Book ‘Noise’ by Daniel Kahneman

PDF Excerpt 'Noise' by Daniel Kahneman
A Flaw in Human Judgment
From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones--"full of novel insights, rigorous evidence, engaging writing, and practical applications” (Adam Grant). Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints...
Publisher: Little, Brown Spark (May 18, 2021)  Hardcover: 464 pages  ISBN-10: 0316451401  ISBN-13: 978-0316451406  ASIN: B08KSC11KQ

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Book excerpt

INTRODUCTION

Two Kinds of Error

I magine that four teams of friends have gone to a shooting arcade. Each team consists of five people; they share one rifle, and each person fires one shot. Figure 1 shows their results. In an ideal world, every shot would hit the bull’s-eye.

A Flaw in Human Judgment
Figure 1: Four teams

That is nearly the case for Team A. The team’s shots are tightly clustered around the bull’s-eye, close to a perfect pattern.

We call Team B biased because its shots are systematically off target. As the figure illustrates, the consistency of the bias supports a prediction. If one of the team’s members were to take another shot, we would bet on its landing in the same area as the first five. The consistency of the bias also invites a causal explanation: perhaps the gunsight on the team’s rifle was bent.

We call Team C noisy because its shots are widely scattered. There is no obvious bias, because the impacts are roughly centered on the bull’s-eye. If one of the team’s members took another shot, we would know very little about where it is likely to hit. Furthermore, no interesting hypothesis comes to mind to explain the results of Team C. We know that its members are poor shots. We do not know why they are so noisy.

Team D is both biased and noisy. Like Team B, its shots are systematically off target; like Team C, its shots are widely scattered.

But this is not a book about target shooting. Our topic is human error. Bias and noise—systematic deviation and random scatter—are different components of error.

The targets illustrate the difference. The shooting range is a metaphor for what can go wrong in human judgment, especially in the diverse decisions that people make on behalf of organizations. In these situations, we will find the two types of error illustrated in figure 1. Some judgments are biased; they are systematically off target. Other judgments are noisy, as people who are expected to agree end up at very different points around the target. Many organizations, unfortunately, are afflicted by both bias and noise.

Figure 2 illustrates an important difference between bias and noise. It shows what you would see at the shooting range if you were shown only the backs of the targets at which the teams were shooting, without any indication of the bull’s-eye they were aiming at.

From the back of the target, you cannot tell whether Team A or Team B is closer to the bull’s-eye. But you can tell at a glance that Teams C and D are noisy and that Teams A and B are not. Indeed, you know just as much about scatter as you did in figure 1. A general property of noise is that you can recognize and measure it while knowing nothing about the target or bias.

Figure 2: Looking at the back of the target

The general property of noise just mentioned is essential for our purposes in this book, because many of our conclusions are drawn from judgments whose true answer is unknown or even unknowable. When physicians offer different diagnoses for the same patient, we can study their disagreement without knowing what ails the patient. When film executives estimate the market for a movie, we can study the variability of their answers without knowing how much the film eventually made or even if it was produced at all. We don’t need to know who is right to measure how much the judgments of the same case vary. All we have to do to measure noise is look at the back of the target.

To understand error in judgment, we must understand both bias and noise. Sometimes, as we will see, noise is the more important problem. But in public conversations about human error and in organizations all over the world, noise is rarely recognized. Bias is the star of the show. Noise is a bit player, usually offstage. The topic of bias has been discussed in thousands of scientific articles and dozens of popular books, few of which even mention the issue of noise. This book is our attempt to redress the balance.

In real-world decisions, the amount of noise is often scandalously high. Here are a few examples of the alarming amount of noise in situations in which accuracy matters:

  • Medicine is noisy. Faced with the same patient, different doctors make different judgments about whether patients have skin cancer, breast cancer, heart disease, tuberculosis, pneumonia, depression, and a host of other conditions. Noise is especially high in psychiatry, where subjective judgment is obviously important. However, considerable noise is also found in areas where it might not be expected, such as in the reading of X-rays.
  • Child custody decisions are noisy. Case managers in child protection agencies must assess whether children are at risk of abuse and, if so, whether to place them in foster care. The system is noisy, given that some managers are much more likely than others to send a child to foster care. Years later, more of the unlucky children who have been assigned to foster care by these heavy-handed managers have poor life outcomes: higher delinquency rates, higher teen birth rates, and lower earnings.
  • Forecasts are noisy. Professional forecasters offer highly variable predictions about likely sales of a new product, likely growth in the unemployment rate, the likelihood of bankruptcy for troubled companies, and just about everything else. Not only do they disagree with each other, but they also disagree with themselves. For example, when the same software developers were asked on two separate days to estimate the completion time for the same task, the hours they projected differed by 71%, on average. Asylum decisions are noisy. Whether an asylum seeker will be admitted into the United States depends on something like a lottery. A study of cases that were randomly allotted to different judges found that one judge admitted 5% of applicants, while another admitted 88%. The title of the study says it all: “Refugee Roulette.” (We are going to see a lot of roulette.)
  • Personnel decisions are noisy. Interviewers of job candidates make widely different assessments of the same people. Performance ratings of the same employees are also highly variable and depend more on the person doing the assessment than on the performance being assessed.
  • Bail decisions are noisy. Whether an accused person will be granted bail or instead sent to jail pending trial depends partly on the identity of the judge who ends up hearing the case. Some judges are far more lenient than others. Judges also differ markedly in their assessment of which defendants present the highest risk of flight or reoffending.
  • Forensic science is noisy. We have been trained to think of fingerprint identification as infallible. But fingerprint examiners sometimes differ in deciding whether a print found at a crime scene matches that of a suspect. Not only do experts disagree, but the same experts sometimes make inconsistent decisions when presented with the same print on different occasions. Similar variability has been documented in other forensic science disciplines, even DNA analysis.
  • Decisions to grant patents are noisy. The authors of a leading study on patent applications emphasize the noise involved: “Whether the patent office grants or rejects a patent is significantly related to the happenstance of which examiner is assigned the application.” This variability is obviously troublesome from the standpoint of equity.

All these noisy situations are the tip of a large iceberg. Wherever you look at human judgments, you are likely to find noise. To improve the quality of our judgments, we need to overcome noise as well as bias.

This book comes in six parts. In part 1, we explore the difference between noise and bias, and we show that both public and private organizations can be noisy, sometimes shockingly so. To appreciate the problem, we begin with judgments in two areas. The first involves criminal sentencing (and hence the public sector). The second involves insurance (and hence the private sector). At first glance, the two areas could not be more different. But with respect to noise, they have much in common. To establish that point, we introduce the idea of a noise audit, designed to measure how much disagreement there is among professionals considering the same cases within an organization.

In part 2, we investigate the nature of human judgment and explore how to measure accuracy and error. Judgments are susceptible to both bias and noise. We describe a striking equivalence in the roles of the two types of error. Occasion noise is the variability in judgments of the same case by the same person or group on different occasions. A surprising amount of occasion noise arises in group discussion because of seemingly irrelevant factors, such as who speaks first.

Part 3 takes a deeper look at one type of judgment that has been researched extensively: predictive judgment. We explore the key advantage of rules, formulas, and algorithms over humans when it comes to making predictions: contrary to popular belief, it is not so much the superior insight of rules but their noiselessness. We discuss the ultimate limit on the quality of predictive judgment—objective ignorance of the future—and how it conspires with noise to limit the quality of prediction. Finally, we address a question that you will almost certainly have asked yourself by then: if noise is so ubiquitous, then why had you not noticed it before?

Part 4 turns to human psychology. We explain the central causes of noise. These include interpersonal differences arising from a variety of factors, including personality and cognitive style; idiosyncratic variations in the weighting of different considerations; and the different uses that people make of the very same scales. We explore why people are oblivious to noise and are frequently unsurprised by events and judgments they could not possibly have predicted.

Part 5 explores the practical question of how you can improve your judgments and prevent error. (Readers who are primarily interested in practical applications of noise reduction might skip the discussion of the challenges of prediction and of the psychology of judgment in parts 3 and 4 and move directly to this part.) We investigate efforts to tackle noise in medicine, business, education, government, and elsewhere. We introduce several noise-reduction techniques that we collect under the label of decision hygiene. We present five case studies of domains in which there is much documented noise and in which people have made sustained efforts to reduce it, with instructively varying degrees of success. The case studies include unreliable medical diagnoses, performance ratings, forensic science, hiring decisions, and forecasting in general. We conclude by offering a system we call the mediating assessments protocol: a general-purpose approach to the evaluation of options that incorporates several key practices of decision hygiene and aims to produce less noisy and more reliable judgments.

What is the right level of noise? Part 6 turns to this question. Perhaps counterintuitively, the right level is not zero. In some areas, it just isn’t feasible to eliminate noise. In other areas, it is too expensive to do so. In still other areas, efforts to reduce noise would compromise important competing values. For example, efforts to eliminate noise could undermine morale and give people a sense that they are being treated like cogs in a machine. When algorithms are part of the answer, they raise an assortment of objections; we address some of them here. Still, the current level of noise is unacceptable. We urge both private and public organizations to conduct noise audits and to undertake, with unprecedented seriousness, stronger efforts to reduce noise. Should they do so, organizations could reduce widespread unfairness—and reduce costs in many areas.

With that aspiration in mind, we end each chapter with a few brief propositions in the form of quotations. You can use these statements as they are or adapt them for any issues that matter to you, whether they involve health, safety, education, money, employment, entertainment, or something else. Understanding the problem of noise, and trying to solve it, is a work in progress and a collective endeavor. All of us have opportunities to contribute to this work. This book is written in the hope that we can seize those opportunities.