Rational Inattention to Discrete Choices: A New Foundation for the Multinomial Logit Model

Individuals must often choose among discrete actions with imperfect information about their payoffs. Before choosing, they have an opportunity to study the payoffs, but doing so is costly. This creates new choices such as the number of and types of questions to ask. We model these situations using the rational inattention approach to information frictions. We find that the decision maker's optimal strategy results in choosing probabilistically in line with a generalized multinomial logit model, which depends both on the actions' true payoffs as well as on prior beliefs.