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Observational and Reinforcement Pattern-learning: An Exploratory Study

Author

Listed:
  • Nobuyuki Hanaki

    (Université Côte d'Azur
    GREDEG CNRS
    Skema Business School
    IUF)

  • Alan Kirman

    (CAMS-EHESS
    Aix Marseille University)

  • Paul Pezanis-Christou

    (School of Economics, University of Adelaide)

Abstract

We examine, experimentally and theoretically in a very simple multi-armed bandit framework, how individuals learn about an undisclosed inter-temporal payoff structure. We propose a baseline reinforcement learning model that allows for pattern-recognitions and associated change in the strategy space, as well as its three augmented versions that accommodate observational learning from the actions and/or payoffs of another player with whom they are matched. The models reproduce the distributional properties of observed discovery times well. Our study further shows that observing another's actions and/or payoffs improves discovery compared to the baseline case when one of the pair discovered the hidden pattern.

Suggested Citation

  • Nobuyuki Hanaki & Alan Kirman & Paul Pezanis-Christou, 2016. "Observational and Reinforcement Pattern-learning: An Exploratory Study," GREDEG Working Papers 2016-24, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), University of Nice Sophia Antipolis, revised Jun 2017.
  • Handle: RePEc:gre:wpaper:2016-24
    as

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    File URL: http://www.gredeg.cnrs.fr/working-papers/GREDEG-WP-2016-24.pdf
    File Function: Revised version, 2017-06
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Multi-armed bandit; reinforcement learning; eureka moment; pay-off patterns; observational learning;

    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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