Representativeness Heuristic

Representativeness Heuristic

The representativeness heuristic is used when making judgments about the probability of an event under uncertainty. It was first proposed by Amos Tversky and Daniel Kahneman, who defined representativeness as "the degree to which (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated". When people rely on representativeness to make judgements, they are likely to judge wrongly because the fact that something is more representative does not make it more likely. This heuristic is used because it is an easy computation. The problem is that people overestimate its ability to accurately predict the likelihood of an event. Thus it can result in neglect of relevant base rates and other cognitive biases.