Predicting Choice from Information Costs

Abstract

An agent acquires a costly flexible signal before making a decision. We explore the degree to which 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, for most utility functions, knowing both the utility and information costs enables a unique behavioral prediction. Finally, we show that for smooth costs, most choices from a menu uniquely pin down the agent's decisions in all submenus.

The Future Economics of Artificial Intelligence: Mythical Agents, a Singleton and the Dark Forest

“…It is also consistent with Sims (2003)'s "rationally inattentive utility maximization" where paying more attention to making a decision implies high attention costs. The point is that learning is costly, it bounds rationality, and needs to be taken into account in models of bounded rational decision-making (Lipnowski and Doron, 2022;Pattanayak and Krishnamurthy, 2021).…”