Amazon.ca:Customer reviews: Noise: A Flaw in Human Judgment

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Useful ideas

Reviewed in Canada on August 30, 2021

The book is easy to read in some places, rather tedious in others. It gives an original treatment of a little understood subject. I was pleasantly surprised at how much I was able to learn about an important subject.

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Asad Ansari

The book has been oversold

Reviewed in Canada on May 24, 2021

I am not sure if I would have bought the book if Daniel Kahneman was not listed on the cover.

7 people found this helpful

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Asad Ansari

The book has been oversold

Reviewed in Canada on May 24, 2021

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I am not sure if I would have bought the book if Daniel Kahneman was not listed on the cover.

7 people found this helpful

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Amazon Customer

Too much stats focus for too long

Reviewed in Canada on July 14, 2021

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Like many other, I find this book very difficult to read because first half of it is pretty much all stats. The entire book can also be a lot more succinct than what it is. Secondly, the book I received has printing error, it ends on page 374 (see picture) and starts from 327-374 again, without going beyond 374. I know there's more content after 374. So I'm missing pages for sure. I appreciate the idea the authors have raised, thus the two star.

One person found this helpful

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Jeff Hillier

Good Ideas Repeated Over and Over Again

Reviewed in Canada on June 14, 2021

Some interesting concepts BUT too much repetition and so much seemed to be common sense for any leader who practices stepping back and getting other perspectives. This book should be 1/2 the length.

4 people found this helpful

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Iron Paradise Fitness

One chapter of content dragged out over 379 pages

Reviewed in the United Kingdom on May 23, 2021

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I desperately wanted to like this book, but every page turn felt like dull slap in the face. Humans make poor, inconsistent decisions and are easily swayed. The end. Save yourself £16 and move on with your life.

80 people found this helpful

Niall O'Connor

This Book Is Noisy

Reviewed in the United Kingdom on June 1, 2021

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For many Kahneman is a God: and one that was awarded the Nobel Memorial Prize in Economic Sciences for his groundbreaking work in applying psychological insights to economic theory, particularly in the areas of judgment and decision-making under uncertainty. Thinking, Fast and Slow was the book that everybody had to have but, as was the case with Hawking's A Brief History of Time, one suspects that many copies went unread. Kahneman himself, in a move akin to God saying that he had difficulty sticking to the Ten Commandments said in a recent interview that "my own experience of how little this knowledge has changed the quality of my own judgement can be sobering." There were two problems with Thinking, Fast and Slow - firstly, the transition from fast to slow was unquantifiable and second, it seemed to propose an ability on the part of the average punter to tap into their unconscious. Moreover, many of those that read the book were great at spotting biases in other people rather than in themselves.

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.

51 people found this helpful

Brendan O'Sullivan

References packed with P-values! REALLY???

Reviewed in the United States on May 19, 2021

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If one would expect the authors to have built their Noise (a.k.a. variance) thesis using research and references which attended to the cautions and caveats of the American Statistical Association (2016) regarding p-values and the now deprecated "statistical significance," one will be most disappointed. One should not be surprised as many of the references illustrating their thesis are in some cases a half-century old, when "statistical significance" was the key to getting published and the core of degree-earning dissertations.

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.

515 people found this helpful

Athan

if you hear any noise... it ain't the content

Reviewed in the United Kingdom on October 22, 2021

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I really have no idea who the intended audience was for this book: the authors really, really dumb it down, to the point of explaining what variance is over several pages of prose. We did not all fail high school.

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!

23 people found this helpful

Jijnasu Forever

Convoluted narrative for a simple message

Reviewed in the United States on May 19, 2021

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I don't have a Nobel prize nor expect to get one. So maybe this review is reflective of my dumbness. But this book was nothing like "Thinking Fast and Slow", primarily because the core concept seems simple enough but beaten to a unnecessarily nasty pulp.

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.

417 people found this helpful

Saptarshi Bhattacharyya

Not very impressive

Reviewed in India on July 3, 2021

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An average book with a lot of hype .. common knowledge packaged to sell books .. effort to monopolize thousands of years of human wisdom as one's own invention, with some colorful words

37 people found this helpful

Reader

The failure of trio-writing: and the triumph of marketing

Reviewed in Germany on July 4, 2021

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It is always a warning sign, when a household name (Kahneman) joins forces with others (Sibony and Sustein) to publish a new book. Especially so, if the new book is a look-alike of the old, very successful Thinking, Fast and Slow. Letter type, size and chapter structure are all the same.
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.

28 people found this helpful

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