Predicting Choice from Information Costs

TL;DR

This paper investigates how knowledge of an agent's information acquisition costs can predict decision-making behavior, revealing limitations and proposing methods to test cost models using multi-menu data.

Contribution

It introduces a framework for testing information cost models, establishes an impossibility result for cost-only predictions, and develops tractable techniques for empirical validation.

Findings

Costs alone do not restrict choices without utility constraints.

Choices from menus can identify decision rules.

Proposes tests for multi-menu data consistency with cost models.

Abstract

An agent acquires a costly flexible signal before making a decision. We explore to what degree knowledge of the agent's information costs helps predict her behavior. We establish an impossibility result: learning costs alone generate no testable restrictions on choice without also imposing constraints on actions' state-dependent utilities. By contrast, choices from a menu often uniquely pin down the agent's decisions in all submenus. To prove the latter result, we define iteratively differentiable cost functions, a tractable class amenable to first-order techniques. Finally, we construct tight tests for a multi-menu data set to be consistent with a given cost.