Noise: A Flaw in Human Judgment
Authors: Daniel Kahneman, Olivier Sibony and Cass R. Sunstein
Whenever writing a book review, I offer my point of view on someone's work and therefore I make judgments. Sometimes they depend on the authors, the topic, how it is addressed and documented, and most of the time I already have a sense of how I will appreciate a book after I read the first pages or chapters. As the recently published book Noise: A Flaw in Human Judgment states, "wherever there is judgment, there is noise, and more of it than you think".
If our mind is seen as a measuring instrument, you can imagine the "noise" in my judgment that was present when I started reading the first pages of this book, as it is co-authored by Nobel laureate Professor Daniel Kahneman, Professor Olivier Sibony and Professor Cass R. Sunstein. The proposed topic is human error, and to understand it, the authors help us explore bias and noise as different components of error. Published in May 2021 by Little, Brown Spark the book consists of six parts and 28 chapters that provide an interesting and informative read.
The first part explores noise versus bias in both public and private organizations. The authors introduce the idea of a noise audit, designed to measure how much disagreement there is among professionals considering the same cases within an organization. The second part investigates the nature of human judgment and explores how to measure accuracy and error. Here the reader will be surprised to find out the occasions and seemingly irrelevant factors that create noise in judgments. Next, the book presents predictive judgment and the key advantages of rules, formulas, artificial intelligence and machine learning algorithms over humans when it comes to making predictions. Part four explores human psychology in order to explain how noise happens. Readers will enjoy finding out the variety of factors, including personality and cognitive style. Part five will be of interest to readers that seek practical applications of noise reduction, as they will find out how they can improve their judgments and prevent errors. The case studies presented are really captivating.
Eliminating noise completely is not feasible and the last part of the book explores what the right level of noise is, as noise audits can be conducted to reduce noise. The appendices of this book include a practical guide for conducting a noise audit and an example of a bias checklist that a decision observer could use.
I recommend Noise to anyone who bases their work on judgments and wants to improve the quality of their decisions, as you can adapt the insights to suit anything that matters to you, whether it involves health, safety, education, money, employment, entertainment, forecasting in general or something else, even writing a review.
The authors propose a thought-provoking topic and give us all the opportunity to contribute to understanding and solving the problem of noise, that despite its ubiquity, is rarely considered to be an important problem.