When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect
We examine decision-making under risk and uncertainty in a laboratory experiment. The heart of our design examines how oneâ€™s propensity to use Bayesâ€™ rule is affected by whether this rule is aligned with reinforcement or clashes with it. In some cases, we create environments where Bayesian updating after a successful outcome should lead a decision-maker to make a change, while no change should be made after observing an unsuccessful outcome. We observe striking patterns: When payoff reinforcement and Bayesian updating are aligned, nearly all people respond as expected. However, when these forces clash, around 50% of all decisions are inconsistent with Bayesian updating. While people tend to make costly initial choices that eliminate complexity in a subsequent decision, we find that complexity alone cannot explain our results. Finally, when a draw provides only information (and no payment), switching errors occur much less frequently, suggesting that the â€˜emotional reinforcementâ€™ (affect) induced by payments is a critical factor in deviations from Bayesian updating. There is considerable behavioral heterogeneity; we identify different types in the population and find that people who make â€˜switching errorsâ€™ are more likely to have cross-period â€˜reinforcementâ€™ tendencies.
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University of California at Santa Barbara, Economics Working Paper Series
qt7g63k28w, Department of Economics, UC Santa Barbara.
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