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William Collins (2021), 463 pages
Description
Discusses why people make bad judgments and how to make better ones by reducing the influence of "noise"--variables that can cause bias in decision making--and draws on examples in many fields, including medicine, law, economic forecasting, forensic science, strategy, and personnel selection.
User reviews
LibraryThing member DavidWineberg
The sheer variety of ways judgment can be clouded is mind-boggling. The more closely we examine judgments, the more noise turns up as a factor. In Noise, an A-list team of celebrity psych stars, Daniel Kahneman, Olivier Sibony and Cass Sunstein pull together their confrères and evidence from the
Noise, at least in psychology, is “unwanted variability”. In practical terms, that means even the most focused person might be swayed by unnoticed noise. Noise can be the home team losing the night before, lunch coming up in half an hour, miserable weather, a toothache – pretty much anything that has nothing to do with the issue at hand. This is all in addition to personal prejudices and the framework of bureaucratic rules that are always in play, restricting the range of possible decisions, and misdirecting them where they should not be going.
All kinds of studies show that trial judges are inconsistent when not totally wrong. The authors say two judges viewing the same evidence in the same case will come to two completely different decisions. So will the same judge given the same case on two different occasions. Sentencing is all over the place, which has led to enforced sentencing guidelines that often make things worse. It has also led to judge-shopping, as the decision patterns of judges builds up over the years. This is not based on evidence or argument, but in which way the judge’s decisions can be erroneous. Think political parties, religion, and stubborn pig-headedness.
The same goes for mere mortals, like supervisors. They all believe they do a creditable job, but the stats show the direct opposite. Even simple linear models do a far better job in every case. Not just sometimes – every time, according to Noise. Even randomly generated models do a far more accurate job of judging people correctly than people do. Artificial intelligence algorithms can also add a little more accuracy, though surprisingly, not significantly so. But people on their own perform miserably.
Still, no one, but no one, would trust a simple model to make a decision on their future; they feel better having personally tried with another human, regardless of the facts. It immediately reminded me of Lake Wobegon, where all the kids are above average. Doesn’t work like that. In the authors’ words, “Models of reality, of a judge or randomly generated models all perform better than nuanced, intuitive, insightful and experienced humans.” To which I would add: anyone who claims they can accurately size up a person on meeting them, can’t.
Errors occur far more frequently than people realize, because everyone trusts their own judgment foremost, and far too often, the judgment of others (their lawyers, doctors and managers, for example).
The worst example of this occurs in job interviews and performance appraisals. Everyone knows the single worst way to make a hire is through a personal, unstructured interview. Yet managers still insist on interviews, and so do candidates, thinking they can master the battle and win the job if they can simply deal with someone in person. Both are totally wrong, yet nonetheless, they both persist. Job interviews have become a nightmare for candidates, going back multiple times for essentially no good reason, as the more people interview them, the more inaccurate their ultimate decision will be.
As for quarterly, semi-annual and annual performance appraisals, those who have to work with the results know they are usually totally worthless. Managers burdened with multiple reports grind them out against a deadline, having little or nothing to do with an individual’s performance. Most everyone is “satisfactory”, especially when managers are required to rate them on a scale. No decisions can validly be taken from these exercises in frustration, but they are taken anyway. And while essentially no one in any organization likes or ever looks forward to the whole process, the noise persists, clouding futures.
Scales themselves are useless, as the authors show in examples such as for astronauts. A bell-curve distribution would show one or two excellent performers, one or two total failures, and most in the middle. But there are no total failures among astronauts. The yearslong training requires and ensures it. So grading on a scale against a bell-curve can be just more noise.
For the open-minded, Noise provides details, tips and tricks to leverage. For example, deliberation, the vaunted value of teams, actually increases the noise around a decision. The mere fact that team members discuss their reasoning before they make a decision increases the noise for everyone participating. The key to making teams work, ironically, is for everyone to do their own research in isolation, and once they have all come to a decision, they can then compare with others on the team.
They call this independent work “decision hygiene”. It cuts down noise in general, but no one can know what specifically, or by how much. The authors liken it to handwashing- no one knows what germs were there to kill. All they know is that handwashing kills germs, and that you can never get rid of all of them.
The authors show that noise occurs in almost any shape or form. The quality of the paper used for a business plan, and the font it is presented in, can tip the success or failure of a proposal in the hands of potential investors.
Another interesting noise source is called whitecoat syndrome. This is noise some people generate going to see a doctor, nurse or lab technician. Their blood pressure rises in anticipation, sometimes causing an erroneous diagnosis.
Things like prejudice are not so much noise as bias. When assessing decisions that go wrong, noise is the standard deviation of errors, while bias is the mean itself. The book is a thorough attempt to make a science of noise and errors in judgment.
Bias is a likely driver of noise. But the book is all about separating the two. It shows that biases, such as “planning fallacy, loss aversion, overconfidence, the endowment effect, status quo bias, excessive discounting of the future, and various biases against various categories of people” are all factors in erroneous decisions. But despite all this, sheer noise outweighs bias heavily.
They use Gaussian mean squared errors to demonstrate the effect of both bias and noise, with noise the clear winner, and dramatically so. Squaring the errors makes them visually arresting, But they still need to be stopped - somehow.
It transpires that errors do not cancel each other out, either. Instead, they add up, taking decisionmakers farther away from the right decision. And with the book piling on a seemingly infinite selection of noise factors and sources, it’s a wonder Man has made it even this far.
Speaking of erroneous judgments, it is difficult to decide what kind of book Noise is. It is steeped in psychology, but it is not a groundbreaking new discipline. People and firms have been actively trying to filter out noise since forever (the better ones, anyway). Nor is it a psych textbook, really, though there are exercises the reader can use right while poring over it. I think it is closer to a handbook of what to be aware of: forewarned is forearmed sort of thing. Though clearly, mere knowledge of the situation is far from enough to counteract it. The book includes how-tos like implementing an audit to identify and isolate noise, so the book definitely has practical applications. Handbook it is, then.
This noise thing is ego-deflating for all humans, who run their lives continually making decisions, not only on facts, but predictive judgments as well (Predictions provide an “illusion of validity”). That we are not equipped to pull this off successfully – at all – should cause a total rethink of where we go from here. Noise is pernicious. Trusting models looms heavily over us all.
David Wineberg
Show More
usual innumerable studies to delineate how bad it really is.Noise, at least in psychology, is “unwanted variability”. In practical terms, that means even the most focused person might be swayed by unnoticed noise. Noise can be the home team losing the night before, lunch coming up in half an hour, miserable weather, a toothache – pretty much anything that has nothing to do with the issue at hand. This is all in addition to personal prejudices and the framework of bureaucratic rules that are always in play, restricting the range of possible decisions, and misdirecting them where they should not be going.
All kinds of studies show that trial judges are inconsistent when not totally wrong. The authors say two judges viewing the same evidence in the same case will come to two completely different decisions. So will the same judge given the same case on two different occasions. Sentencing is all over the place, which has led to enforced sentencing guidelines that often make things worse. It has also led to judge-shopping, as the decision patterns of judges builds up over the years. This is not based on evidence or argument, but in which way the judge’s decisions can be erroneous. Think political parties, religion, and stubborn pig-headedness.
The same goes for mere mortals, like supervisors. They all believe they do a creditable job, but the stats show the direct opposite. Even simple linear models do a far better job in every case. Not just sometimes – every time, according to Noise. Even randomly generated models do a far more accurate job of judging people correctly than people do. Artificial intelligence algorithms can also add a little more accuracy, though surprisingly, not significantly so. But people on their own perform miserably.
Still, no one, but no one, would trust a simple model to make a decision on their future; they feel better having personally tried with another human, regardless of the facts. It immediately reminded me of Lake Wobegon, where all the kids are above average. Doesn’t work like that. In the authors’ words, “Models of reality, of a judge or randomly generated models all perform better than nuanced, intuitive, insightful and experienced humans.” To which I would add: anyone who claims they can accurately size up a person on meeting them, can’t.
Errors occur far more frequently than people realize, because everyone trusts their own judgment foremost, and far too often, the judgment of others (their lawyers, doctors and managers, for example).
The worst example of this occurs in job interviews and performance appraisals. Everyone knows the single worst way to make a hire is through a personal, unstructured interview. Yet managers still insist on interviews, and so do candidates, thinking they can master the battle and win the job if they can simply deal with someone in person. Both are totally wrong, yet nonetheless, they both persist. Job interviews have become a nightmare for candidates, going back multiple times for essentially no good reason, as the more people interview them, the more inaccurate their ultimate decision will be.
As for quarterly, semi-annual and annual performance appraisals, those who have to work with the results know they are usually totally worthless. Managers burdened with multiple reports grind them out against a deadline, having little or nothing to do with an individual’s performance. Most everyone is “satisfactory”, especially when managers are required to rate them on a scale. No decisions can validly be taken from these exercises in frustration, but they are taken anyway. And while essentially no one in any organization likes or ever looks forward to the whole process, the noise persists, clouding futures.
Scales themselves are useless, as the authors show in examples such as for astronauts. A bell-curve distribution would show one or two excellent performers, one or two total failures, and most in the middle. But there are no total failures among astronauts. The yearslong training requires and ensures it. So grading on a scale against a bell-curve can be just more noise.
For the open-minded, Noise provides details, tips and tricks to leverage. For example, deliberation, the vaunted value of teams, actually increases the noise around a decision. The mere fact that team members discuss their reasoning before they make a decision increases the noise for everyone participating. The key to making teams work, ironically, is for everyone to do their own research in isolation, and once they have all come to a decision, they can then compare with others on the team.
They call this independent work “decision hygiene”. It cuts down noise in general, but no one can know what specifically, or by how much. The authors liken it to handwashing- no one knows what germs were there to kill. All they know is that handwashing kills germs, and that you can never get rid of all of them.
The authors show that noise occurs in almost any shape or form. The quality of the paper used for a business plan, and the font it is presented in, can tip the success or failure of a proposal in the hands of potential investors.
Another interesting noise source is called whitecoat syndrome. This is noise some people generate going to see a doctor, nurse or lab technician. Their blood pressure rises in anticipation, sometimes causing an erroneous diagnosis.
Things like prejudice are not so much noise as bias. When assessing decisions that go wrong, noise is the standard deviation of errors, while bias is the mean itself. The book is a thorough attempt to make a science of noise and errors in judgment.
Bias is a likely driver of noise. But the book is all about separating the two. It shows that biases, such as “planning fallacy, loss aversion, overconfidence, the endowment effect, status quo bias, excessive discounting of the future, and various biases against various categories of people” are all factors in erroneous decisions. But despite all this, sheer noise outweighs bias heavily.
They use Gaussian mean squared errors to demonstrate the effect of both bias and noise, with noise the clear winner, and dramatically so. Squaring the errors makes them visually arresting, But they still need to be stopped - somehow.
It transpires that errors do not cancel each other out, either. Instead, they add up, taking decisionmakers farther away from the right decision. And with the book piling on a seemingly infinite selection of noise factors and sources, it’s a wonder Man has made it even this far.
Speaking of erroneous judgments, it is difficult to decide what kind of book Noise is. It is steeped in psychology, but it is not a groundbreaking new discipline. People and firms have been actively trying to filter out noise since forever (the better ones, anyway). Nor is it a psych textbook, really, though there are exercises the reader can use right while poring over it. I think it is closer to a handbook of what to be aware of: forewarned is forearmed sort of thing. Though clearly, mere knowledge of the situation is far from enough to counteract it. The book includes how-tos like implementing an audit to identify and isolate noise, so the book definitely has practical applications. Handbook it is, then.
This noise thing is ego-deflating for all humans, who run their lives continually making decisions, not only on facts, but predictive judgments as well (Predictions provide an “illusion of validity”). That we are not equipped to pull this off successfully – at all – should cause a total rethink of where we go from here. Noise is pernicious. Trusting models looms heavily over us all.
David Wineberg
Show Less
LibraryThing member waldhaus1
When similar decisions have different outcomes that is seen as noise. The interesting question is whether there is nose in the right answer or if it really makes sense to think in terms of a right answer. Heisenberg's uncertainty principle States there are limits to accuracy with which certain
Show More
physical properties of matter can be known. Perhaps both the uncertainty of the future and or incomplete knowledge of the present make noise in decisions inevitable. The book is very thought provoking and examines ways to improve the judgement process. Show Less
LibraryThing member Paul_S
The problem of noise is very much worth raising and this is a fair analysis of it but the way it's presented oversteps approachable straight into simplified and patronising. It's clearly written for the busy executive. It even has recaps and readymade soundbites you can repeat to your underlings
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and fellow board members at the end of each chapter. Show Less
LibraryThing member jpsnow
Noise is a masterful deconstruction of the components of error in our human judgment process, followed by plenty of practical advice about how to minimize it rationally. Among my favorite take-aways: a simple model often beats complex weightings (in a sense, because of noise), social perceptions
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can be substantially wrong when initial early signs cascade into bigger impacts, and one helpful solution is having a deliberate process that includes various ways of thinking and re-thinking. Show Less
LibraryThing member nbmars
The authors comprise a distinguished group: Kahneman is an Israeli psychologist and economist 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. Sibony is Professor of
The authors seek to illuminate the real nature of decision-making, focusing on the pervasive persistence of “noise” and how it affects what we believe and what we decide. What is this noise and what is its source? Noise, the authors state, “is the unwanted variability of judgments, and there is too much of it.” What motivated them to write the book, they explain in the conclusion, is “the sheer magnitude of system nose and the amount of damage it does.”
Noise come about from a looseness of standards that allow for unintended variables to affect outcomes. Think of judges for example, who can base punishment on a number of situations that are specified, but also have latitude. Thus, if the judge is in a bad mood, or tired, or subject to prejudices that might be unconscious, the judge is apt to rule differently than if those conditions do not apply. A similar situation obtains with doctors making diagnoses, with noise also coming from what array of symptoms they were, or were not, exposed to in medical school. The result of this noise is an array of decisions affected by random variations rather than fair, consistent, and predictable outcomes.
The authors suggest conducting a “noise audit” to check for variation in decisions across similar and even identical circumstances. They then suggest ways to combat this noise that is inevitably discovered.
One technique is to have multiple persons involved in reaching decisions. Another is to establish guidelines and constraints that limit intuition, idiosyncratic preferences, and cognitive biases. Exercises with decision makers evaluating decisions made by others can help expose noise and make those participating in the exercise more aware of it in their own behavior.
Overall, I found this book both moderately entertaining and ultimately depressing. With noise being not only bruited but valued by one half of American society, reason is increasingly taking a back seat. It doesn’t seem likely that “exercises” are likely to remedy the problem of massive misinformation and its astonishing influence.
Show More
Strategy at HEC Paris, an Associate Fellow of Saïd Business School in Oxford University, and has taught at London Business School, Ecole Polytechnique, ENA, IE Madrid, and other institutions. Sunstein is currently the Robert Walmsley University Professor at Harvard, and is the founder and director of the Program on Behavioral Economics and Public Policy at Harvard Law School. In 2018, he received the Holberg Prize from the government of Norway, sometimes described as the equivalent of the Nobel Prize for law and the humanities.The authors seek to illuminate the real nature of decision-making, focusing on the pervasive persistence of “noise” and how it affects what we believe and what we decide. What is this noise and what is its source? Noise, the authors state, “is the unwanted variability of judgments, and there is too much of it.” What motivated them to write the book, they explain in the conclusion, is “the sheer magnitude of system nose and the amount of damage it does.”
Noise come about from a looseness of standards that allow for unintended variables to affect outcomes. Think of judges for example, who can base punishment on a number of situations that are specified, but also have latitude. Thus, if the judge is in a bad mood, or tired, or subject to prejudices that might be unconscious, the judge is apt to rule differently than if those conditions do not apply. A similar situation obtains with doctors making diagnoses, with noise also coming from what array of symptoms they were, or were not, exposed to in medical school. The result of this noise is an array of decisions affected by random variations rather than fair, consistent, and predictable outcomes.
The authors suggest conducting a “noise audit” to check for variation in decisions across similar and even identical circumstances. They then suggest ways to combat this noise that is inevitably discovered.
One technique is to have multiple persons involved in reaching decisions. Another is to establish guidelines and constraints that limit intuition, idiosyncratic preferences, and cognitive biases. Exercises with decision makers evaluating decisions made by others can help expose noise and make those participating in the exercise more aware of it in their own behavior.
Overall, I found this book both moderately entertaining and ultimately depressing. With noise being not only bruited but valued by one half of American society, reason is increasingly taking a back seat. It doesn’t seem likely that “exercises” are likely to remedy the problem of massive misinformation and its astonishing influence.
Show Less
LibraryThing member BraveKelso
Prof. Kahneman's explanations of the flaws in decision making in Thinking Fast and Slow are popular and well-regarded. In this work, the authors explain the statistics of departures from the normal decisions make on certain facts. The variability of "judgment" by trusted experts - bail pending
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criminal trial, insurance premiums, medical diagnosis - has consequences. The authors correcty point out that variability conceals personal animosity, discriminary motives, personal quirks and faulty thought processes. They favour decisions based on rules to judgment. They flirt with automated decision making, challenging some of the critics of digital solutionism. Automated decision making fascinates big business for the potential of resolving problems without expensive and complicated human intervention. Show Less
LibraryThing member brianinbuffalo
As someone who enjoyed Kahneman’s “Thinking Fast and Slow” so much that I integrated some of the fascinating insights into my college-level communications course, I was eagerly looking forward to reading his new work. I was profoundly disappointed. One candid reviewer describes the book as a
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“rough slog.” The reviewer was being kind. In all honesty, I recall some sessions in my high school algebra class as more lively and engaging. It’s not like I didn’t give Kahneman’s latest work a genuine try. I read about a third of this dense work before calling it quits. I assign it 2 stars because I did glean several nuggets that shed light on decision-making and fuzzy judgements. Show Less
LibraryThing member boeintuy2
I actually liked this book. Useful framework for noise expressed as essentially variance components. Clearly and with great variety, established the significance of the subject. Offered ideas to address the concerns. Then with an openness uncharacteristic of books that propose solutions, sought to
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address the tradeoff and weakness with their own proposal. Not an attack on other ideas to be found. Show Less
LibraryThing member creighley
Gets very technical but it is interesting. Didn’t finish. The book discusses the MANY variables made in decision-making and it’s implications within our society. Need to finish!
LibraryThing member steve02476
Important stuff for people trying to understand decision-making flaws in large organizations. But overly long and a bit repetitive too. My main gripe is that it focused on problems for a handful of types of decision-makers, such a judges, doctors, insurers, Human Resources staff. Those are
I think it was interesting in the sections where they contrast noise (sorta-random mistakes in decision-making) and bias (more predictable mistakes).
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important people that do things that affect all of us, but it wasn’t made clear to me if the concepts in this book really apply to most people in every day situations.I think it was interesting in the sections where they contrast noise (sorta-random mistakes in decision-making) and bias (more predictable mistakes).
Show Less
LibraryThing member Devil_llama
This book has some merits, but being interesting isn't one of them. It is repetitive and filled with statistical discussions. I love, absolutely love, statistics, but there are ways to discuss them that isn't just plain boring. Also, some of the statistical data they presented seem to support their
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conclusion, but...and this is a big but...the effect was small enough that it likely didn't meet the criterion of being important. Significance isn't enough; is the difference big enough to cover the deviation and the overlap? And even if it is, does it matter? If I'd finished the book, perhaps they'd have convinced me it did, but I couldn't slog through any more of it, even though their major premise is accurate. The world does have a lot of noise in our judgement, causing one person to judge vastly different than another, and even the same person to vary depending on the environment. I'm not sure AI is the answer, though, even though they are enthusiastic. The biases that develop quickly in AI seem to make that a risky proposition. Overall, I don't recommend it. Show Less
Awards
Language
Original publication date
2021-05-18
Local notes
An interesting exploration of why people make bad judgments and how to make better ones.
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