Representativeness heuristic

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The representativeness heuristic is a heuristic wherein we assume commonality between objects of similar appearance. While often very useful in everyday life, it can also result in neglect of relevant base rates and other errors. The representative heuristic was first identified by Amos Tversky and Daniel Kahneman.

Two examples are commonly used when explaining this heuristic.

[edit] Tom W.

In a study done in 1973, 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.

• One group of subjects was asked how similar Tom W. was to a student in one of nine types of college graduate majors (business administration, computer science, engineering, humanities/education, law, library science, medicine, physical/life sciences, or social science/social work). Most subjects associated Tom W. with an engineering student, and thought he was least like a student of social science/social work.

• A second group of subjects was asked instead to estimate the probability that Tom W. was a grad student in each of the nine majors. The probabilities were in line with the judgments from the previous group.

• A third group of subjects was asked to estimate the proportion of first-year grad students there were in each of the nine majors.

The second group's probabilities were based mainly on how much they thought Tom W. was representative of each of the majors, and completely neglected the base rate probability of being that kind of student in the first place (the third group). Had the subjects thought about those base rates, their estimated probability that Tom W. was an engineer would have been much lower, as there were few engineering grad students at the time.

[edit] The Taxicab problem

In another study done by Tversky and Kahneman, subjects were given the following problem:

A cab was involved in a hit and run accident at night. Two cab companies, the Green and the Blue, operate in the city. 85% of the cabs in the city are Green and 15% are Blue.

A witness identified the cab as Blue. The court tested the reliability of the witness under the same circumstances that existed on the night of the accident and concluded that the witness correctly identified each one of the two colors 80% of the time and failed 20% of the time.

What is the probability that the cab involved in the accident was Blue rather than Green?

Most subjects gave probabilities over 50%, and some gave answers over 80%. The correct answer, found using Bayes' theorem, is lower than these estimates:

• There is a 12% chance (15% times 80%) of the witness correctly identifying a blue cab.
• There is a 17% chance (85% times 20%) of the witness incorrectly identifying a green cab as blue.
• There is therefore a 29% chance (12% plus 17%) the witness will identify the cab as blue.
• This results in a 41% chance (12% divided by 29%) that the cab identified as blue is actually blue.

The study's authors write, "This is an interesting insight on research psychology in relation to reality." Representativeness is cited in the similar effect of the gambler's fallacy, the regression fallacy and the conjunction fallacy. Another psychological heuristic Tversky and Kahneman found is the availability heuristic.

[edit] References

• Baron, J. (2000). Thinking and Deciding (3d ed.). Cambridge University Press.

• Kahneman, D., & Tversky, A. (1973). On the Psychology of Prediction. Psychological Review, 80, 237-251.

• Tversky, A., & Kahneman, D. (1982). Evidential Impact of Base Rates. In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press.