The Similarity Heuristic

43 Pages Posted: 16 Nov 2007 Last revised: 29 Sep 2012

See all articles by Daniel Read

Daniel Read

University of Warwick - Warwick Business School

Yael Grushka-Cockayne

University of Virginia - Darden School of Business

Date Written: 2007


Decision makers often make snap judgments using fast-and-frugal decision rules called cognitive heuristics. Research into cognitive heuristics has been divided into two camps. One camp has emphasized the limitations and biases produced by the heuristics; another has focused on the accuracy of heuristics and their ecological validity. In this paper we investigate a heuristic proposed by the first camp, using the methods of the second. We investigate a subset of the representativeness heuristic we call the “similarity” heuristic, whereby decision makers who use it judge the likelihood that an instance is a member of one category rather than another by the degree to which it is similar to others in that category. We provide a mathematical model of the heuristic and test it experimentally in a trinomial environment. In this domain, the similarity heuristic turns out to be a reliable and accurate choice rule and both choice and response time data suggest it is also how choices are made. We conclude with a theoretical discussion of how our work fits in the broader ‘fast-and-frugal’ heuristics program, and of the boundary conditions for the similarity heuristic.

Keywords: heuristics and biases, fast-and-frugal heuristics, similarity, representative design, base-rate neglect, Bayesian inference

Suggested Citation

Read, Daniel and Grushka-Cockayne, Yael, The Similarity Heuristic (2007). Available at SSRN: or

Daniel Read (Contact Author)

University of Warwick - Warwick Business School ( email )

Coventry CV4 7AL
United Kingdom

Yael Grushka-Cockayne

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics