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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.
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.
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.
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.