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Efficient Coding and Risky Choice

Frydman, Cary and Jin, Lawrence J. (2022) Efficient Coding and Risky Choice. Quarterly Journal of Economics, 137 (1). pp. 161-213. ISSN 0033-5533. doi:10.1093/qje/qjab031.

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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 preregistered 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.

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Additional Information:© The Author(s) 2021. Published by Oxford University Press on behalf of the President and Fellows of Harvard College. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( Advance Access publication on August 19, 2021. We are grateful to Andrei Shleifer (the editor), four anonymous referees, Nicholas Barberis, Nicola Gennaioli, Katrin Gödker, Alex Imas, Shimon Kogan, John O’Doherty, Stavros Panageas, Antonio Rangel, Elke Weber, Michael Woodford, and seminar participants at Caltech, the Chinese University of Hong Kong, Harvard University, the London School of Economics, the National University of Singapore, the Ohio State University, Tsinghua University, the University of Hong Kong, the University of Mannheim, the University of New South Wales, the University of Notre Dame, the University of Technology Sydney, the University of Utah, the University of California, San Diego, the University of Pennsylvania, the University of Southern California, the University of Texas at Dallas, the University of Warwick, the University of Zurich, Washington University in St. Louis, Yale University, the Behavioral Economics Annual Meeting, the Chicago Booth Conference in Behavioral Finance and Decision Making, the Kentucky Finance Conference, the LA Finance Day Conference, the NBER Behavioral Finance Meeting, the Society for Neuroeconomics Conference, and the Sloan-Nomis Workshop on the Cognitive Foundations of Economic Behavior for helpful comments. Frydman acknowledges financial support from the National Science Foundation (Grant #1749824); Jin acknowledges financial support from the Linde Institute at Caltech. Data Availability. Data and code replicating the tables and figures in this article can be found in Frydman and Jin (2021) in the Harvard Dataverse,
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Linde Institute of Economic and Management ScienceUNSPECIFIED
Subject Keywords:efficient coding, perception, risky choice, neuroeconomics
Issue or Number:1
Classification Code:JEL classification: G02, G41
Record Number:CaltechAUTHORS:20220309-966814000
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Official Citation:Cary Frydman, Lawrence J Jin, Efficient Coding and Risky Choice, The Quarterly Journal of Economics, Volume 137, Issue 1, February 2022, Pages 161–213,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113856
Deposited By: George Porter
Deposited On:10 Mar 2022 21:24
Last Modified:10 Mar 2022 21:24

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