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Prolonged Learning and Hasty Stopping: The Wald Problem With Ambiguity

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  • Sarah Auster
  • Yeon-Koo Che
  • Konrad Mierendorff

Abstract

This paper studies sequential information acquisition by an ambiguity-averse decision maker (DM), who decides how long to collect information before taking an irreversible action. The agent optimizes against the worst-case belief and updates prior by prior. We show that the consideration of ambiguity gives rise to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment excessively when facing modest uncertainty and, to counteract it, may stop experimenting prematurely when facing high uncertainty. In the latter case, the DM’s stopping rule is non-monotonic in beliefs and features randomized stopping.

Suggested Citation

  • Sarah Auster & Yeon-Koo Che & Konrad Mierendorff, 2022. "Prolonged Learning and Hasty Stopping: The Wald Problem With Ambiguity," CRC TR 224 Discussion Paper Series crctr224_2022_366, University of Bonn and University of Mannheim, Germany.
    • Handle: RePEc:bon:boncrc:crctr224_2022_366
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    References listed on IDEAS

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    1. Nicholas C. Barberis, 2009. "A Model of Casino Gambling," NBER Working Papers 14947, National Bureau of Economic Research, Inc.
    2. Subir Bose & Ludovic Renou, 2014. "Mechanism Design With Ambiguous Communication Devices," Econometrica, Econometric Society, vol. 82(5), pages 1853-1872, September.
    3. Nicholas Barberis, 2012. "A Model of Casino Gambling," Management Science, INFORMS, vol. 58(1), pages 35-51, January.
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    Cited by:

    1. Sarah Auster & Christian Kellner, 2023. "Timing Decisions Under Model Uncertainty," CRC TR 224 Discussion Paper Series crctr224_2023_460, University of Bonn and University of Mannheim, Germany.
    2. Sarah Auster & Christian Kellner, 2023. "Timing Decisions under Model Uncertainty," ECONtribute Discussion Papers Series 252, University of Bonn and University of Cologne, Germany.

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    More about this item

    Keywords

    Wald Problem; Ambiguity Aversion;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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