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An Experiment on Learning in a Multiple Games Environment

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Author Info
Grimm, Veronika
Mengel, Friederike (METEOR)

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Abstract

We study experimentally how players learn to make decisions if they face many different (normal-form) games. Games are generated randomly from a uniform distribution in each of 100 rounds. We find that agents do extrapolate between games but learn to play strategically equivalent games in the same way. If either there are few games or if explicit information about the opponent''s behavior is provided (or both) convergence to the unique Nash equilibrium generally occurs. Otherwise this is not the case and play converges to a distribution of actions which is Non-Nash. Action choices, though, that cannot be explained by theoretical models of either belief-bundling or action bundling are never observed. Estimating different learning models we find that Nash choices are best explained by finer categorizations than Non-Nash choices. Furthermore participants scoring better in the "Cognitive Reflection Test" choose Nash actions more often than other participants.

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Paper provided by Maastricht : METEOR, Maastricht Research School of Economics of Technology and Organization in its series Research Memoranda with number 007.

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Date of creation: 2009
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Handle: RePEc:dgr:umamet:2009007

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Keywords: microeconomics ;

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  1. Rustichini, Aldo, 1999. "Optimal Properties of Stimulus--Response Learning Models," Games and Economic Behavior, Elsevier, vol. 29(1-2), pages 244-273, October. [Downloadable!] (restricted)
  2. Abreu, Dilip & Rubinstein, Ariel, 1988. "The Structure of Nash Equilibrium in Repeated Games with Finite Automata," Econometrica, Econometric Society, vol. 56(6), pages 1259-81, November. [Downloadable!] (restricted)
  3. Jakub Steiner & Colin Stewart, 2007. "Learning by Similarity in Coordination Problems," CERGE-EI Working Papers wp324, The Center for Economic Research and Graduate Education - Economic Institute, Prague. [Downloadable!]
  4. Ed Hopkins, 2002. "Two Competing Models of How People Learn in Games," Econometrica, Econometric Society, vol. 70(6), pages 2141-2166, November. [Downloadable!] (restricted)
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  5. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
  6. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January. [Downloadable!] (restricted)
  7. Sarin, Rajiv & Vahid, Farshid, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 28(2), pages 294-309, August. [Downloadable!] (restricted)
  8. Shane Frederick, 2005. "Cognitive Reflection and Decision Making," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 25-42, Fall. [Downloadable!] (restricted)
  9. Gilboa, Itzhak & Schmeidler, David, 1996. "Case-Based Optimization," Games and Economic Behavior, Elsevier, vol. 15(1), pages 1-26, July. [Downloadable!] (restricted)
    Other versions:
    • Itzhak Gilboa & David Schmeidler, 1993. "Case-Based Optimization," Discussion Papers 1039, Northwestern University, Center for Mathematical Studies in Economics and Management Science. [Downloadable!]
  10. Hopkins, Ed, 2007. "Adaptive learning models of consumer behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 64(3-4), pages 348-368. [Downloadable!] (restricted)
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This page was last updated on 2009-11-18.

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