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Bayesian Learning in UnstableSettings: Experimental Evidence Based on the Bandit Problem

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  • Élise PAYZAN LE NESTOUR

    (Swiss Finance Institute at the École Polytechnique Fédérale de Lausanne (EPFL))

Abstract

We study learning in a bandit problem where the outcome probabilities of six arms switch (jump) over time a restless bandit. In the experiment, optimal Bayesian learning tracks the jumps through learning of the probability of a jump or direct jump detection and, once a jump has occurred, re-learns the outcome probabilities. Such Bayesian learning is much more complex than the natural alternative which learns through trial-and-error (adaptive expectations). Yet, when combined with a partially myopic decision rule, Bayesian learning better matches the behavior observed in the lab. This result suggests that agents may be less limited in their computational capacities than previously thought, and that complexity does not always hamper fully rational learning.

Suggested Citation

  • Élise PAYZAN LE NESTOUR, 2010. "Bayesian Learning in UnstableSettings: Experimental Evidence Based on the Bandit Problem," Swiss Finance Institute Research Paper Series 10-28, Swiss Finance Institute.
  • Handle: RePEc:chf:rpseri:rp1028
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    Cited by:

    1. Camelia M. Kuhnen, 2015. "Asymmetric Learning from Financial Information," Journal of Finance, American Finance Association, vol. 70(5), pages 2029-2062, October.

    More about this item

    Keywords

    Decision-making; Uncertainty; Cognitive Processes; Adaptation; Unstable Conditions; Bayesian Learning;

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics

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