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Predicting Choice from Information Costs

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  • Elliot Lipnowski
  • Doron Ravid

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.

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  • Elliot Lipnowski & Doron Ravid, 2022. "Predicting Choice from Information Costs," Papers 2205.10434, arXiv.org, revised Apr 2023.
    • Handle: RePEc:arx:papers:2205.10434
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    References listed on IDEAS

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    1. Robert J. Aumann, 1995. "Repeated Games with Incomplete Information," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262011476, December.
    2. Hong, Chew Soo & Karni, Edi & Safra, Zvi, 1987. "Risk aversion in the theory of expected utility with rank dependent probabilities," Journal of Economic Theory, Elsevier, vol. 42(2), pages 370-381, August.
    3. Andrew Caplin & Mark Dean, 2013. "Behavioral Implications of Rational Inattention with Shannon Entropy," NBER Working Papers 19318, National Bureau of Economic Research, Inc.
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    Cited by:

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    2. Mark Whitmeyer & Kun Zhang, 2023. "Redeeming Falsifiability?," Papers 2303.15723, arXiv.org.
    3. Naudé, Wim, 2023. "Artificial Intelligence and the Economics of Decision-Making," IZA Discussion Papers 16000, Institute of Labor Economics (IZA).

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