Predicting Choice from Information Costs

Elliot Lipnowski and Doron Ravid

Papers from arXiv.org

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.

Date: 2022-05, Revised 2023-04
New Economics Papers: this item is included in nep-dcm and nep-upt
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