The Undoing Project: A Friendship that Changed the World(58)

The first person to arrive for work on the morning Danny finished his sketch was an Oregon researcher named Robyn Dawes. Dawes was trained in statistics and legendary for the rigor of his mind. Danny handed him the sketch of Tom W. “He read it over and he had a sly smile, as if he had figured it out,” said Danny. “And he said, ‘Computer scientist!’ After that I wasn’t worried about how the Oregon students would fare.”

The Oregon students presented with the problem simply ignored all objective data and went with their gut sense, and predicted with great certainty that Tom W. was a computer scientist. Having established that people would allow a stereotype to warp their judgment, Amos and Danny then wondered: If people are willing to make irrational predictions based on that sort of information, what kind of predictions might they make if we give them totally irrelevant information? As they played with this idea—they might increase people’s confidence in their predictions by giving them any information, however useless—the laughter to be heard from the other side of the closed door must have grown only more raucous. In the end, Danny created another character. This one he named “Dick”:

Dick is a 30 year old man. He is married with no children. A man of high ability and high motivation, he promises to be quite successful in his field. He is well liked by his colleagues.

Then they ran another experiment. It was a version of the book bag and poker chips experiment that Amos and Danny had argued about in Danny’s seminar at Hebrew University. They told their subjects that they had picked a person from a pool of 100 people, 70 of whom were engineers and 30 of whom were lawyers. Then they asked them: What is the likelihood that the selected person is a lawyer? The subjects correctly judged it to be 30 percent. And if you told them that you were doing the same thing, but from a pool that had 70 lawyers in it and 30 engineers, they said, correctly, that there was a 70 percent chance the person you’d plucked from it was a lawyer. But if you told them you had picked not just some nameless person but a guy named Dick, and read them Danny’s description of Dick—which contained no information whatsoever to help you guess what Dick did for a living—they guessed there was an equal chance that Dick was a lawyer or an engineer, no matter which pool he had emerged from. “Evidently, people respond differently when given no specific evidence and when given worthless evidence,” wrote Danny and Amos. “When no specific evidence is given, the prior probabilities are properly utilized; when worthless specific evidence is given, prior probabilities are ignored.”*

There was much more to “On the Psychology of Prediction”—for instance, they showed that the very factors that caused people to become more confident in their predictions also led those predictions to be less accurate. And in the end it returned to the problem that had interested Danny since he had first signed on to help the Israeli army rethink how it selected and trained incoming recruits:

The instructors in a flight school adopted a policy of consistent positive reinforcement recommended by psychologists. They verbally reinforced each successful execution of a flight maneuver. After some experience with this training approach, the instructors claimed that contrary to psychological doctrine, high praise for good execution of complex maneuvers typically results in a decrement of performance on the next try. What should the psychologist say in response?

The subjects to whom they posed this question offered all sorts of advice. They surmised that the instructors’ praise didn’t work because it led the pilots to become overconfident. They suggested that the instructors didn’t know what they were talking about. No one saw what Danny saw: that the pilots would have tended to do better after an especially poor maneuver, or worse after an especially great one, if no one had said anything at all. Man’s inability to see the power of regression to the mean leaves him blind to the nature of the world around him. We are exposed to a lifetime schedule in which we are most often rewarded for punishing others, and punished for rewarding.



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When they wrote their first papers, Danny and Amos had no particular audience in mind. Their readers would be the handful of academics who happened to subscribe to the highly specialized psychology trade journals in which they published. By the summer of 1972, they had spent the better part of three years uncovering the ways in which people judged and predicted—but the examples that they had used to illustrate their ideas were all drawn directly from psychology, or from the strange, artificial-seeming tests that they had given high school and college students. Yet they were certain that their insights applied anywhere in the world that people were judging probabilities and making decisions. They sensed that they needed to find a broader audience. “The next phase of the project will be devoted primarily to the extension and application of this work to other high-level professional activities, e.g., economic planning, technological forecasting, political decision making, medical diagnosis, and the evaluation of legal evidence,” they wrote in a research proposal. They hoped, they wrote, that the decisions made by experts in these fields could be “significantly improved by making these experts aware of their own biases, and by the development of methods to reduce and counteract the sources of bias in judgment.” They wanted to turn the real world into a laboratory. It was no longer just students who would be their lab rats but also doctors and judges and politicians. The question was: How to do it?