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This book examines how we make judgments and why so many judgments are flawed – including those that the makers believe to be correct judgments. The authors identify two prevailing problems in human judgments decisions. The first is bias and the second is noise. Bias is a subject that is covered in great depth and detail in Daniel Kahneman’s (one of the three authors of this book under review) book, ‘Thinking, Fast and Slow’ (2013 Farrar, Straus & Giroux).
‘Noise’ here, is about – well, noise, in the decision-making process and the decisions themselves. Noise is the diversity of decisions or conclusions on the same question. By way of a simple illustration, if we have a case in which a 40-year-old man, with a family of five, is convicted of stealing a loaf of bread, and two judges are asked to decide the sentence on him, one says jail for a day and the other says jail for a month. The different outcomes are the noise that conceals the correct judgment. One of them must be wrong.
The authors show why understanding noise is important. In their discussions, they question the utility and differences between rules and standards and how they might be applied to reduce noise in decision-making. They pose the question: ‘Who counts as disabled, such that they should qualify for economic benefits reserved for those who are unable to work?’ The authors maintain that if the question is phrased in this way, ‘judges will make ad hoc decisions that will be noisy and unfair’. They show how standards and rules approach may often result in less noisy, and fairer judgments. ‘If doctors are given clear guidelines to decide whether patients have strep throat, their decisions might be fast and relatively straightforward.’
This book is a primer for all decision makers, not only in the professional fields, but also in administrative work, and even all of us making decisions in the domestic settings. But, if leaders of domestic organisations use algorithms either to replace human judgment or supplement it, would that be desirable? Are we – should we – be prepared to displace discretion for rules? That, perhaps is a matter of distinguishing two different situations. The first is one where the facts are the same. The second is where the facts are not uniform. Even if we were to disagree with some of the claims of the authors, this is a book that will stimulate the mind of every decision maker.
I felt the need to write this review because the top reviews for this book are negative and I respectfully disagree as I find this book quite valuable as former statistical decision-maker in institutional investments (hard facts treasured over all) as well as a product decision-maker in a bank (client centric soft factors essential too).
Agreeing with other reviewers, this book does start off fairly slow (as it is meant to be holistic) but it does recommend the practice oriented reader to jump to the solution-ing part of the book if they are already convinced of the issues that "noise" (i.e. large variability between expert decision makers despite similar facts) causes in institutional decision making such as Judicial judgment, insurance underwriting, asset valuation, etc.
I would go so far as to recommend the practice-oriented institutional decision maker go to the last chapter "review & conclusion" and start from there because it will allow you to pick and choose items of strategic interest since some of the solutions suggested may already be familiar to you depending on your background (E.g. Managers who invest in applied psychological research for judiciaries, investment analysis, etc).
Personally, I found it useful to compare notes with these veteran researchers in decision making to ensure our methodologies were sound and also enjoyed their step-by-step approaches to implement organisation-level protocols (i.e. to affect all individual judgment makers in an organisation) to both detect and minimise the interference of "noise" in high-risk/ high-impact decision making. This then allows the reader to better figure out what methods need to be added to their standard operating procedures to improve.