Posterior-Mean Separable Costs of Information Acquisition

TL;DR

This paper investigates how to identify linear costs of information acquisition based on observed choice data, enabling the application of information design to decision-making problems.

Contribution

It provides testable conditions to verify the assumption of linear costs of learning, facilitating the use of information design techniques in belief-dependent decision models.

Findings

Testable conditions for linear cost assumption

Method to identify belief-dependent learning costs

Application of information design in stochastic choice data

Abstract

We analyze a problem of revealed preference given state-dependent stochastic choice data in which the payoff to a decision maker (DM) only depends on their beliefs about posterior means. Often, the DM must also learn about or pay attention to the state; in applied work on this subject, a convenient assumption is that the costs of such learning are linearly dependent in the distribution over posterior means. We provide testable conditions to identify whether this assumption holds. This allows for the use of information design techniques to solve the DM's problem.