed that “the rule of law calls for a body of impersonal rules, applicable across the board, binding on judges as well as everyone else.” He explicitly argued for the use of “computers as an aid toward orderly thought in sentencing.” He also recommended the creation of a commission on sentencing.

Frankel’s book became one of the most influential in the entire history of criminal law—not only in the United States but also throughout the world. His work did suffer from a degree of informality. It was devastating but impressionistic. To test for the reality of noise, several people immediately followed up by exploring the level of noise in criminal sentencing.

An early large-scale study of this kind, chaired by Judge Frankel himself, took place in 1974. Fifty judges from various districts were asked to set sentences for defendants in hypothetical cases summarized in identical pre-sentence reports. The basic finding was that “absence of consensus was the norm” and that the variations across punishments were “astounding.” A heroin dealer could be incarcerated for one to ten years, depending on the judge. Punishments for a bank robber ranged from five to eighteen years in prison. The study found that in an extortion case, sentences varied from a whopping twenty years imprisonment and a $65,000 fine to a mere three years imprisonment and no fine. Most startling of all, in sixteen of twenty cases, there was no unanimity on whether any incarceration was appropriate.

This study was followed by a series of others, all of which found similarly shocking levels of noise. In 1977, for example, William Austin and Thomas Williams conducted a survey of forty-seven judges, asking them to respond to the same five cases, each involving low-level offenses. All the descriptions of the cases included summaries of the information used by judges in actual sentencing, such as the charge, the testimony, the previous criminal record (if any), social background, and evidence relating to character. The key finding was “substantial disparity.” In a case involving burglary, for example, the recommended sentences ranged from five years in prison to a mere thirty days (alongside a fine of $100). In a case involving possession of marijuana, some judges recommended prison terms; others recommended probation.

A much larger study, conducted in 1981, involved 208 federal judges who were exposed to the same sixteen hypothetical cases. Its central findings were stunning:

In only 3 of the 16 cases was there a unanimous agreement to impose a prison term. Even where most judges agreed that a prison term was appropriate, there was a substantial variation in the lengths of prison terms recommended. In one fraud case in which the mean prison term was 8.5 years, the longest term was life in prison. In another case the mean prison term was 1.1 years, yet the longest prison term recommended was 15 years.

As revealing as they are, these studies, which involve tightly controlled experiments, almost certainly understate the magnitude of noise in the real world of criminal justice. Real-life judges are exposed to far more information than what the study participants received in the carefully specified vignettes of these experiments. Some of this additional information is relevant, of course, but there is also ample evidence that irrelevant information, in the form of small and seemingly random factors, can produce major differences in outcomes. For example, judges have been found more likely to grant parole at the beginning of the day or after a food break than immediately before such a break. If judges are hungry, they are tougher.

A study of thousands of juvenile court decisions found that when the local football team loses a game on the weekend, the judges make harsher decisions on the Monday (and, to a lesser extent, for the rest of the week). Black defendants disproportionately bear the brunt of that increased harshness. A different study looked at 1.5 million judicial decisions over three decades and similarly found that judges are more severe on days that follow a loss by the local city’s football team than they are on days that follow a win.

A study of six million decisions made by judges in France over twelve years found that defendants are given more leniency on their birthday. (The defendant’s birthday, that is; we suspect that judges might be more lenient on their own birthdays as well, but as far as we know, that hypothesis has not been tested.) Even something as irrelevant as outside temperature can influence judges. A review of 207,000 immigration court decisions over four years found a significant effect of daily temperature variations: when it is hot outside, people are less likely to get asylum. If you are suffering political persecution in your home country and want asylum elsewhere, you should hope and maybe even pray that your hearing falls on a cool day.

Reducing Noise in Sentencing

In the 1970s, Frankel’s arguments, and the empirical findings supporting them, came to the attention of Edward M. Kennedy, brother of the slain president John F. Kennedy, and one of the most influential members of the US Senate. Kennedy was shocked and appalled. As early as 1975, he introduced sentencing reform legislation; it didn’t go anywhere. But Kennedy was relentless. Pointing to the evidence, he continued to press for the enactment of that legislation, year after year. In 1984, he succeeded. Responding to the evidence of unjustified variability, Congress enacted the Sentencing Reform Act of 1984.

The new law was intended to reduce noise in the system by reducing “the unfettered discretion the law confers on those judges and parole authorities responsible for imposing and implementing the sentences.” In particular, members of Congress referred to “unjustifiably wide” sentencing disparity, specifically citing findings that in the New York area, punishments for identical actual cases could range from three years to twenty years of imprisonment. Just as Judge Frankel had recommended, the law created the US Sentencing Commission, whose principal job was clear: to issue sentencing guidelines that were meant to be mandatory and that would establish a restricted range for criminal sentences.

In the following year, the commission established those guidelines, which were generally based on average sentences for similar crimes in an analysis of ten thousand actual cases. Supreme Court Justice Stephen Breyer, who was heavily involved in the process, defended the use of past practice by pointing to the intractable disagreement within the commission: “Why didn’t the Commission sit down and really go and rationalize this thing and not just take history? The short answer to that is: we couldn’t. We couldn’t because there are such good arguments all over the place pointing in opposite directions.… Try listing all the crimes that there are in rank order of punishable merit.… Then collect results from your friends and see if they all match. I will tell you they won’t.”

Under the guidelines, judges have to consider two factors to establish sentences: the crime and the defendant’s criminal history. Crimes are assigned one of forty-three “offense levels,” depending on their seriousness. The defendant’s criminal history refers principally to the number and severity of a defendant’s previous convictions. Once the crime and the criminal history are put together, the guidelines offer a relatively narrow range of sentencing, with the top of the range authorized to exceed the bottom by the greater of six months or 25%. Judges are permitted to depart from the range altogether by reference to what they see as aggravating or mitigating circumstances, but departures must be justified to an appellate court.

Even though the guidelines are mandatory, they are not entirely rigid. They do not go nearly as far as Judge Frankel wanted. They offer judges significant room to maneuver. Nonetheless, several studies, using a variety of methods and focused on a range of historical periods, reach the same conclusion: the guidelines cut the noise. More technically, they “reduced the net variation in sentence attributable to the happenstance of the identity of the sentencing judge.”

The most elaborate study came from the commission itself. It compared sentences in bank robbery, cocaine distribution, heroin distribution, and bank embezzlement cases in 1985 (before the guidelines went into effect) with the sentences imposed between January 19, 1989, and September 30, 1990. Offenders were matched with respect to the factors deemed relevant to sentencing under the guidelines. For every offense, variations across judges were much smaller in the later period, after the Sentencing Reform Act had been implemented.

According to another study, the expected difference in sentence length between judges was 17%, or 4.9 months, in 1986 and 1987. That number fell to 11%, or 3.9 months, between 1988 and 1993. An independent study covering different periods found similar success in reducing interjudge disparities, which were defined as the differences in average sentences among judges with similar caseloads.

Despite these findings, the guidelines ran into a firestorm of criticism. Some people, including many judges, thought that some sentences were too severe—a point about bias, not noise. For our purposes, a much more interesting objection, which came from numerous judges, was that guidelines were deeply unfair because they prohibited judges from taking adequate account of the particulars of the case. The price of reducing noise was to make decisions unacceptably mechanical. Yale law professor Kate Stith and federal judge José Cabranes wrote that “the need is not for blindness, but for insight, for equity,” which “can only occur in a judgment that takes account of the complexities of the individual case.”

This objection led to vigorous challenges to the guidelines, some of them based on law, others based on policy. Those challenges failed until, for technical reasons entirely unrelated to the debate summarized here, the Supreme Court struck the guidelines down in 2005. As a result of the court’s ruling, the guidelines became merely advisory. Notably, most federal judges were much happier after the Supreme Court decision. Seventy-five percent preferred the advisory regime, whereas just 3% thought the mandatory regime was better.

What have been the effects of changing the guidelines from mandatory to advisory? Harvard law professor Crystal Yang investigated this question, not with an experiment or a survey but with a massive data set of actual sentences, involving nearly four hundred thousand criminal defendants. Her central finding is that by multiple measures, interjudge disparities increased significantly after 2005. When the guidelines were mandatory, defendants who had been sentenced by a relatively harsh judge were sentenced to 2.8 months longer than if they had been sentenced by an average judge. When the guidelines became merely advisory, the disparity was doubled. Sounding much like Judge Frankel from forty years before, Yang writes that her “findings raise large equity concerns because the identity of the assigned sentencing judge contributes significantly to the disparate treatment of similar offenders convicted of similar crimes.”

After the guidelines became advisory, judges became more likely to base their sentencing decisions on their personal values. Mandatory guidelines reduce bias as well as noise. After the Supreme Court’s decision, there was a significant increase in the disparity between the sentences of African American defendants and white people convicted of the same crimes. At the same time, female judges became more likely than male judges were to exercise their increased discretion in favor of leniency. The same is true of judges appointed by Democratic presidents.

Three years after Frankel’s death in 2002, striking down the mandatory guidelines produced a return to something more like his nightmare: law without order.

The story of Judge Frankel’s fight for sentencing guidelines offers a glimpse of several of the key points we will cover in this book. First, judgment is difficult because the world is a complicated, uncertain place. This complexity is obvious in the judiciary and holds in most other situations requiring professional judgment. Broadly, these situations include judgments made by doctors, nurses, lawyers, engineers, teachers, architects, Hollywood executives, members of hiring committees, book publishers, corporate executives of all kinds, and managers of sports teams. Disagreement is unavoidable wherever judgment is involved.

Second, the extent of these disagreements is much greater than we expect. While few people object to the principle of judicial discretion, almost everyone disapproves of the magnitude of the disparities it produces. System noise, that is, unwanted variability in judgments that should ideally be identical, can create rampant injustice, high economic costs, and errors of many kinds.

Third, noise can be reduced. The approach advocated by Frankel and implemented by the US Sentencing Commission—rules and guidelines—is one of several approaches that successfully reduce noise. Other approaches are better suited to other types of judgment. Some methods adopted to reduce noise can simultaneously reduce bias as well.

Fourth, efforts at noise reduction often raise objections and run into serious difficulties. These issues must be addressed, too, or the fight against noise will fail.

Speaking of Noise in Sentencing

“Experiments show large disparities among judges in the sentences they recommend for identical cases. This variability cannot be fair. A defendant’s sentence should not depend on which judge the case happens to be assigned to.”

“Criminal sentences should not depend on the judge’s mood during the hearing, or on the outside temperature.”

“Guidelines are one way to address this issue. But many people don’t like them, because they limit judicial discretion, which might be necessary to ensure fairness and accuracy. After all, each case is unique, isn’t it?”


A Noisy System

Our initial encounter with noise, and what first triggered our interest in the topic, was not nearly so dramatic as a brush with the criminal justice system. Actually, the encounter was a kind of accident, involving an insurance company that had engaged the consulting firm with which two of us were affiliated.

Of course, the topic of insurance is not everyone’s cup of tea. But our findings show the magnitude of the problem of noise in a for-profit organization that stands to lose a lot from noisy decisions. Our experience with the insurance company helps explain why the problem is so often unseen and what might be done about it.

The insurance company’s executives were weighing the potential value of an effort to increase consistency—to reduce noise—in the judgments of people who made significant financial decisions on the firm’s behalf. Everyone agreed that consistency is desirable. Everyone also agreed that these judgments could never be entirely consistent, because they are informal and partly subjective. Some noise is inevitable.

Disagreement emerged when it came to its magnitude. The executives doubted that noise could be a substantial problem for their company. Much to their credit, however, they agreed to settle the question by a kind of simple experiment that we will call a noise audit. The result surprised them. It also turned out to be a perfect illustration of the problem of noise.

A Lottery That Creates Noise

Many professionals in any large company are authorized to make judgments that bind the company. For example, this insurance company employs numerous underwriters who quote premiums for financial risks, such as insuring a bank against losses due to fraud or rogue trading. It also employs many claims adjusters who forecast the cost of future claims and also negotiate with claimants if disputes arise.

Every large branch of the company has several qualified underwriters. When a quote is requested, anyone who happens to be available may be assigned to prepare it. In effect, the particular underwriter who will determine a quote is selected by a lottery.

The exact value of the quote has significant consequences for the company. A high premium is advantageous if the quote is accepted, but such a premium risks losing the business to a competitor. A low premium is more likely to be accepted, but it is less advantageous to the company. For any risk, there is a Goldilocks price that is just right—neither too high nor too low—and there is a good chance that the average judgment of a large group of professionals is not too far from this Goldilocks number. Prices that are higher or lower than this number are costly—this is how the variability of noisy judgments hurts the bottom line.

The job of claims adjusters also affects the finances of the company. For example, suppose that a claim is submitted on behalf of a worker (the claimant) who permanently lost the use of his right hand in an industrial accident. An adjuster is assigned to the claim—just as the underwriter was assigned, because she happens to be available. The adjuster gathers the facts of the case and provides an estimate of its ultimate cost to the company. The same adjuster then takes charge of negotiating with the claimant’s representative to ensure that the claimant receives the benefits promised in the policy while also protecting the company from making excessive payments.

The early estimate matters because it sets an implicit goal for the adjuster in future negotiations with the claimant. The insurance company is also legally obligated to reserve the predicted cost of each claim (i.e., to have enough cash to be able to pay it). Here again, there is a Goldilocks value from the perspective of the company. A settlement is not guaranteed, as there is an attorney for the claimant on the other side, who may choose to go to court if the offer is miserly. On the other hand, an overly generous reserve may allow the adjuster too much latitude to agree to frivolous demands. The adjuster’s judgment is consequential for the company—and even more consequential for the claimant.

We use the word lottery to emphasize the role of chance in the selection of one underwriter or adjuster. In the normal operation of the company, a single professional is assigned to a case, and no one can ever know what would have happened if another colleague had been selected instead.

Lotteries have their place, and they need not be unjust. Acceptable lotteries are used to allocate “goods,” like courses in some universities, or “bads,” like the draft in the military. They serve a purpose. But the judgment lotteries we talk about allocate nothing. They just produce uncertainty. Imagine an insurance company whose underwriters are noiseless and set the optimal premium, but a chance device then intervenes to modify the quote that the client actually sees. Evidently, there would be no justification for such a lottery. Neither is there any justification for a system in which the outcome depends on the identity of the person randomly chosen to make a professional judgment.

Noise Audits Reveal System Noise

The lottery that picks a particular judge to establish a criminal sentence or a single shooter to represent a team creates variability, but this variability remains unseen. A noise audit—like the one conducted on federal judges with respect to sentencing—is a way to reveal noise. In such an audit, the same case is evaluated by many individuals, and the variability of their responses is made visible.

The judgments of underwriters and claims adjusters lend themselves especially well to this exercise because their decisions are based on written information. To prepare for the noise audit, executives of the company constructed detailed descriptions of five representative cases for each group (underwriters and adjusters). Employees were asked to evaluate two or three cases each, working independently. They were not told that the purpose of the study was to examine the variability of their judgments.

Request Desk/Exam Copy


  • “The gold standard for a behavioral science book is to offer novel insights, rigorous evidence, engaging writing, and practical applications. It’s rare for a book to cover more than two of those bases, but Noise rounds all four—it’s a home run. Get ready for some of the world’s greatest minds to help you rethink how you evaluate people, make decisions, and solve problems.”—Adam Grant, author of Think Again and host of the TED podcast WorkLife
  • "Noise completes a trilogy that started with Thinking, Fast and Slow and Nudge. Together, they highlight what all leaders need to know to improve their own decisions, and more importantly, to improve decisions throughout their organizations. Noise reveals a critical lever for improving decisions, not captured in much of the existing behavioral economics literature. I encourage you to read Noise soon, before noise destroys more decisions in your organization."—Max H. Bazerman, author of Better, Not Perfect
  • “The influence of Noise should be seismic, as it explores a fundamental yet grossly underestimated peril of human judgment. Deepening its must-read status, it provides accessible methods for reducing the decisional menace.”—Robert Cialdini, author of Influence and Pre-Suasion
  • “Choices matter. Unfortunately, many of the choices people make are fundamentally flawed by the presence of noise, the subject of this absolutely fascinating and essential book. It is deeply researched, thoughtful, and accessible. I began it with a sense of intrigue and concluded it with a sense of celebration. We can make better choices in business, politics, and our personal lives. This book lights the way.”—Rita McGrath, author of Seeing Around Corners
  • "Brilliant! Noise goes deep on an under-appreciated source of error in human judgment: randomness. The story of noise has lacked the charisma of the story of cognitive bias…until now. Kahneman, Sibony, and Sunstein bring noise to life, making a compelling case for why we should take random variation in human judgment as seriously as we do bias and offering practical solutions for reducing noise (and bias) in judgment."
     —Annie Duke, author of Thinking in Bets
  • "Noise may be the most important book I've read in more than a decade. A genuinely new idea so exceedingly important you will immediately put it into practice. A masterpiece."—Angela Duckworth, author of Grit
  • "In Noise, the authors brilliantly apply their unique and novel insights into the flaws in human judgment to every sphere of human endeavor: from moneyball coaches to central bankers to military commanders to heads of state. Noise is a masterful achievement and a landmark in the field of psychology."—Philip E. Tetlock, coauthor of Superforecasting
  • “The earth has been so fully explored that scientists can’t possibly discover a previously unknown mammal the size of an elephant. The same could be said about the landscape of decision-making, yet Kahneman, Sibony, and Sunstein have discovered a problem as large as an elephant: noise. In this important book they show us why noise matters, why there’s so much more of it than we realize, and how to reduce it. Implementing their advice would give us more profitable businesses, healthier citizens, a fairer legal system, and happier lives.”—Jonathan Haidt, NYU Stern School of Business
  • "Noise is an absolutely brilliant investigation of a massive societal problem that has been hiding in plain sight."—Steven Levitt, coauthor of Freakonomics
  • "A tour de force of scholarship and clear writing."—New York Times
  • “Well-researched, convincing and practical book . . . written by the all-star team . . . The details and evidence will satisfy rigorous and demanding readers, as will the multiple viewpoints it offers on noise. Every academic, policymaker, leader and consultant ought to read this book. People with the power and persistence required to apply the insights in Noise will make more humane and fair decisions, save lives, and prevent time, money and talent from going to waste.”—Robert Sutton, Washington Post
  • "Convincing...A humbling lesson in inaccuracy."—Financial Times

On Sale
May 18, 2021
Page Count
464 pages
Little Brown Spark

Daniel Kahneman

About the Author

Daniel Kahneman is the Eugene Higgins Professor of Psychology, Princeton University, Professor of Public Affairs, Woodrow Wilson School of Public and International Affairs, and the winner of the 2002 Nobel Prize in Economic Sciences. Kahneman is a member of the American Academy of Arts and Sciences and the National Academy of Sciences. He is a fellow of the American Psychological Association, the American Psychological Society, the Society of Experimental Psychologists, and the Econometric Society.

​He has been the recipient of numerous awards, among them the Distinguished Scientific Contribution Award of the American Psychological Association, the Warren Medal of the Society of Experimental Psychologists, and Hilgard Award for Career Contributions to General Psychology, and the Award for Lifetime Contributions to Psychology from the American Psychological Association. He lives in New York City. He is the author of New York Times bestseller Thinking, Fast and Slow.

Olivier Sibony is a professor, writer and advisor specializing in the quality of strategic thinking and the design of decision processes. Sibony teaches Strategy, Decision Making and Problem Solving at HEC Paris. He is also an Associate Fellow of Saïd Business School in Oxford University. Sibony's research centers on improving the quality of decision making by reducing the impact of behavioral biases. He is the author of numerous articles in academic and popular publications, including Before You Make That Big Decision, co-authored with Nobel Prize winner Daniel Kahneman.

Cass R. Sunstein is currently the Robert Walmsley University Professor at Harvard. From 2009 to 2012, he was Administrator of the White House Office of Information and Regulatory Affairs. From 2013 to 2014, he served on President Obama's Review Group on Intelligence and Communications Technologies. From 2016 to 2017, he served on the Defense Innovation Board of the US Department of Defense. Sunstein is author of many articles and books, including two New York Times bestsellers: The World According to Star Wars and Nudge (with Richard H. Thaler). His other books include, Risk and Reason, Why Societies Need Dissent, The Second Bill of Rights, Conspiracy Theories and Other Dangerous Ideas, The Ethics of Influence, and Can It Happen Here? Authoritarianism in America. He lives in Cambridge, Massachusetts.


Learn more about this author

Olivier Sibony

About the Author

Olivier Sibony is an Affiliate Professor of Strategy at HEC Paris and an Associate Fellow at Saïd Business School, Oxford University. Previously, he spent 25 years in the Paris and New York offices of McKinsey & Company, where he was a senior partner. Sibony's research on improving the quality of strategic decision making has been featured in many publications, including Harvard Business Review and MIT Sloan Management Review. He is a graduate of HEC Paris and hold a PhD from Paris Sciences et Lettres University.

Learn more about this author