Efficient Coding and Risky Choice
74 Pages Posted: 5 Nov 2018 Last revised: 10 Jul 2019
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: Suggested Citation