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Expedient and Monotone Learning Rules

Author

Listed:
  • Tilman Borgers
  • Antonio Morales
  • Rajiv Sarin

Abstract

This paper considers learning rules for environments in which little prior and feedback information is available to the decision-maker. Two properties of such learning rules, absolute expediency and monotonicity, are studied. The paper provides some necessary and some sufficient conditions for these properties. A number of examples show that there is quite a large variety of learning rules which have these properties. It is also shown that all learning rules that have these properties are, in some sense, related to replicator dynamics of evolutionary game theory.
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Suggested Citation

  • Tilman Borgers & Antonio Morales & Rajiv Sarin, 2010. "Expedient and Monotone Learning Rules," Levine's Working Paper Archive 625018000000000099, David K. Levine.
  • Handle: RePEc:cla:levarc:625018000000000099
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    References listed on IDEAS

    as
    1. Samuelson, L., 1989. "Evolutionnary Stability In Asymmetric Games," Papers 11-8-2, Pennsylvania State - Department of Economics.
    2. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
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    4. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
    5. David Easley & Aldo Rustichini, 1999. "Choice without Beliefs," Econometrica, Econometric Society, vol. 67(5), pages 1157-1184, September.
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    7. 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 848-881, September.
    8. Borgers, Tilman & Sarin, Rajiv, 2000. "Naive Reinforcement Learning with Endogenous Aspirations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 921-950, November.
    9. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    10. John G. Cross, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 87(2), pages 239-266.
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    Citations

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    Cited by:

    1. Agastya, Murali & Slinko, Arkadii, 2015. "Dynamic choice in a complex world," Journal of Economic Theory, Elsevier, vol. 158(PA), pages 232-258.
    2. Jonas Hedlund & Carlos Oyarzun, 2018. "Imitation in heterogeneous populations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(4), pages 937-973, June.
    3. Bouwe R. Dijkstra, 2011. "Good and Bad Equilibria with the Informal Sector," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 167(4), pages 668-685, December.
    4. Carlos Oyarzun & Johannes Ruf, 2009. "Monotone imitation," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 41(3), pages 411-441, December.
    5. Karl H. Schlag, 2007. "Distribution-Free Learning," Economics Working Papers ECO2007/01, European University Institute.
    6. Hopkins, Ed, 2007. "Adaptive learning models of consumer behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 64(3-4), pages 348-368.
    7. Oyarzun, Carlos & Sarin, Rajiv, 2012. "Mean and variance responsive learning," Games and Economic Behavior, Elsevier, vol. 75(2), pages 855-866.
    8. Mengel Friederike & Rivas Javier, 2012. "An Axiomatization of Learning Rules when Counterfactuals are not Observed," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 12(1), pages 1-19, July.
    9. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    10. Oyarzun, Carlos & Ruf, Johannes, 2014. "Convergence in models with bounded expected relative hazard rates," Journal of Economic Theory, Elsevier, vol. 154(C), pages 229-244.
    11. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    12. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.
    13. Antonio Morales & Pablo Brañas Garza, 2003. "Computational Errors in Guessing Games1," Economic Working Papers at Centro de Estudios Andaluces E2003/11, Centro de Estudios Andaluces.
    14. Lahkar, Ratul & Seymour, Robert M., 2013. "Reinforcement learning in population games," Games and Economic Behavior, Elsevier, vol. 80(C), pages 10-38.
    15. Oyarzun, Carlos, 2014. "A note on absolutely expedient learning rules," Journal of Economic Theory, Elsevier, vol. 153(C), pages 213-223.
    16. Rivas, Javier, 2013. "Cooperation, imitation and partial rematching," Games and Economic Behavior, Elsevier, vol. 79(C), pages 148-162.
    17. Oyarzun, Carlos & Sanjurjo, Adam & Nguyen, Hien, 2017. "Response functions," European Economic Review, Elsevier, vol. 98(C), pages 1-31.
    18. John Huyck & Raymond Battalio & Frederick Rankin, 2007. "Selection dynamics and adaptive behavior without much information," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 33(1), pages 53-65, October.
    19. Antonio J. Morales Siles, 2002. "Absolute Expediency and Imitative Behaviour," Economic Working Papers at Centro de Estudios Andaluces E2002/03, Centro de Estudios Andaluces.
    20. Norman, Thomas W.L., 2023. "Pigouvian algorithmic platform design," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 322-332.

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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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