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Noise: The new book from the authors of ‘Thinking, Fast and Slow’ and ‘Nudge’ (English Edition) Format Kindle
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Description du produit
Quatrième de couverture
Biographie de l'auteur
Détails sur le produit
- ASIN : B08LCZFJZ2
- Éditeur : William Collins (18 mai 2021)
- Langue : Anglais
- Taille du fichier : 1804 KB
- Synthèse vocale : Activée
- Confort de lecture : Activé
- X-Ray : Activé
- Word Wise : Activé
- Nombre de pages de l'édition imprimée : 415 pages
- Classement des meilleures ventes d'Amazon : 3,380 en Boutique Kindle (Voir les 100 premiers en Boutique Kindle)
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It is very different, and it is ironic that the book misses its goal of not being clear enough.
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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.
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.
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.