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

Élise PAYZAN LE Nestour
Additional contact information
Élise PAYZAN LE Nestour: Swiss Finance Institute at the École Polytechnique Fédérale de Lausanne (EPFL)
Authors registered in the RePEc Author Service: Elise Payzan-LeNestour

No 10-28, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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.

Keywords: Decision-making; Uncertainty; Cognitive Processes; Adaptation; Unstable Conditions; Bayesian Learning (search for similar items in EconPapers)
JEL-codes: C53 C91 D83 D87 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2010-06
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://ssrn.com/abstract=1628657 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1028

Access Statistics for this paper

More papers in Swiss Finance Institute Research Paper Series from Swiss Finance Institute Contact information at EDIRC.
Bibliographic data for series maintained by Ridima Mittal ().