Predicting Choice from Information Costs
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.
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