Key concepts of the book
Companies live, or die, by the ability of the people who work within them to make decisions. Their judgments determine what strategy to follow, where to invest funds, how to set prices, who to hire and promote, and a host of other decisions. But there’s a problem, say Daniel Kahneman (2002 Nobel Prize in economics), Olivier Sibony and Cass Sunstein: wherever there is a need to make a decision, there is noise. Noise does not refer to the acoustics in the room, but to the high variability of inputs and cognitive processing that people have to deal with when making individual and collective judgments.
“Noise,” co-authored by Daniel Kahneman, Olivier Sibony and Cass Sunstein, exposes how we make systematic errors in decision making. The authors argue that it is time to pay more attention to noise, because reducing it can reduce error, just as with bias, another form of error related to our way of reasoning already studied by Kahneman and extensively explained in his previous works. In the decision-making process, both these elements, bias and noise, lead to error. Taking an example from the mathematics of precision: the bias can be related to the error that is made more frequently (average), while the noise is related to the variability of the error. That is, when looking at different judgments, if one notices that they share similar errors, one speaks of bias and prejudice (e.g., Judges impose harsher sentences on people of color rather than whites); conversely, if the errors in these judgments are different and disconnected, one speaks of noise (e.g., the same crime is punished differently by each Judge, making the system inconsistent). Thus, one can define “noise” as unwanted variability in professional judgments. The adjective “undesirable” in the definition is relevant, because sometimes variability in judgments is not a problem, sometimes it is even desirable, but not when it affects a professional judgment. The obvious example would be a doctor’s diagnosis. If two doctors give you two different diagnoses, at least one of them must be wrong. This is a judgment in which variability is undesirable. There is a correct answer, and you would want these two people to give the same answer. When you don’t have the same answer to questions that you would like the same answer to, you have noise.
So the book, a pioneer on the subject, is intended to inform and make the reader aware, rather than to serve as a tool for the dissemination of a solution method.
We have to accept that wherever there is judgment, there is noise. Just as you would want to reduce bias—even if you cannot completely eliminate it—reducing noise is a good thing. It improves accuracy
Here’s a forecasting example to make it more concrete. Say we are planning how long it will take to redecorate our kitchen. We can expect that all of us will be too optimistic; all of us will underestimate the time it will take to finish the renovation. But even though we’re all talking about the same kitchen, none of us will have the exact same estimate of how long the project will take. The average error, whereby we underestimate the time, will be the bias in our forecast. The variability in those forecasts is the noise
The noise we’re talking about in the book is “system noise,” or unwanted variability within a system of judgments. A good example is the judicial system. Judges should be interchangeable. They should give the identical sentence in the identical case. When they don’t, that is system noise. We found the same dynamics in medicine, with underwriters in insurance, and in many other functions
Book Structure & Contents
Noise is an important 400-page book that should leave executives who take the time to read it somewhat troubled. Their discomfort should stem from the realization that they have underestimated the negative effects of noise on decision making in their organizations. The book gives extensive analysis to the topic, outlining all the possibilities for error that can result. Specifically, there are three types of noise: level noise is the variability in the average responses of different individuals. Pattern noise is the variability in the responses of individuals to specific cases. Occasion noise is the variability in the responses of the same individual.
At this point, the authors offer readers advice for reducing the level of noise in decision making. The first recommendation they give is to improve the skills of those making judgments and decisions, and then suggest implementing a set of guidelines for “decision hygiene” that can help reduce noise. The choice of this term lies in the fact that noise reduction, like sanitation, is a prevention against an unidentified enemy, the authors explain. Guidelines include: providing enough information so that people don’t jump to conclusions too soon; aggregating independent people to generate the so-called “wisdom of the crowd”; using shared scales on which to base individual judgments; and breaking down complex judgments into more understandable components.
To conclude, the authors devote the final chapters of Noise to examining seven objections to decision hygiene, ranging from the expense of dealing with noise, to the crushing of individual prerogatives. Despite this lengthy display of objectivity, the authors conclude that even when the objections are considered, noise reduction remains a valid and even urgent goal.
As fascinating and enlightening as this book is, it can be a bit tiring to read. The authors have devoted more space to analyzing and studying the problem, rather than giving practical suggestions on how to solve it, resulting in a thorough and scientific analysis of noise. Which is understandable, given that the authors argue not only that the deleterious effects of noise are largely ignored by the public, but also that when they are identified, people tend to react with disbelief or outright resistance. After all, as admitted by Kahneman himself in an interview on McKinsey, the book is, in a sense, premature but has the merit of bringing attention to a topic, the awareness of decision-making processes that companies and managers use, to date still little investigated. Given the age of the scientist (87), having had the idea, the authors did not wait to publish it. As a result, the book came out a little early, before it should have, so it is still immature, not mature, but still comprehensive and clarifying.