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Noise: A Flaw in Human Judgment Kindle Edition
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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.
- LanguageEnglish
- PublisherLittle, Brown and Company
- Publication dateMay 18, 2021
- File size4250 KB
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Editorial Reviews
Review
"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 --This text refers to the hardcover edition.
About the Author
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 Republic.com, 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.
@casssunstein --This text refers to the hardcover edition.
Review
"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
"Compelling...A humbling lesson in inaccuracy."―Financial Times --This text refers to an alternate kindle_edition edition.
Product details
- ASIN : B08KQ2FKBX
- Publisher : Little, Brown and Company (May 18, 2021)
- Publication date : May 18, 2021
- Language : English
- File size : 4250 KB
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Enabled
- Word Wise : Enabled
- Sticky notes : On Kindle Scribe
- Print length : 465 pages
- Page numbers source ISBN : 0316451398
- Best Sellers Rank: #22,823 in Kindle Store (See Top 100 in Kindle Store)
- #11 in Sociology of Social Theory
- #12 in Business Decision-Making
- #13 in Cognitive Psychology (Kindle Store)
- Customer Reviews:
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About the authors

Daniel Kahneman (Hebrew: דניאל כהנמן, born March 5, 1934) is an Israeli-American psychologist notable for his work on the psychology of judgment and decision-making, as well as behavioral economics, for which he was awarded the 2002 Nobel Memorial Prize in Economic Sciences (shared with Vernon L. Smith). His empirical findings challenge the assumption of human rationality prevailing in modern economic theory. With Amos Tversky and others, Kahneman established a cognitive basis for common human errors that arise from heuristics and biases (Kahneman & Tversky, 1973; Kahneman, Slovic & Tversky, 1982; Tversky & Kahneman, 1974), and developed prospect theory (Kahneman & Tversky, 1979).
In 2011, he was named by Foreign Policy magazine to its list of top global thinkers. In the same year, his book Thinking, Fast and Slow, which summarizes much of his research, was published and became a best seller. He is professor emeritus of psychology and public affairs at Princeton University's Woodrow Wilson School. Kahneman is a founding partner of TGG Group, a business and philanthropy consulting company. He is married to Royal Society Fellow Anne Treisman.
In 2015 The Economist listed him as the seventh most influential economist in the world.
Bio from Wikipedia, the free encyclopedia. Photo by see page for author [Public domain], via Wikimedia Commons.

Cass R. Sunstein is the Robert Walmsley University Professor at Harvard Law School, where he is the founder and director of the Program on Behavioral Economics and Public Policy. He is by far the most cited law professor in the United States. From 2009 to 2012 he served in the Obama administration as Administrator of the White House Office of Information and Regulatory Affairs. He has testified before congressional committees, appeared on national television and radio shows, been involved in constitution-making and law reform activities in a number of nations, and written many articles and books, including Simpler: The Future of Government and Wiser: Getting Beyond Groupthink to Make Groups Smarter.

Olivier Sibony is a professor, writer and advisor specializing in the quality of strategic thinking and the design of decision processes. Olivier teaches Strategy, Decision Making and Problem Solving at HEC Paris. He is also an Associate Fellow of Saïd Business School in Oxford University.
Before he was a professor, Olivier spent 25 years with McKinsey & Company in France and in the U.S., where he was a Senior Partner. There, he was, at various times, a leader of the Global Strategy Practice and of the Consumer Goods & Retail Sector.
Olivier’s research interests focus on improving the quality of decision-making by reducing the impact of behavioral biases. He is the author of articles in various publications including “Before You Make That Big Decision”, co-authored with Nobel Prize winner Daniel Kahneman, which was selected as the cover feature of Harvard Business Review’s book selection of “10 Must-Reads on Making Smart Decisions”. In French, he also authored a book, Réapprendre à Décider.
Olivier builds on this research and on his experience to advise senior leaders on strategic and operational decision-making. He is a frequent keynote speaker and facilitator of senior management and supervisory board meetings. He also serves as a member of corporate, advisory and investment boards.
Olivier Sibony is a graduate of HEC Paris and holds a Ph. D. from Université Paris-Dauphine.
He lives in Paris.
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The first two thirds of the book set up the later chapters. The later chapters cover many specific techniques you can use to reduce noise and improve judgement and prediction. Don't jump right to them, though. The early part of the book lays the necessary groundwork for you to understand why the techniques work. Later I’ll identify chapters with particular value for business readers.
Noise is about two things that affect our judgment. Bias is systematic deviation. Noise is random scatter. We need to understand both to improve judgment. Alas, most of the time noise hardly gets any consideration while bias is the star of the show. The authors wrote this book to “redress the balance.”
They say that the key theme of the book is: “wherever there is judgment there is noise --and more of it than you think.”
The book is divided into six parts. Part one is about the difference between noise and bias. Part two is about human judgment. Part three is about predictive judgment. Part four describes the psychological causes of noise. Part five explores several practical issues. This is the part that would be of greatest interest to most business readers. Part six wraps up the book with techniques for measuring and overcoming noise.
There’s a lot of actionable value for businesspeople, but as I said earlier, you need to read the first two thirds of the book to get to it. Here are some things that may make the book worth your time.
This is an excellent overview of structured decision processes and why they often improve judgment. There are also specific chapters you may find interesting and helpful.
Chapter 23 is “Defining the Scale in Performance Ratings.” Some research indicates that performance only has a 20 percent impact on the final performance evaluation. This chapter includes techniques you can use to reduce both bias and noise and make your evaluations fairer and more consistent.
Chapter 24 is “Structure in Hiring.” Hiring almost always involves at least one interview. And interviewers make subjective judgments about the person they interview. We know that humans aren’t very good at sussing out whether a particular person will succeed or fail on the job. We know that different interviewers often have wildly varying assessments of the same candidate. This chapter will give you some tools for improving the results of your interviews.
Chapter 25 is “The Mediating Assessments Protocol.” This has special value for you if you are a maker of deals and subject to what has been called “deal heat.” The mediating assessment protocol is a tool for overcoming deal heat and making better decisions.
Chapter 28 is “Rules or Standards?” I never thought about the difference between these two until I read this chapter. You learn how rules and standards affect the amount of judgment in particular situations.
In a Nutshell
Noise is an excellent book about improving our judgment by reducing scattered results (noise) and reducing inconsistencies in the decision process. The first two thirds of the book establish the definitions and principles for dealing with noise. The final third of the book has several chapters with practical applications of the principles.
“Noise is the unwanted variability of judgments, and there is too much of it.” I personally think “static” would be the better descriptor but terminology is clearly an author’s prerogative. And there is no question that there is a lot of error in the judicial system, medical diagnosis, insurance underwriting, the education system, the recruitment, evaluation, and advancement of employees, forensic science, and just about everything to do with business and political decision-making.
But to think anyone can wrap all of that up with one neat bow and in doing so provide a potential roadmap to address them all is attempting to map the intricacies of the universe on the back of a cocktail napkin. You inevitably end up with so much clinical jargon (e.g., decision hygiene, mediating assessments protocol) as to make it all, well, outrageously noisy—and unnecessarily, if not painfully, long.
These three individuals are clearly brilliant and well schooled on the topic. Their academic peers may well give the book rave reviews. That’s okay. That’s the way judgment works.
As I am not a peer, however, I took exception with some of the assumptions. Judges are notoriously inconsistent in setting prison sentences. But setting a prison sentence is not the same as diagnosing breast cancer. One is clearly a judgment. The other seems better characterized as analytical.
Perhaps more importantly, setting prison sentences and medical diagnosis are largely judgments made by individuals. When judgments are made within an organization, however, as in deciding who gets promoted in a corporation, the individual is dwarfed by the organizational power structure that the authors never fully address. It is often power, not noise, which compromises the judgment of most organizations.
I believe, to be fair, that they would argue that power and noise are interrelated. Fair enough. It is the power, however, that defines the culture, the processes, and the judgments of most organizations. And if the power structure is faulty, which it often is, the reduction of noise won’t matter much and probably won’t get addressed anyway.
There is, to be fair, a lot of good common sense here. It is true that “correlation does not imply causation” and the “wisdom of crowds”, probabilistically anyway, is pretty well established. And I could not agree more that “even the most enthusiastic proponents of AI agree that algorithms are not, and will not soon be, a universal substitute for human judgment.”
In the last third of the book or so the authors acknowledge that some believe that more noise (i.e. fewer rules, guidelines, etc.), not less, would lead to more equitable and fair outcomes in many social systems (e.g., assigning prison sentences). They acknowledge that it ultimately depends on the process but they generally come down on the side of less noise.
If you put the issue in a broader human context, however, I don’t believe the case is convincing. If we lived in a society defined by collective identity and a sense of mutual obligation I would probably agree. But we don’t and in that sense the idea of reducing noise may be counter-intuitively untimely.
Sometimes the reduction of noise through rules or standards can actually increase inequity and reduce fairness. The US Tax Code is a case in point. The noise has, over decades, been reduced to a dull hum. We have the lengthiest and most complex tax code on the planet. But is it fairer as a result?
No. By reducing the noise we have given a distinct advantage to those with the wealth to hire armies of lawyers, accountants, and lobbyists to find and exploit the noise that’s left behind because one byproduct of rules and guidelines is loopholes.
The procedures for qualifying to vote and certifying elections are another example. More rules may provide further protection against fraud. But there is a very good chance it will also prohibit people with the legitimate right to vote from doing so or even compromising the very ideal of democracy. Which is the worse outcome?
Most social systems, in other words, exist in a larger context. If there exists a strong sense of collective social identity and mutual obligation reducing the noise in most social systems might make sense. That, however, is not the world we currently live in. And until that context changes, more rules and guidelines are as likely to result in less equity rather than more.
I cannot summarize a big book in a few words, but they show that we are having a lot of noise in our decissions) and that reducing this noise (random instabilities in our ways of making decisions) will reduce mistaked that we make considerably. I think that anyone who reads this book should also read Gary Kleins' "Seeing what other people don't", and learn more about the Kaizen methods, to see why noise has also a strong positive value. The authors mention "collective ignorance" but I don't think that they fully realise how much it is a major player, and reducing noise makes here things worse. They do mention things as a problem but as if it is a minor issue. Still - if you set aside their goal and just try to learn new things, this is a very good book.
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At the same time, they bring into the discussion some serious tools you won’t even meet until you get to graduate school in statistics, like the “percentage concordant,” which is not some type of supersonic airplane, but a rank correlation type of measure, and even provide a mini-table to move you from percentage concordant (PC) to correlation. The table, by the way, is bogus in the absence of context, as percentage concordant is a construct that I’m willing to bet relies heavily on assumptions that go unmentioned here.
The chapters end with summaries, which was OK for Thinking Fast and Slow, but a bit of an insult when the subject matter is so plain.
The style is pompous and paternalistic.
System A and System B are parachuted in, but (i) they’re barely explained (ii) that’s a theory to explain bias rather than noise (and invite a celebrity author to the proceedings)
Most annoyingly, terribly little ground is covered in this weighty tome. Gun to my head, I could probably get it all down to one page. Let me try:
1. Noise is just as bad as bias in terms of messing up your results
2. A good way to measure how bad your results are is the mean square error
3. Composition of Mean Square Error:
• Mean square error is made up of Bias and Noise
• Noise is made up of Level Noise and Pattern Noise
• Pattern Noise is made up of Stable Pattern Noise and Occasion Noise
• Level Noise is the kind of noise that comes from the fact that some judges are harsh and some are lenient, so two guys who did the same crime could get very different punishment.
• Pattern Noise is the kind of noise that comes from the fact that a judge may have a daughter, making him less harsh on young women that remind him of his daughter. He could be a harsh judge who is less harsh on young women who remind him of his daughter; or he could be a lenient judge who is extra lenient on young women who remind him of his daughter.
• Occasion Noise is the kind of noise that comes from the fact that judges are harsher right before lunch. Same judge, same crime, same perpetrator, different outcome, because it was a different occasion
4. If you ask people to measure something independently from one another, the more the merrier; but if they talk to each other first, then they will amplify errors for a variety of reasons that lead to groupthink
5. Machines beat people when it comes to cutting noise
6. In the quest to limit noise, people can fight back by sticking to simple rules
7. We humans like to build stories after the fact to explain what happened; they’re usually bogus: statistical explanations beat causal explanations
8. Bias can be the source of noise: inconsistency in bias is noise
9. Noise can arise when you’re told to rank things on a scale; to cut noise, it’s better to go ordinal than cardinal
10. To improve judgements you need (i) better judges (ii) a decision process that aggregates in a way that maintains independence among the judges (iii) guidelines (iv) relative rather than absolute judgements
11. There is a place for intuition: it’s got to be brought in at the very end, after all the mechanical work has finished
12. There actually is a place for noise: when people are bound to game the system
Read something else!
And so to Noise, a book, we are told that is designed to offer suggestions for the improvement of human judgement. As for Noise itself we are told in the book that that noise is about statistical thinking. We are also told that noise is a distinct source of error and that "the scatter in the forecasts is noise" and, that whenever we observe noise we should work to reduce it. However, we are also told that noise is invisible and embarrassing.
Noise occurs because people are idiosyncratic; they inhabit different psychological spaces; their moods are triggered by a unique set of contexts - they see and respond to the evidence in different ways. Not to mention their unconscious response to particular cues. (In many respects - seemingly the same things that trigger biases, and we are told rather confusingly that "psychological biases create system noise when many people differ in their biases.") We enter a convoluted vortex - biases cause noise - where there is noise (invisible) there will surely also be more biases at work - the two, it seems, exist in relationship that is characterised by their mutual and continuous interruption of each other. And there is actually no clear sense given as to how one should go about unpicking them.
Surprise surprise the authors pay passing homage to prediction markets, of which they say; "much of the time prediction markets have been found to do very well.") Prediction markets, in the wild (outisde of organisations) have not actually performed very well at all - because they lack insiders and do nothing more than aggregate noise. Their record on political events over the past ten years has been terrible (In the recent Chesham and Amersham By-Election in the UK, for example, the Tories were trading at 1.17 on the Betfair Betting Exchange as Polls opened - they lost). A better example, in the context of noise would have been horse racing betting markets - which contain lots of noise and bias, but which display a consistent ability to be predictive - because of the presence of insiders, who cancel out the noise.
Sadly it seems that we have gone back twenty years, to the notion of the jar of sweets and the benefits of aggregating independent judgements. In a nutshell, this book is about 380 pages too long.
‘Noise’ here, is about – well, noise, in the decision-making process and the decisions themselves. Noise is the diversity of decisions or conclusions on the same question. By way of a simple illustration, if we have a case in which a 40-year-old man, with a family of five, is convicted of stealing a loaf of bread, and two judges are asked to decide the sentence on him, one says jail for a day and the other says jail for a month. The different outcomes are the noise that conceals the correct judgment. One of them must be wrong.
The authors show why understanding noise is important. In their discussions, they question the utility and differences between rules and standards and how they might be applied to reduce noise in decision-making. They pose the question: ‘Who counts as disabled, such that they should qualify for economic benefits reserved for those who are unable to work?’ The authors maintain that if the question is phrased in this way, ‘judges will make ad hoc decisions that will be noisy and unfair’. They show how standards and rules approach may often result in less noisy, and fairer judgments. ‘If doctors are given clear guidelines to decide whether patients have strep throat, their decisions might be fast and relatively straightforward.’
This book is a primer for all decision makers, not only in the professional fields, but also in administrative work, and even all of us making decisions in the domestic settings. But, if leaders of domestic organisations use algorithms either to replace human judgment or supplement it, would that be desirable? Are we – should we – be prepared to displace discretion for rules? That, perhaps is a matter of distinguishing two different situations. The first is one where the facts are the same. The second is where the facts are not uniform. Even if we were to disagree with some of the claims of the authors, this is a book that will stimulate the mind of every decision maker.











