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The Representativeness Heuristic is a mental shortcut that helps us make decisions or judgments quickly by comparing information to our mental prototypes or stereotypes. Essentially, it's how we assess the probability of an event by how much it resembles our existing idea of a category. For example, when meeting a new person with glasses and a love of books, we might instantly categorize them as a 'bookworm' based on our mental image of what a bookish person looks like. While this heuristic can be efficient, it can also lead to cognitive biases and errors in judgment, as it often overlooks other relevant information. According to Tversky and Kahneman, who first identified this concept in the 1970s, people rely on the representativeness heuristic when they judge the likelihood that an object or event A belongs to class or process B by how closely A resembles B (Tversky & Kahneman, 1974).
Statistics in various studies have shown that the representativeness heuristic can lead to systematic errors. For instance, when people are asked to judge the probability of a woman being a bank teller versus a feminist bank teller, they often incorrectly rate the latter as more likely, due to the representativeness of the description 'feminist' fitting their stereotype of a woman more closely than just 'bank teller' (Tversky & Kahneman, 1983). This illustrates the conjunction fallacy, a type of logical fallacy that occurs as a result of the representativeness heuristic. Understanding this heuristic is crucial because it affects not only personal decision-making but also has implications in areas such as finance, law, and medicine, where judgments and decisions can have significant consequences.
The representativeness heuristic is a heuristic (rule of thumb) that has been demonstrated to be a natural part of human cognition. Like any other rule of thumb, it has pluses and minuses. The representativeness heuristic argues that people see commonality between items or people of similar appearance, or between an object and a group it appears to be a part of. For instance, a culturally ignorant Westerner might see all brown-skinned people as being part of the same group, despite there being many brown-skinned races without any relation to one another.
The studies that lead to the discovery of the representativeness heuristic were initially conducted by Amos Tversky and Daniel Kahneman in the early 1970s. Kahneman would later go on to win the 2002 Nobel Prize in Economics. To test for the representativeness heuristic, Kahneman and Tversky gave their subjects the following information:
"Tom W. is of high intelligence, although lacking in true creativity. He has a need for order and clarity, and for neat and tidy systems in which every detail finds its appropriate place. His writing is rather dull and mechanical, occasionally enlivened by somewhat corny puns and by flashes of imagination of the sci-fi type. He has a strong drive for competence. He seems to feel little sympathy for other people and does not enjoy interacting with others. Self-centered, he nonetheless has a deep moral sense."
The subjects receiving the information were then divided into three groups, each being given a different decision task:
The first group was asked how similar Tom W. sounded to nine different majors. Most subjects associated him most with an engineering major, and least with a student of social science/social work.
The second group was asked to estimate the probability that Tom W. was a member of any of nine different majors. These probabilities were closely in line with the similarity ratings given by the first group.
The third group was asked to estimate what percentages of first-year grad students were in each of the nine majors, a question completely unrelated to Tom W.
The results indicated that the 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 at the school where the study was conducted, making up substantially less than 1/9 of all students. Being misled based on representations, the subjects ignored the background probabilities of Tom W. being in any given major, notwithstanding any of his personal qualities. Extensive subsequent testing has found that this pathology is universal and applies in a wide variety of problem domains.
The lesson learned from the representativeness heuristic is this: instead of judging something based just on its qualities, consider the background probabilities and try not to make too many assumptions.