Judgment under Uncertainty: Heuristics and Biases.

摘要

The paper describes three heuristics, or mental operations, that are employed in judgment under uncertainty. The first is an assessment of representativeness or similarity which is usually performed when people are asked to judge the likelihood that an object or event A belongs to a class or process B. The second is an assessment of the availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development. The third is an adjustment from a starting point, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty. (Modified author abstract)