Efficient Coding and Risky Choice

74 Pages Posted: 5 Nov 2018 Last revised: 10 Jul 2019

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: July 7, 2019


We present a model of risky choice in which the decision maker (DM) perceives a lottery payoff with noise due to the brain's limited capacity to represent information. We model perception using the principle of efficient coding, which implies that perception is most precise for frequently occurring stimuli. Our model shows that it is efficient for risk taking to be more sensitive to those payoffs that the DM encounters more frequently. The model generates a value function and a probability weighting function that are similar to those in prospect theory, but it also predicts that the DM's value function fluctuates with the recently encountered distribution of payoffs. To test the model, we manipulate the distribution of payoffs in a laboratory experiment. We find that risk taking is indeed more sensitive to those payoffs that are presented more frequently. We then conduct an additional experiment to test the key driving mechanism of our model, namely that the perception of a payoff is noisy and depends on the recent environment. In this second experiment, we incentivize subjects to classify which of two symbolic numbers is larger. We find that subjects exhibit higher accuracy for those numbers that they have observed more frequently. Overall, our experimental results suggest that risk taking depends systematically on the payoff distribution to which the DM's perceptual system has recently adapted.

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 (July 7, 2019). 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 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

Register to save articles to
your library


Paper statistics

Abstract Views
PlumX Metrics