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Noise: A Flaw in Human Judgment por Daniel…
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Noise: A Flaw in Human Judgment (original 2021; edição 2021)

por Daniel Kahneman (Autor), Olivier Sibony (Autor), Cass R. Sunstein (Autor)

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Membro:Paul_S
Título:Noise: A Flaw in Human Judgment
Autores:Daniel Kahneman (Autor)
Outros autores:Olivier Sibony (Autor), Cass R. Sunstein (Autor)
Informação:Little, Brown Spark (2021), 464 pages
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Noise: A Flaw in Human Judgment por Daniel Kahneman (2021)

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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 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. ( )
  jpsnow | Aug 26, 2021 |
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 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. ( )
1 vote waldhaus1 | Jul 29, 2021 |
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 and fellow board members at the end of each chapter. ( )
  Paul_S | Jul 24, 2021 |
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 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 ( )
5 vote DavidWineberg | Apr 14, 2021 |
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Kahneman, Danielautor principaltodas as ediçõesconfirmado
Sibony, Olivierautor principaltodas as ediçõesconfirmado
Sunstein, Cass R.autor principaltodas as ediçõesconfirmado
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