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Recency, Records and Recaps: Learning and Non-Equilibrium Behavior in a Simple Decision Problem

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  • Drew Fudenberg
  • Peysakhovich, A

Abstract

Nash equilibrium takes optimization as a primitive, but suboptimal behavior can persist in simple stochastic decision problems. This has motivated the development of other equilibrium concepts such as cursed equilibrium and behavioral equilibrium. We experimentally study a simple adverse selection (or “lemons†) problem and find that learning models that heavily discount past information (i.e. display recency bias) explain patterns of behavior better than Nash, cursed or behavioral equilibrium. Providing counterfactual information or a record of past outcomes does little to aid convergence to optimal strategies, but providing sample averages (“recaps†) gets individuals most of the way to optimality. Thus recency effects are not solely due to limited memory but stem from some other form of cognitive constraints. Our results show the importance of going beyond static optimization and incorporating features of human learning into economic models.
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  • Drew Fudenberg & Peysakhovich, A, 2014. "Recency, Records and Recaps: Learning and Non-Equilibrium Behavior in a Simple Decision Problem," Working Paper 167691, Harvard University OpenScholar.
    • Handle: RePEc:qsh:wpaper:167691
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    File URL: http://scholar.harvard.edu/fudenberg/node/167691
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    Cited by:

    1. Block, Juan I. & Fudenberg, Drew & Levine, David K., 2019. "Learning dynamics with social comparisons and limited memory," Theoretical Economics, Econometric Society, vol. 14(1), January.
    2. Emerson Melo, 2021. "Learning in Random Utility Models Via Online Decision Problems," Papers 2112.10993, arXiv.org, revised Aug 2022.
    3. Aurelie Ouss & Alexander Peysakhovich, 2015. "When Punishment Doesn't Pay: "Cold Glow" and Decisions to Punish," Journal of Law and Economics, University of Chicago Press, vol. 58(3).
    4. Drew Fudenberg & David K. Levine, 2016. "Whither Game Theory? Towards a Theory of Learning in Games," Journal of Economic Perspectives, American Economic Association, vol. 30(4), pages 151-170, Fall.
    5. Emerson Melo, 2021. "Learning In Random Utility Models Via Online Decision Problems," CAEPR Working Papers 2022-003 Classification-D, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. Scott Duke Kominers & Xiaosheng Mu & Alexander Peysakhovich, 2019. "Paying for Attention: The Impact of Information Processing Costs on Bayesian Inference," Working Papers 2019-31, Princeton University. Economics Department..
    7. Juan I Block & Drew Fudenberg & David K Levine, 2017. "Learning Dynamics Based on Social Comparisons," Levine's Working Paper Archive 786969000000001375, David K. Levine.
    8. Alexander Peysakhovich & David G. Rand, 2016. "Habits of Virtue: Creating Norms of Cooperation and Defection in the Laboratory," Management Science, INFORMS, vol. 62(3), pages 631-647, March.
    9. Nichole Szembrot, 2018. "Experimental study of cursed equilibrium in a signaling game," Experimental Economics, Springer;Economic Science Association, vol. 21(2), pages 257-291, June.
    10. Fausto Cavalli & Mario Gilli & Ahmad Naimzada, 2022. "Endogenous interdependent preferences in a dynamical contest model," Working Papers 492, University of Milano-Bicocca, Department of Economics, revised Mar 2022.
    11. Alexander Peysakhovich & Uma R. Karmarkar, 2016. "Asymmetric Effects of Favorable and Unfavorable Information on Decision Making Under Ambiguity," Management Science, INFORMS, vol. 62(8), pages 2163-2178, August.
    12. Peysakhovich, Alexander & Naecker, Jeffrey, 2017. "Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 373-384.
    13. Drew Fudenberg & David K Levine, 2016. "Whither Game Theory?," Levine's Working Paper Archive 786969000000001307, David K. Levine.

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