There are some situations where you would hope or expect that experts in a certain area, when faced with the same fact pattern, would make similar decisions: for example, if several doctors separately examine the same patient, or several judges consider an appropriate sentence for the same convicted criminal, or several insurance underwriters consider the appropriate insurance premium for a certain commercial client. Of course, no one would expect identical decisions all the time, but one might hope or expect that in settings like these, the range of variation would be modest. However, it turns out that there is often a lot of “noise” in these decisions–meaning that real-world doctors, judges, underwriters, patent examiners, grant reviewers, teachers giving grades, and others often make very different decisions when facing what seem to be objectively similar fact patterns.
Sara Frueh interviews Daniel Kahneman in “Try to Design an Approach to Making a Judgment; Don’t Just Go Into It Trusting Your Intuition” (Issues in Science and Technology, Spring 2022). During the last few years, Kahneman has been focusing on this kind of “noise” in individual decision-making.
There is a fundamental conflict here. On one side, we often want experts to be able to see the big picture and the entire subject and to be alert for special circumstances that can and should make a difference. We want our experts to do individually customized decision-making. On the other side, different experts will be hypersensitive to different kinds of unique circumstances–often without even being self-aware about what facts or patterns are affecting their particular judgment. Thus, when experts have the freedom to customize their decisions, the result is discomfiting amount of “noise” in those decisions. There is no perfect answer here. But Kahneman argues that it is important to think about the process by which you would like to make the decision, and not just to rely on intuition. He says:
Given that, we do have ideas about procedures that are better than others, and the main example in my mind was a contrast between structured and unstructured hiring interviews. Unstructured interviews are when interviewers do what comes naturally. The structured interview breaks up the problems into dimensions, gets separate judgments on each dimension, and delays global evaluation until the end of the process, when all the information available can be considered at once. We know that neither structured nor unstructured interviews are very good predictors of success on the job, which is extremely difficult to predict. But within those limits, the structured interview is clearly better than the unstructured one.
If you think of decisions, then decisions involve options, and you can think of the options as similar to job candidates. That means that each option has attributes, and you want to assess those attributes separately. And we expect that approach to have the same kind of advantages that structured interviews have over unstructured interviews. So, the most important recommendation of decision hygiene is structuring. Try to design an approach to making a judgment or solving a problem, and don’t just go into it trusting your intuition to give you the right answer.
There are a number of controversies about whether this “structure” for decision-making can be written out in a clear way. Such a structure might be, for example, a written set of guidelines for judges that spell out how certain circumstances must (almost always) lead to certain criminal sentences. Or such a structure might be embodied in a computerized algorithm. Kahneman offers a qualified defense of creating structures for decision-making. As he says: “I have more confidence in the ability of institutions to improve their thinking than in the ability of individuals to improve their thinking.” When it comes to algorithms in particular, he argues:
Well, I think that there is widespread antipathy to algorithms, and it’s a special case of people’s preference for the natural over the artificial. In general we prefer something that is authentic over something that is fabricated, and we prefer something that’s human over something that is mechanical. And so we are strongly biased against algorithms. I think that’s true for all of us. Other things being equal, we would prefer a diagnosis to be made or a sentence to be passed by a human rather than by an algorithm. That’s an emotional thing.
But that feeling has to be weighed against the fact that algorithms, when they’re feasible, have major advantages over human judgment—one of them being that they are noise-free. That is, when you present the same problem to an algorithm on two occasions, you are going to get the same answer. So, that’s one big advantage of algorithms. The other is that they’re improvable. So, if you detect a bias or you detect something that is wrong, you can improve the algorithm much more easily than you can improve human judgment. And the third is that humans are biased and noisy. It’s not as if we’re talking of humans not being biased. The biases of humans are hidden by the noise in their judgment, whereas when there is a bias in an algorithm, you can see it because there is no noise to hide it. But the idea that only algorithms are biased is ridiculous; to the extent they have their biases, they learn them from people.
In addition, Kahneman points out that it is useful to separate the idea of using a structured approach to provide insight and background into making a decision, and the actual process of making the decision itself. There can be “deal-breaker” facts that cause a decision-maker to reach outside the structure that has been set up–but that doesn’t mean the structure isn’t a useful starting point.
[Y]ou really want to create a distinction between the final decision and the process of creating that decision. And in the process of creating a decision, diversity is a very good thing. When you’re constructing a committee to make decisions—whether of hiring or of strategy—you do not want people to come from exactly the same background and to have the same inclinations. You want diversity. You want different points of view represented, and you want different sources of knowledge represented. In some occasions increasing diversity in the making of the decision could reduce noise in the decision itself.
The real deep principle of what we call decision hygiene is independence. That is, you want items of information to be as independent of each other as possible. For example, you want witnesses who don’t talk to each other, and preferably who saw the same event from different perspectives. You do not want all your information to be redundant. So, good decisions are decisions that are made on the basis of diverse information.