Noise Daniel Kahneman Quotes

To understand error in judgment, we must understand both bias and noise.

wherever there is judgment, there is noise—and more of it than you think.

There is at least one source of occasion noise that we have all noticed: mood.

Causally, noise is nowhere; statistically, it is everywhere.

Bias and noise—systematic deviation and random scatter—are different components of error.

Life is often more complex than the stories we like to tell about it.

It is more useful to pay attention to people who disagree with you than to pay attention to those who agree.

Most organizations prefer consensus and harmony over dissent and conflict. The procedures in place often seem expressly designed to minimize the frequency of exposure to actual disagreements and, when such disagreements happen, to explain them away.

people are rarely aware of their own biases when they are being misled by them. This lack of awareness is itself a known bias, the bias blind spot. People often recognize biases more easily in others than they do in themselves

In a negotiation situation, for instance, good mood helps. People in a good mood are more cooperative and elicit reciprocation. They tend to end up with better results than do unhappy negotiators.

When physicians are under time pressure, they are apparently more inclined to choose a quick-fix solution, despite its serious downsides.

When Vul and Pashler let three weeks pass before asking their subjects the same question again, the benefit rose to one-third the value of a second opinion.

good judgments depend on what you know, how well you think, and how you think.

When the facts change, I change my mind. What do you do?”)

Judgment is not a synonym for thinking, and making accurate judgments is not a synonym for having good judgment.

Noise is mostly a by-product of our uniqueness, of our “judgment personality.

On the other hand, a good mood makes us more likely to accept our first impressions as true without challenging them.

people who make judgments behave as if a true value exists, regardless of whether it does.

People cannot be faulted for failing to predict the unpredictable, but they can be blamed for a lack of predictive humility.

Judgment can therefore be described as measurement in which the instrument is a human mind.

In terms of noise, psychiatry is an extreme case.

it is hard to agree with reality if you cannot agree with yourself.

Scientists in diverse disciplines were quick to adopt the least squares method. Over two centuries later, it remains the standard way to evaluate errors wherever achieving accuracy is the goal.

Averaging two guesses by the same person does not improve judgments as much as does seeking out an independent second opinion. As Vul and Pashler put it, “You can gain about 1/10th as much from asking yourself the same question twice as you can from getting a second opinion from someone else.” This is not a large improvement. But you can make the effect much larger by waiting to make a second guess.

Words that you have seen before become easier to see again—you can identify them better than other words when they are shown very briefly or masked by noise, and you will be quicker (by a few hundredths of a second) to read them than to read other words. In short, you experience greater cognitive ease in perceiving a word you have seen earlier, and it is this sense of ease that gives you the impression of familiarity.

There is good reason to believe that general intelligence is likely to be associated with better judgment. Intelligence is correlated with good performance in virtually all domains. All other things being equal, it is associated not only with higher academic achievement but also with higher job performance.

Groups can go in all sorts of directions, depending in part on factors that should be irrelevant. Who speaks first, who speaks last, who speaks with confidence, who is wearing black, who is seated next to whom, who smiles or frowns or gestures at the right moment—all these factors, and many more, affect outcomes.

Causal thinking helps us make sense of a world that is far less predictable than we think. It also explains why we view the world as far more predictable than it really is. In the valley of the normal, there are no surprises and no inconsistencies. The future seems as predictable as the past. And noise is neither heard nor seen.

How Groups Amplify Noise

judgment, there is noise—and more of it than we think.

Level noise is variability in the average level of judgments by different judges. Pattern noise is variability in judges’ responses to particular cases.

Variability in judgments is also expected and welcome in a competitive situation in which the best judgments will be rewarded.

The same is true when multiple teams of researchers attack a scientific problem, such as the development of a vaccine: we very much want them to look at it from different angles.

But our focus is on judgments in which variability is undesirable. System noise is a problem of systems, which are organizations, not markets.

Wherever there is judgment, there is noise—and more of it than we think.

So far in this chapter, we have focused on predictive judgment tasks, and most of the judgments we will discuss are of that type.

Sentencing a felon is not a prediction. It is an evaluative judgment that seeks to match the sentence to the severity of the crime.

For embezzlement actions that were similar to one another, one man was sentenced to 117 days in prison, while another was sentenced to 20 years.

Averaging is mathematically guaranteed to reduce noise:

Whatever their flaws, rankings are less noisy than ratings.

if we want people to feel that they have been treated with respect and dignity, we might have to tolerate some noise.

If you want to deter misconduct, you should tolerate some noise.

objective ignorance accumulates steadily the further you look into the future.

There is essentially no evidence of situations in which people do very poorly and models do very well with the same information.

there is a large amount of objective ignorance in the prediction of human behavior.

why do we all—seem to underestimate our objective ignorance of the world?

the preference for causal thinking also contributes to the neglect of noise as a source of error, because noise is a fundamentally statistical notion.

Errors are bound to occur when a judgment of similarity is substituted for a judgment of probability,

Venn diagrams apply only to probability, not to similarity. Hence the predictable logical error that many people make.

Most of us, most of the time, live with the unquestioned belief that the world looks as it does because that’s the way it is.

When we substitute an easier question for the one we should be answering, errors are bound to occur.

There is a genuine limit on people’s ability to assign distinct labels to stimuli on a dimension, and that limit is around seven labels.

bias is a compelling figure, while noise is the background to which we pay no attention. That is how we remain largely unaware of a large flaw in our judgment.

whenever accuracy is the goal, bias and noise play the same role in the calculation of overall error.

Henry Faulds, a Scottish physician, published the first scientific paper suggesting the use of fingerprints as an identification technique.

simple mechanical rules were generally superior to human judgment.

A matter of judgment is one with some uncertainty about the answer and where we allow for the possibility that reasonable and competent people might disagree.

Relying on causal thinking about a single case is a source of predictable errors. Taking the statistical view, which we will also call the outside view, is a way to avoid these errors.

As humans, we are keenly aware that we make mistakes, but that is a privilege we are not prepared to share. We expect machines to be perfect. If this expectation is violated, we discard them.

Pain and noise are biologically set to be signals that attract attention, and depression involves a self-reinforcing cycle of miserable thoughts. There is therefore no adaptation to these conditions.

Two men, neither of whom had a criminal record, were convicted for cashing counterfeit checks in the amounts of $58.40 and $35.20, respectively. The first man was sentenced to fifteen years, the second to 30 days.

People often tend to trust and like leaders who are firm and clear and who seem to know, immediately and deep in their bones, what is right. Such leaders inspire confidence. But the evidence suggests that if the goal is to reduce error, it is better for leaders (and others) to remain open to counterarguments and to know that they might be wrong. If they end up being decisive, it is at the end of a process, not at the start.

Diversity of opinions is essential for generating ideas and options. Contrarian thinking is essential to innovation. A plurality of opinions among movie critics is a feature, not a bug. Disagreements among traders make markets. Strategy differences among competing start-ups enable markets to select the fittest. In what we call matters of judgment, however, system noise is always a problem. If two doctors give you different diagnoses, at least one of them is wrong.

Every large branch of the company has several qualified underwriters. When a quote is requested, anyone who happens to be available may be assigned to prepare it. In effect, the particular underwriter who will determine a quote is selected by a lottery.

Enforcing decision hygiene can be thankless. Noise is an invisible enemy, and a victory against an invisible enemy can only be an invisible victory. But like physical health hygiene, decision hygiene is vital. After a successful operation, you like to believe that it is the surgeon’s skill that saved your life—and it did, of course—but if the surgeon and all the personnel in the operating room had not washed their hands, you might be dead. There may not be much glory to be gained in hygiene, but there are results.

To be actively open-minded is to actively search for information that contradicts your preexisting hypotheses. Such information includes the dissenting opinions of others and the careful weighing of new evidence against old beliefs. Actively openminded people agree with statements like this: “Allowing oneself to be convinced by an opposing argument is a sign of good character.” They disagree with the proposition that “changing your mind is a sign of weakness” or that “intuition is the best guide in making decisions.

Level noise is when judges show different levels of severity. Pattern noise is when they disagree with one another on which defendants deserve more severe or more lenient treatment. And part of pattern noise is occasion noise—when judges disagree with themselves.

We hold a single interpretation of the world around us at any one time, and we normally invest little effort in generating plausible alternatives to it. One interpretation is enough, and we experience it as true. We do not go through life imagining alternative ways of seeing what we see.

There is reason to believe that some people make better judgments than others do. Task-specific skill, intelligence, and a certain cognitive style—best described as being actively open-minded—characterize the best judges. Unsurprisingly, good judges will make few egregious mistakes. Given the multiple sources of individual differences, however, we should not expect even the best judges to be in perfect agreement on complex judgment problems. The infinite variety of backgrounds, personalities, and experiences that make each of us unique is also what makes noise inevitable.

We have described these studies of mood in some detail because we need to emphasize an important truth: you are not the same person at all times. As your mood varies (something you are, of course, aware of), some features of your cognitive machinery vary with it (something you are not fully aware of).

You may have noticed that the decomposition of system noise into level noise and pattern noise follows the same logic as the error equation in the previous chapter, which decomposed error into bias and noise. This time, the equation can be written as follows: System Noise2 = Level Noise2 + Pattern Noise2

More often than not, in fact, outstanding performance will become less outstanding. Conversely, very poor performance will improve. It is easy to imagine social, psychological, or even political reasons for this observation, but reasons are not required. The phenomenon is purely statistical. Extreme observations in one direction or the other will tend to become less extreme, simply because past performance is not perfectly correlated with future performance. This tendency is called regression to the mean (hence the technical term nonregressive for matching predictions, which fail to take it into account).

For the insurance company, the illusion of agreement was shattered only by the noise audit. How had the leaders of the company remained unaware of their noise problem? There are several possible answers here, but one that seems to play a large role in many settings is simply the discomfort of disagreement.

Another formal process for aggregating diverse views is known as the Delphi method. In its classic form, this method involves multiple rounds during which the participants submit estimates (or votes) to a moderator and remain anonymous to one another. At each new round, the participants provide reasons for their estimates and respond to the reasons given by others, still anonymously. The process encourages estimates to converge (and sometimes forces them to do so by requiring new judgments to fall within a specific range of the distribution of previous-round judgments). The method benefits both from aggregation and social learning.

Matters of judgment differ from matters of opinion or taste, in which unresolved differences are entirely acceptable. The insurance executives who were shocked by the result of the noise audit would have no problem if claims adjusters were sharply divided over the relative merits of the Beatles and the Rolling Stones, or of salmon and tuna.

From the perspective of noise reduction, a singular decision is a recurrent decision that happens only once. Whether you make a decision only once or a hundred times, your goal should be to make it in a way that reduces both bias and noise. And practices that reduce error should be just as effective in your one-of-a-kind decisions as in your repeated ones.

If an event that was assigned a probability of 90% fails to happen, the judgment of probability was not necessarily a bad one. After all, outcomes that are just 10% likely to happen end up happening 10% of the time. The Gambardi exercise is an example of a nonverifiable predictive judgment, for two separate reasons: Gambardi is fictitious and the answer is probabilistic.

mood has a measurable influence on what you think: what you notice in your environment, what you retrieve from your memory, how you make sense of these signals. But mood has another, more surprising effect: it also changes how you think. And here, the effects are not those you might imagine. Being in a good mood is a mixed blessing, and bad moods have a silver lining. The costs and benefits

group deliberation often adds more error in bias than it removes in noise. Organizations that want to harness the power of diversity must welcome the disagreements that will arise when team members reach their judgments independently. Eliciting and aggregating judgments that are both independent and diverse will often be the easiest, cheapest, and most broadly applicable decision hygiene strategy.

The truth is, as Jacoby and many followers have shown, that the name David Stenbill will look familiar when you see it because you will see it more clearly. Words that you have seen before become easier to see again—you can identify them better than other words when they are shown very briefly or masked by noise, and you will be quicker (by a few hundredths of a second) to read them than to read other words. In

The essential feature of this internal signal is that the sense of coherence is part of the experience of judgment. It is not contingent on a real outcome. As a result, the internal signal is just as available for nonverifiable judgments as it is for real, verifiable ones. This explains why making a judgment about a fictitious character like Gambardi feels very much the same as does making a judgment about the real world.

An adjuster is assigned to the claim—just as the underwriter was assigned, because she happens to be available. The adjuster gathers the facts of the case and provides an estimate of its ultimate cost to the company. The same adjuster then takes charge of negotiating with the claimant’s representative to ensure that the claimant receives the benefits promised in the policy while also protecting the company from making excessive payments.

When we asked 828 CEOs and senior executives from a variety of industries how much variation they expected to find in similar expert judgments, 10% was also the median answer and the most frequent one (the second most popular was 15%). A 10% difference would mean, for instance, that one of the two underwriters set a premium of $9,500 while the other quoted $10,500. Not a negligible difference, but one that an organization can be expected to tolerate.

This request required the subjects to think actively of information they had not considered the first time. The instructions to participants read as follows: First, assume that your first estimate is off the mark. Second, think about a few reasons why that could be. Which assumptions and considerations could have been wrong? Third, what do these new considerations imply? Was the first estimate rather too high or too low? Fourth, based on this new perspective, make a second, alternative estimate.

The Delphi method has worked well in many situations, but it can be challenging to implement. A simpler version, mini-Delphi, can be deployed within a single meeting. Also called estimate-talk-estimate, it requires participants first to produce separate (and silent) estimates, then to explain and justify them, and finally to make a new estimate in response to the estimates and explanations of others. The consensus judgment is the average of the individual estimates obtained in that second round.

We have contrasted two ways of evaluating a judgment: by comparing it to an outcome and by assessing the quality of the process that led to it. Note that when the judgment is verifiable, the two ways of evaluating it may reach different conclusions in a single case. A skilled and careful forecaster using the best possible tools and techniques will often miss the correct number in making a quarterly inflation forecast. Meanwhile, in a single quarter, a dart-throwing chimpanzee will sometimes be right.

Our noise audit found much greater differences. By our measure, the median difference in underwriting was 55%, about five times as large as was expected by most people, including the company’s executives. This result means, for instance, that when one underwriter sets a premium at $9,500, the other does not set it at $10,500—but instead quotes $16,700. For claims adjusters, the median ratio was 43%. We stress that these results are medians: in half the pairs of cases, the difference between the two judgments was even larger.

Many professional judgments are nonverifiable. Barring egregious errors, underwriters will never know, for instance, whether a particular policy was overpriced or underpriced. Other forecasts may be nonverifiable because they are conditional. “If we go to war, we will be crushed” is an important prediction, but it is likely to remain untested (we hope). Or forecasts may be too long term for the professionals who make them to be brought to account—like, for instance, an estimate of mean temperatures by the end of the twenty-first century.

In an ideal world, evaluating people’s performance would not be a judgment task; objective facts would be sufficient to determine how well people are doing. But most modern organizations have little in common with Adam Smith’s pin factory, in which every worker had a measurable output. What would that output be for a chief financial officer or for a head of research? Today’s knowledge workers balance multiple, sometimes contradictory objectives. Focusing on only one of them might produce erroneous evaluations and have harmful incentive effects.

They asked forty-two experienced investors in the firm to estimate the fair value of a stock (the price at which the investors would be indifferent to buying or selling). The investors based their analysis on a one-page description of the business; the data included simplified profit and loss, balance sheet, and cash flow statements for the past three years and projections for the next two. Median noise, measured in the same way as in the insurance company, was 41%. Such large differences among investors in the same firm, using the same valuation methods, cannot be good news.

Aggregating judgments can be an excellent way of reducing noise, and therefore error. But what happens if people are listening to one another? You might well think that their doing so is likely to help. After all, people can learn from one another and thus figure out what is right. Under favorable circumstances, in which people share what they know, deliberating groups can indeed do well. But independence is a prerequisite for the wisdom of crowds. If people are not making their own judgments and are relying instead on what other people think, crowds might not be so wise after all.

Focusing on the process of judgment, rather than its outcome, makes it possible to evaluate the quality of judgments that are not verifiable, such as judgments about fictitious problems or long-term forecasts. We may not be able to compare them to a known outcome, but we can still tell whether they have been made incorrectly. And when we turn to the question of improving judgments rather than just evaluating them, we will focus on process, too. All the procedures we recommend in this book to reduce bias and noise aim to adopt the judgment process that would minimize error over an ensemble of similar cases.

The exact value of the quote has significant consequences for the company. A high premium is advantageous if the quote is accepted, but such a premium risks losing the business to a competitor. A low premium is more likely to be accepted, but it is less advantageous to the company. For any risk, there is a Goldilocks price that is just right—neither too high nor too low—and there is a good chance that the average judgment of a large group of professionals is not too far from this Goldilocks number. Prices that are higher or lower than this number are costly—this is how the variability of noisy judgments hurts the bottom line.

Herzog and Hertwig then averaged the two estimates thus produced. Their technique, which they named dialectical bootstrapping, produced larger improvements in accuracy than did a simple request for a second estimate immediately following the first. Because the participants forced themselves to consider the question in a new light, they sampled another, more different version of themselves—two “members” of the “crowd within” who were further apart. As a result, their average produced a more accurate estimate of the truth. The gain in accuracy with two immediately consecutive “dialectical” estimates was about half the value of a second opinion.

nothing. Even when unfairness is only a minor concern, system noise poses another problem. People who are affected by evaluative judgments expect the values these judgments reflect to be those of the system, not of the individual judges. Something must have gone badly wrong if one customer, complaining of a defective laptop, gets fully reimbursed, and another gets a mere apology; or if one employee who has been with a firm for five years asks for a promotion and gets exactly that, while another employee, whose performance is otherwise identical, is politely turned down. System noise is inconsistency, and inconsistency damages the credibility of the system.

They were testing for a particular driver of noise: social influence. The key finding was that group rankings were wildly disparate: across different groups, there was a great deal of noise. In one group, “Best Mistakes” could be a spectacular success, while “I Am Error” could flop. In another group, “I Am Error” could do exceedingly well, and “Best Mistakes” could be a disaster. If a song benefited from early popularity, it could do really well. If it did not get that benefit, the outcome could be very different. To be sure, the very worst songs (as established by the control group) never ended up at the very top, and the very best songs never ended up at the very bottom.

The early estimate matters because it sets an implicit goal for the adjuster in future negotiations with the claimant. The insurance company is also legally obligated to reserve the predicted cost of each claim (i.e., to have enough cash to be able to pay it). Here again, there is a Goldilocks value from the perspective of the company. A settlement is not guaranteed, as there is an attorney for the claimant on the other side, who may choose to go to court if the offer is miserly. On the other hand, an overly generous reserve may allow the adjuster too much latitude to agree to frivolous demands. The adjuster’s judgment is consequential for the company—and even more consequential for the claimant.

A large literature going back several decades has documented noise in professional judgment. Because we were aware of this literature, the results of the insurance company’s noise audit did not surprise us. What did surprise us, however, was the reaction of the executives to whom we reported our findings: no one at the company had expected anything like the amount of noise we had observed. No one questioned the validity of the audit, and no one claimed that the observed amount of noise was acceptable. Yet the problem of noise—and its large cost—seemed like a new one for the organization. Noise was like a leak in the basement. It was tolerated not because it was thought acceptable but because it had remained unnoticed.

If a weather forecaster said today’s high temperature would be seventy degrees Fahrenheit and it is sixty-five degrees, the forecaster made an error of plus five degrees. Evidently, this approach does not work for nonverifiable judgments like the Gambardi problem, which have no true outcome. How, then, are we to decide what constitutes good judgment? The answer is that there is a second way to evaluate judgments. This approach applies both to verifiable and nonverifiable ones. It consists in evaluating the process of judgment. When we speak of good or bad judgments, we may be speaking either about the output (e.g., the number you produced in the Gambardi case) or about the process—what you did to arrive at that number.

Fifty judges from various districts were asked to set sentences for defendants in hypothetical cases summarized in identical pre-sentence reports. The basic finding was that “absence of consensus was the norm” and that the variations across punishments were “astounding.” A heroin dealer could be incarcerated for one to ten years, depending on the judge. Punishments for a bank robber ranged from five to eighteen years in prison. The study found that in an extortion case, sentences varied from a whopping twenty years imprisonment and a $65,000 fine to a mere three years imprisonment and no fine. Most startling of all, in sixteen of twenty cases, there was no unanimity on whether any incarceration was appropriate. This study was followed by a series of others, all of which found similarly shocking levels of noise. In 1977, for example, William Austin and Thomas Williams conducted a survey of forty-seven judges, asking them to respond to the same five cases, each involving low-level offenses. All the descriptions of the cases included summaries of the information used by judges in actual sentencing, such as the charge, the testimony, the previous criminal record (if any), social background, and evidence relating to character. The key finding was “substantial disparity.” In a case involving burglary, for example, the recommended sentences ranged from five years in prison to a mere thirty days (alongside a fine of $100). In a case involving possession of marijuana, some judges recommended prison terms; others recommended probation.