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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.

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Bibliographic Info

Paper provided by Swiss Finance Institute in its series Swiss Finance Institute Research Paper Series with number 10-28.

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Length: 23 pages
Date of creation: Jun 2010
Date of revision:
Handle: RePEc:chf:rpseri:rp1028

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Web page: http://www.SwissFinanceInstitute.ch
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Related research

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

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References

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Cited by:
  1. Kuhnen, Camelia M., 2012. "Asymmetric learning from financial information," MPRA Paper 39412, University Library of Munich, Germany.

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