Efficient Coding and Risky Choice

74 Pages Posted: 5 Nov 2018 Last revised: 3 Aug 2021

See all articles by Cary Frydman

Cary Frydman

University of Southern California - Marshall School of Business

Lawrence J. Jin

California Institute of Technology

Date Written: August 2, 2021

Abstract

We experimentally test a theory of risky choice in which the perception of a lottery payoff is noisy due to information processing constraints in the brain. We model perception using the principle of efficient coding, which implies that perception is most accurate for those payoffs that occur most frequently. Across two pre-registered laboratory experiments, we manipulate the distribution from which payoffs in the choice set are drawn. In our first experiment, we find that risk taking is more sensitive to payoffs that are presented more frequently. In a follow-up task, we incentivize subjects to classify which of two symbolic numbers is larger. Subjects exhibit higher accuracy and faster response times for numbers they have observed more frequently. In our second experiment, we manipulate the payoff distribution so that efficient coding modulates the strength of valuation biases. As we experimentally increase the frequency of large payoffs, we find that subjects perceive the upside of a risky lottery more accurately and take greater risk. Together, our experimental results suggest that risk taking depends systematically on the payoff distribution to which the decision maker's perceptual system has recently adapted. More broadly, our findings highlight the importance of imprecise and efficient coding in economic decision-making.

Keywords: efficient coding, perception, risky choice, neuroeconomics

JEL Classification: G02, G41, D81, D87

Suggested Citation

Frydman, Cary and Jin, Lawrence J., Efficient Coding and Risky Choice (August 2, 2021). Quarterly Journal of Economics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3270773 or http://dx.doi.org/10.2139/ssrn.3270773

Cary Frydman

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA California 90089
United States

Lawrence J. Jin (Contact Author)

California Institute of Technology ( email )

1200 E. California Blvd.
MC 228-77
Pasadena, CA 91125
United States
626-395-4558 (Phone)

HOME PAGE: http://www.hss.caltech.edu/content/lawrence-jin

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

Paper statistics

Downloads
324
Abstract Views
1,963
rank
117,381
PlumX Metrics