Associative Learning and Representativeness

59 Pages Posted: 12 Jun 2020 Last revised: 24 Jun 2020

See all articles by Jessica A. Wachter

Jessica A. Wachter

University of Pennsylvania - Finance Department; National Bureau of Economic Research (NBER)

Michael J. Kahana

University of Pennsylvania - Department of Psychology

Date Written: June 23, 2020

Abstract

The representativeness heuristic constitutes constitutes a striking departure from Bayesian inference. According to a strong form of the heuristic, agents reverse a conditioning argument: for example inferring that a patient is more likely than not to have a disease, conditional on a positive test result. The correct inference is that a positive test result is more likely than not, conditional on disease. Recent research implicates representativeness in a wide range of financial market anomalies, with consequences for the real economy. However, the cognitive foundations of representativeness heuristic (RH) remain unknown. Here, we show that the RH emerges from a model of associative memory, leading to a cognitive foundation for the RH, and a means of integrating the RH into economic models involving decision-making under uncertainty.

Keywords: Context, Memory, Diagnostic expectations

JEL Classification: E03, G02

Suggested Citation

Wachter, Jessica A. and Kahana, Michael J., Associative Learning and Representativeness (June 23, 2020). Available at SSRN: https://ssrn.com/abstract=3602327

Jessica A. Wachter (Contact Author)

University of Pennsylvania - Finance Department ( email )

The Wharton School
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Philadelphia, PA 19104
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National Bureau of Economic Research (NBER)

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Michael J. Kahana

University of Pennsylvania - Department of Psychology ( email )

3815 Walnut Street
Philadelphia, PA 19104-6196
United States

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