A Note on Optimal Experimentation under Risk Aversion
Vladimír Novák () and
CERGE-EI Working Papers from The Center for Economic Research and Graduate Education - Economics Institute, Prague
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.
Keywords: experimentation; learning; risk aversion (search for similar items in EconPapers)
JEL-codes: D81 D83 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-exp, nep-mic and nep-upt
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Journal Article: A note on optimal experimentation under risk aversion (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:cer:papers:wp618
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