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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.
"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.
- 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
- Print length : 521 pages
- Lending : Not Enabled
- Best Sellers Rank: #21,876 in Kindle Store (See Top 100 in Kindle Store)
- Customer Reviews:
About the authors
Reviewed in the United States on May 19, 2021
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Top reviews from the United States
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The basic premise seems to be that decisions have noise in them (duh) and its important to understand that we should evaluate the decision making process and not just the outcome. Accuracy, Precision, and Bias are terms familiar to anyone with a basic understanding of statistics; for others, a couple of early examples focusing on shooting targets easily educates the three terms and their differences. The authors keep on stating the same concepts in a number of ways for the first 5-6 chapters. And very often, simple observations are turned to very dense phrases without really serving any purpose than trying to sound very academic or scholarly. (For example, "..what they are trying to achieve is, regardless of verifiability, is the internal signal of completion provided by the coherence between the facts of the case and the judgement. And what they should be trying to achieve...is the judgement process that would provide the best judgement over an ensemble of similar cases") . Then the authors spend a chapter or two differentiating "predictive" and "evaluative" judgements only to conclude that the difference is "fuzzy" (genius observation) and a decision will usually require both.
If you are able to grind your way through the first 3 Parts (12 chapters), you will be able to pick up some new insights in Part IV and V that discuss on how variability/noise occurs and their various sources. Conducting a "noise audit" and what constitutes decision "hygiene" are sections worth reading for those whose roles require constant synthesis of inputs from various experts/sources/stakeholders etc.
Overall, the unnecessarily dense style that overcomplicates a simple message, lack of a clear target audience, and a narrative arc that just takes too long to provide new insights or provocative thoughts, makes this a fairly dull read.
Consider that the following studies listed in the Notes to the Introduction all used p-values:
(2) Child Protection and Child Outcomes: Measuring the Effects of Foster Care
(4) Refugee Roulette: Disparities in Asylum Adjudication
In Chapter 1:
(14) A Survey(!!!) of 47 Judges (dated 1977) (Survey vs. Random Control Study)
(16) Extraneous Factors in Judicial Decisions cites a p-value <.0001 on page 5
... and similar p-value references associated with judges' differential and variance in sentencing: related to food breaks, nearby NFL Team winning recently, birthdays, outside air temperature. IMHO, the identification of these explanatory factors based on p-values are bogus and illustrative of John Ioannidis' 2005 paper: Why Most Published Research Findings Are False.
It is disconcerting that these scholar authors utilize many questionable references to architect a thesis about what is more commonly known as variance. As the normal Gaussian distribution is ubiquitous, one should not be startled that selected ranges within it vary significantly.
Given the presence of uncertainty and the idiosyncracy and variability of individual experience, human judgments will vary. Human judgment is noisy! DUH !!!
The authors have failed their scholarship and profession.
Top reviews from other countries
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.
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!
Yet the result is disappointing. The household name sold us a book that, as a stand alone publication, would be a likely (or very likely) failure.
The book is far too long; arguments are repeated from Thinking, Fast and Slow; fresh evidence/new ideas are far and few.
Very sad. Hope this triumviratus will NOT write any more books.