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

  • Gary Charness
  • Dan Levin

We examine decision-making under risk and uncertainty 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. 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 ‘emotional reinforcement’ (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 ‘switching errors’ are more likely to have cross-period ‘reinforcement’ tendencies.

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Article provided by American Economic Association in its journal American Economic Review.

Volume (Year): 95 (2005)
Issue (Month): 4 (September)
Pages: 1300-1309

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Handle: RePEc:aea:aecrev:v:95:y:2005:i:4:p:1300-1309
Note: DOI: 10.1257/0002828054825583
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  1. Grether, David M, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, MIT Press, vol. 95(3), pages 537-57, November.
  2. Samuelson, William & Zeckhauser, Richard, 1988. " Status Quo Bias in Decision Making," Journal of Risk and Uncertainty, Springer, vol. 1(1), pages 7-59, March.
  3. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, June.
  4. Charness, Gary & Levin, Dan, 2003. "When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect," University of California at Santa Barbara, Economics Working Paper Series qt7g63k28w, Department of Economics, UC Santa Barbara.
  5. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
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  10. Zizzo, Daniel John & Stolarz-Fantino, Stephanie & Wen, Julie & Fantino, Edmund, 2000. "A violation of the monotonicity axiom: experimental evidence on the conjunction fallacy," Journal of Economic Behavior & Organization, Elsevier, vol. 41(3), pages 263-276, March.
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  12. Cheung, Yin-Wong & Friedman, Daniel, 1997. "Individual Learning in Normal Form Games: Some Laboratory Results," Games and Economic Behavior, Elsevier, vol. 19(1), pages 46-76, April.
  13. Camerer, Colin & Ho, Teck-Hua, 1997. "Experience-Weighted Attraction Learning in Games: A Unifying Approach," Working Papers 1003, California Institute of Technology, Division of the Humanities and Social Sciences.
  14. Ouwersloot, Hans & Nijkamp, Peter & Rietveld, Piet, 1998. "Errors in probability updating behaviour : Measurement and impact analysis," Journal of Economic Psychology, Elsevier, vol. 19(5), pages 535-563, October.
  15. Loomes, Graham & Sugden, Robert, 1982. "Regret Theory: An Alternative Theory of Rational Choice under Uncertainty," Economic Journal, Royal Economic Society, vol. 92(368), pages 805-24, December.
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