Efficient Coding and Risky Choice

Abstract

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 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.