Judgment under uncertainty: Heuristics and biases


Authors: 
Tversky, A., & Kahnman, D.
Category: 
Volume: 
185
Pages: 
1124-1131
Year: 
1974
Publisher: 
Science
Abstract: 

This article described three heuristics that are employed in making judgments under uncertainty: (i) representativness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) 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; and (iii) adjustment from an anchor, 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.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education

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