The representativeness heuristic is a natural part of human cognition, where people see commonality between similar-looking items or people. It has advantages and disadvantages. A study by Kahneman and Tversky found that subjects tended to assign Tom W. to the engineering group based on representativeness alone, despite the fact that engineering students were relatively rare in the school where the study was conducted. The lesson learned is to consider underlying probabilities and not make too many assumptions.
The representativeness heuristic is a heuristic (rule of thumb) that has been shown to be a natural part of human cognition. Like any other rule of thumb, it has advantages and disadvantages. The representativeness heuristic holds that people see commonality between similar-looking items or people, or between an object and a group of which it appears to be a part. For example, a culturally ignorant Westerner might see all dark-skinned people as part of the same group, despite the fact that there are many dark-skinned races with no relation to each other.
The studies leading to the discovery of the representativeness heuristic were initially conducted by Amos Tversky and Daniel Kahneman in the early 1970s. Kahneman would go on to win the Nobel Prize in Economics in 2002. To test the representativeness heuristic, Kahneman and Tversky gave their subjects the following information:
“Tom W. is highly intelligent, though lacking in true creativity. He needs order and clarity, and clean, uncluttered systems in which every detail finds its proper place. His writing is rather boring and rote, enlivened at times by somewhat corny puns and sci-fi-type flashes of imagination. He has a strong drive for competence. He seems to have little sympathy for other people and doesn’t like interacting with others. Self-centered, he nevertheless has a deep moral sense ”.
The subjects who received the information were then divided into three groups, each of which was assigned a different decision-making task:
The first group was asked how Tom W sounded similar to nine different majors. Most subjects associated him more with an engineering major, and less with a social science/social work student.
The second group was asked to estimate the probability that Tom W. was a member of one of nine different majors. These probabilities were closely in line with the similarity ratings provided by the first group.
The third group was asked to estimate what percentages of first-year graduate students were in each of the nine majors, a question completely foreign to Tom W.
The results indicated that subjects had a high tendency to assign Tom W. to the engineering group based on representativeness alone, despite the fact that engineering students were relatively rare in the school where the study was conducted, with a substantially less than 1/9 of all students. Being misled on the basis of the representations, subjects ignored the underlying probabilities that Tom W. was in a given specialty, despite his personal qualities. Subsequent extensive testing has found that this pathology is universal and applies across a wide variety of problem domains.
The lesson learned from the representativeness heuristic is this: Instead of judging something based on its qualities alone, consider the underlying probabilities and try not to make too many assumptions.
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