I recently read the book Noise by Daniel Kahneman, Olivier Sibony, and Cass Sunstein. I will refer to it as the current book, because it was written after the book Thinking, Fast and Slow by Kahneman (one of the authors of the current book); I will refer to the latter book as the previous book because many concepts from the previous book are briefly reviewed in the current book, and as I reviewed the previous book in the post just before this one on this blog [LINK], I will sometimes compare some aspects of the current book to the previous book.
The current book introduces the concepts of statistical noise & statistical bias in human judgments, discusses the psychological biases that can lead to statistical biases & noise (of which statistical noise can be clearly seen even in the absence of clear information about statistical biases), demonstrates how statistical noise can lead to uncontrolled & large variations in human judgments in fields like criminal justice, medicine, forensic science, insurance claims adjustment, corporate hiring, and college admissions, explains the sorts of systematic techniques at individual & organizational levels that can be used to reduce noise in judgments, and discusses some tradeoffs that may be encountered when implementing these noise reduction strategies. The authors' discussion of many of the psychological biases that lead to statistical noise in judgments reviews concepts from the previous book, especially Systems 1 & 2.
When reading the current book, I found myself generally agreeing with the discussions of techniques to reduce noise in domains where the presence of significant statistical noise in judgments is broadly recognized as a severe problem. These techniques include aggregating predictions or evaluations that are made independently, structuring/sequencing discussions among people judging things so that their decisions don't affect each other through emergent group-based social dynamics, carefully accounting for base rates from external information when assessing various internal probabilities, and breaking up decision processes into smaller steps that are more clearly defined in their intent and in example decisions/anchors. It helped a lot that I had read the previous book first, such that even if I didn't remember every detail of every psychological bias presented in both the previous book and the current book, those things looked familiar upon reading them in the current book.
There were also a few new things that I learned from the current book. I learned about how the process of judgment feels so satisfying and infuses confidence into the person making the judgment specifically from the psychological signal of having completed the judgment, which explains why so many people who make professional judgments in many domains are so reluctant to turn their discretion over to more systematic rules or algorithms. I also learned about how simple models of human predictive judgments, when those predictive judgments are about specific outcomes, may do a better job at predicting the outcomes that are the objects of judgment than at predicting the judgments that humans would make, simply because those models lack within-person noise pervasive in human judgments.
However, my overall opinion of the current book was shaped more by the many major and minor (the latter to an appropriately lesser extent) criticisms of it. These minor and major criticisms as well as my concluding remarks will be presented in separate sections as follows after the jump; the spoiling of my concluding remarks is simply that I do not recommend this book to others.
