ABSTRACT
The heuristics and biases approach to the study of judgment and decision making originated in the work of Daniel Kahneman and Amos Tversky in several publications in the 1970s and 1980s (for example, Gilovich, Griffin, & Kahneman, 2002; Kahneman, Slovic, & Tversky, 1982; Kahneman & Tversky, 2000). The idea was to compare judgments and decisions in laboratory experiments to normative models and look for systematic departures, which were called biases. Normative models were specifications of the right answer, the standard for evaluation of the responses. Examples of normative models were probability theory, statistics, and expected-utility theory. Many of the biases could be explained in terms of the use of heuristics, that is, rules that are not guaranteed to produce normative responses but were used because they usually approximated such outcomes. Yet sometimes they were quite misleading. An example is judging the probability that an example is a member of a category in terms of the similarity of the example to the category, the representativeness heuristic. This heuristic is often useful but it ignores the size of the category and is thus misleading when possible categories differ in size.
