A Note on Optimal Experimentation Under Risk Aversion

Abstract

In a standard two-armed bandit setup, this paper 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.