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A Note on Optimal Experimentation under Risk Aversion

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  • Vladimir Novak
  • Tim Willems

Abstract

This paper solves the two-armed bandit problem when decision makers are risk averse. It shows, counterintuitively, that a more risk-averse decision maker might be more willing to take risky actions. The reason relates to the fact that pulling the risky arm in bandit models produces information on the environment – thereby reducing the risk that a decision maker will face in the future. This finding gives reason for caution when inferring risk preferences from observed actions: in a bandit setup, observing a greater appetite for risky actions can actually be indicative of more risk aversion, not less. Studies which do not take this into account may produce biased estimates.

Suggested Citation

  • Vladimir Novak & Tim Willems, 2018. "A Note on Optimal Experimentation under Risk Aversion," CERGE-EI Working Papers wp618, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  • Handle: RePEc:cer:papers:wp618
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    References listed on IDEAS

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    More about this item

    Keywords

    experimentation; learning; risk aversion;

    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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