When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect

We examine decision-making under risk in a laboratory experiment. The heart of our design examines how one's propensity to use Bayes' 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...

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Vydáno v:The American economic review Ročník 95; číslo 4; s. 1300 - 1309
Hlavní autoři: Charness, Gary, Levin, Dan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Nashville American Economic Association 01.09.2005
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ISSN:0002-8282, 1944-7981
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Abstract We examine decision-making under risk in a laboratory experiment. The heart of our design examines how one's propensity to use Bayes' 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. On the other hand, when these forces clash, around 50% of all decisions are inconsistent with Bayesian updating; a slight increase in the precision of the information and decrease in the complexity of the calculations does not lower the error rate. However, when a draw provides only information (and no payment), switching errors occur much less frequently, suggesting that the "emotional reinforcement" (affect) induced by payments is a critical factor in deviations from Bayesian updating. We also find considerable behavioral heterogeneity across the population. Finally, we see that people have a "taste for consistency" as voluntary draws are more likely to be repeated than draws that were required. [PUBLICATION ABSTRACT]
AbstractList We examine decision-making under risk in a laboratory experiment. The heart of our design examines how one's propensity to use Bayes' 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. On the other hand, when these forces clash, around 50% of all decisions are inconsistent with Bayesian updating; a slight increase in the precision of the information and decrease in the complexity of the calculations does not lower the error rate. However, when a draw provides only information (and no payment), switching errors occur much less frequently, suggesting that the "emotional reinforcement" (affect) induced by payments is a critical factor in deviations from Bayesian updating. We also find considerable behavioral heterogeneity across the population. Finally, we see that people have a "taste for consistency" as voluntary draws are more likely to be repeated than draws that were required. [PUBLICATION ABSTRACT]
Author Levin, Dan
Charness, Gary
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Snippet We examine decision-making under risk in a laboratory experiment. The heart of our design examines how one's propensity to use Bayes' rule is affected by...
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SubjectTerms Bayes rule
Bayesian analysis
Bayesian method
Binomials
Cost efficiency
Decision making
Decision theory
Economic costs
Economic theory
Economics
Emotions
Error rates
Errors
Expected utility
Experiments
Game theory
Heuristic
Heuristics
Investors
Laboratories
Payments
Psychological reinforcement
Reinforcement
Risk
Shorter Papers
Studies
Success
Utility theory
Title When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect
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