Efficient Coding and Risky Choice

49 Pages Posted: 5 Nov 2018

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: October 22, 2018


We present a model of risky choice in which the perception of a lottery payoff is noisy and optimally depends on the payoff distribution to which the decision maker has adapted. The perceived value of a payoff is precisely defined according to a core idea in neuroscience called the efficient coding hypothesis, which indicates that more perceptual resources are allocated to those stimuli that occur more frequently. We show that this principle implies that, for a given choice set of lotteries, risk taking varies systematically with the recently encountered distribution of payoffs. We test our model in two laboratory experiments. In the first experiment, we manipulate the distribution from which payoffs are drawn. Consistent with efficient coding of lottery payoffs, we find that risk taking is more sensitive to payoffs that are encountered more frequently in the choice set. Furthermore, sensitivity to extreme payoffs is initially small, but grows over time after repeated exposure. Our second experiment consists of a purely perceptual task, in which subjects classify which of two symbolic numbers is larger. We find that accuracy depends on the distribution of numbers to which the subject has adapted, which provides support for our key model assumption that perception of numerical payoffs is noisy and changes across environments.

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 (October 22, 2018). 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

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