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Learning the Krepsian state: Exploration through consumption

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  • Piermont, Evan
  • Takeoka, Norio
  • Teper, Roee

Abstract

We take the Krepsian approach to provide a behavioral foundation for responsive subjective learning processes. In contrast to the standard subjective state space models, the resolution of uncertainty regarding the true state is endogenous and depends on the decision maker's actions. There need not be full resolution of uncertainty between periods. When the decision maker chooses what to consume, she also chooses the information structure to which she will be exposed. When she consumes outcomes, she learns her relative preference between them; after each consumption history, the decision maker's information structure is a refinement of the previous information structure. We provide the behavioral restrictions corresponding to a recursive representation exhibiting such a learning process. Through the incorporation of dynamics we are able to identify the set of preferences the decision maker believes possible after each history of consumption, without appealing to an environment with risk.

Suggested Citation

  • Piermont, Evan & Takeoka, Norio & Teper, Roee, 2016. "Learning the Krepsian state: Exploration through consumption," Games and Economic Behavior, Elsevier, vol. 100(C), pages 69-94.
    • Handle: RePEc:eee:gamebe:v:100:y:2016:i:c:p:69-94
    DOI: 10.1016/j.geb.2016.09.002
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    Citations

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    Cited by:

    1. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.
    2. Mihm, Maximilian & Ozbek, Kemal, 2019. "On the identification of changing tastes," Games and Economic Behavior, Elsevier, vol. 116(C), pages 203-216.
    3. Roee Teper, 2016. "Plans of Action," Working Paper 5859, Department of Economics, University of Pittsburgh.
    4. João V. Ferreira & Nicolas Gravel, 2017. "Choice with Time," AMSE Working Papers 1729, Aix-Marseille School of Economics, France.
    5. Jason Delaney & Sarah Jacobson & Thorsten Moenig, 2020. "Preference discovery," Experimental Economics, Springer;Economic Science Association, vol. 23(3), pages 694-715, September.
    6. Yosuke Hashidate, 2018. "Preferences for Randomization and Anticipated Utility," CIRJE F-Series CIRJE-F-1083, CIRJE, Faculty of Economics, University of Tokyo.
    7. Cooke, Kevin, 2017. "Preference discovery and experimentation," Theoretical Economics, Econometric Society, vol. 12(3), September.
    8. Ke, Shaowei, 2018. "Rational expectation of mistakes and a measure of error-proneness," Theoretical Economics, Econometric Society, vol. 13(2), May.
    9. Marek Kapera, 2022. "Learning own preferences through consumption," KAE Working Papers 2022-074, Warsaw School of Economics, Collegium of Economic Analysis.
    10. Krishna, R. Vijay & Sadowski, Philipp, 2021. "Randomly evolving tastes and delayed commitment," Journal of Mathematical Economics, Elsevier, vol. 92(C), pages 81-94.
    11. Piermont, Evan, 2017. "Introspective unawareness and observable choice," Games and Economic Behavior, Elsevier, vol. 106(C), pages 134-152.

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    More about this item

    Keywords

    Responsive learning; Subjective learning; Conditional preferences; Preference for flexibility;
    All these keywords.

    JEL classification:

    • 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

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