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Naive Reinforcement Learning with Endogenous Aspirations

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Author Info
Borgers, Tilman
Sarin, Rajiv

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Abstract

This article considers a simple model of reinforcement learning. All behavior change derives from the reinforcing or deterring effect of instantaneous payoff experiences. Payoff experiences are reinforcing or deterring depending on whether the paxoff exceeds an aspiration level or falls short of it. Over time, the aspiration level is adjusted toward the actually experienced payoffs. This article shows that aspiration level adjustments may improve the decision maker's long-run performance by preventing him or her from feeling dissatisfied with even the best available strategies. However, such movements also lead to persistent deviations from expected payoff maximization by creating "probability matching" effects. Copyright 2000 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

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Publisher Info
Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.

Volume (Year): 41 (2000)
Issue (Month): 4 (November)
Pages: 921-50
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Handle: RePEc:ier:iecrev:v:41:y:2000:i:4:p:921-50

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  1. Dana Heller, 2000. "Parametric Adaptive Learning," Econometric Society World Congress 2000 Contributed Papers 1496, Econometric Society. [Downloadable!]
  2. Guerdjikova, Ani, 2006. "Portfolio Choice and Asset Prices in an Economy Populated by Case-Based Decision Makers," Working Papers 06-13, Cornell University, Center for Analytic Economics. [Downloadable!]
  3. Gabriel Galand, 2009. "The Neutrality of Money Revisited with a Bottom-Up Approach: Decentralisation, Limited Information and Bounded Rationality," Computational Economics, Springer, vol. 33(4), pages 337-360, May. [Downloadable!] (restricted)
  4. Ed Hopkins, 2006. "Adaptive Learning Models of Consumer Behaviour," Levine's Bibliography 122247000000000658, UCLA Department of Economics. [Downloadable!]
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  5. Droste, E. & Kosfeld, M. & Voorneveld, M., 1998. "A myopic adjustment process leading to best-reply matching," Discussion Paper 111, Tilburg University, Center for Economic Research. [Downloadable!]
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  6. Tilman Börgers & Rajiv Sarin & Antonio J. Morales, 2001. "Expedient and Monotone Learning Rules," Economic Working Papers at Centro de Estudios Andaluces E2001/06, Centro de Estudios Andaluces. [Downloadable!]
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  7. Dean P Foster & Peyton Young, 2006. "Regret Testing Leads to Nash Equilibrium," Levine's Working Paper Archive 784828000000000676, David K. Levine. [Downloadable!]
  8. F. de Vries, 1999. "The Behavioral Firm and Its Internal Game: Evolutionary Dynamics of Decision Making," Working Papers ir99036, International Institute for Applied Systems Analysis. [Downloadable!]
  9. Mikhael Shor, 2003. "Learning to Respond: The Use of Heuristics in Dynamic Games," Game Theory and Information 0301001, EconWPA. [Downloadable!]
  10. Friederike Mengel, 2007. "Learning Across Games," Working Papers. Serie AD 2007-05, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie). [Downloadable!]
  11. 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. [Downloadable!]
  12. Drew Fudenberg & David K Levine, 2006. "An Economists Perspective on Multi-Agent Learning," Levine's Working Paper Archive 784828000000000683, David K. Levine. [Downloadable!]
  13. Ed Hopkins, 2004. "Adaptative Learning Models of Consumer Behaviour (first version)," ESE Discussion Papers 80, Edinburgh School of Economics, University of Edinburgh.
  14. Droste, E., 1999. "Habit formation and the evolution of social communication networks," Discussion Paper 50, Tilburg University, Center for Economic Research. [Downloadable!]
  15. John Duffy, 2004. "Agent-Based Models and Human Subject Experiments," Computational Economics 0412001, EconWPA. [Downloadable!]
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