ERRORS OF JUDGMENT AND CHOICE----- Episode 2

THE HEURISTICS THAT ARE EMPLOYED TO ASSESS 
PROBABILITIES AND TO PREDICT VALUES 

                              REPRESENTATIVENESS 

   Many of the probabilistic questions with which people are concerned belong to one of the following types : What is the probability that object A belongs to class B ? What is the probability that event A originates from process B ? What is the probability that process B will generate event A ? In answering such questions people typically rely on the representativeness heuristic, in which probabilities are evaluated by the degree to which A is representative of B, that is, by the degree to which A resembles B. For example, when A is highly representative of B, the probability that A originates from B is judged to be high. On the other hand, if A is not similar to B, the probability that A originates from B is judged to be low. 

   Think back to the example of "Steve who was described 
as very shy and withdrawn, invariably helpful, but with little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail. " How do people assess the probability that Steve is engaged in a particular occupation from a list of possibilities [ for example, farmer, salesman, airline pilot, librarian, or physician ?] How do people order these occupations from most to least likely ? In the representativeness heuristic, the probability that Steve is a librarian, for example, is assessed by the degree to which he is representative of, or similar to, the stereotype of a librarian. Indeed, research with problems of this type has shown that people order the occupations by probability and by similarity in exactly the same way. This approach is not influenced by several factors that should affect judgments of probability. 

   Insensitivity to prior probability of outcomes. One of the factors that has no effect on representativeness but should have a major effect on probability is the prior probability, or base-rate frequency of the outcomes. In the case of Steve, for example, the fact that there are many more farmers than librarians in the population should enter into any reasonable estimate of the probability that Steve is a librarian rather than a farmer. Considerations of base-rate frequency, however, do not affect the similarity of Steve to the stereotypes of librarians and farmers. If people evaluate probability by representativeness, therefore, prior probabilities will be neglected. This hypothesis was tested in an experiment where prior probabilities were manipulated. Students were shown brief personality descriptions of several individuals, allegedly sampled at random from a group of 100 professionals ---- engineers and lawyers. The subjects were asked to assess, for each description, the probability that it belonged to an engineer rather than to a lawyer. In one experimental condition, subjects were told that the group from which the description had been drawn consisted of 70 engineers and 30 lawyers. In another condition, subjects were told that the group consisted of 30 engineers and 70 lawyers. The odds that any particular description belongs to an an engineer rather than to a lawyer should be higher in the first condition, where there is a majority of engineers, than in the second condition, where there is a majority of lawyers. Specifically, it can be shown by applying Bayes' rule that the ratio of these odds should be  [.7 / .3--squared], or 5.44, for each for each description. In a sharp violation of Bayes' rule, the subjects in these two conditions produced essentially the same probability judgments. Apparently, subjects evaluated the likelihood that a particular description belonged to an engineer rather than to a lawyer by the degree to which this description was representative of the two stereotypes, with little or no regard for the prior probabilities of the categories. 

   The subjects used prior probabilities correctly when they had no other information. In the absence of a personality sketch, they judged the probability that an unknown individual is an engineer to be .7 and .3, respectively, in the two base-rate conditions. However, prior probabilities were ignored when a description was introduced even when this description was totally uninformative. 

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