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Individual and Social Learning

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  • Nobuyuki Hanaki

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Abstract

We use adaptive models to understand the dynamics that lead to efficient and fair outcomes in a repeated Battle of the Sexes game. Human subjects appear to easily recognize the possibility of a coordinated alternation of actions as a mean to generate an efficient and fair outcome. Yet such typical learning models as Fictitious Play and Reinforcement Learning have found it hard to replicate this particular result. We develop a model that not only uses individual learning but also the “social learning” that operates through evolutionary selection. We find that the efficient and fair outcome emerges relatively quickly in our model. Copyright Springer Science+Business Media, Inc. 2005

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File URL: http://hdl.handle.net/10.1007/s10614-005-9003-5
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 26 (2005)
Issue (Month): 3 (November)
Pages: 31-50

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Handle: RePEc:kap:compec:v:26:y:2005:i:3:p:31-50

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Web page: http://www.springerlink.com/link.asp?id=100248
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Keywords: reinforcement learning; evolutionary dynamics;

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  1. Charness, Gary & Rabin, Matthew, 2001. "Understanding Social Preferences with Simple Tests," Department of Economics, Working Paper Series, Department of Economics, Institute for Business and Economic Research, UC Berkeley qt4qz9k8vg, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  2. Barry Sopher & Dilip Mookherjee, 2000. "Learning and Decision Costs in Experimental Constant Sum Games," Departmental Working Papers, Rutgers University, Department of Economics 199625, Rutgers University, Department of Economics.
  3. 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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  6. Miller, John H., 1996. "The coevolution of automata in the repeated Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 29(1), pages 87-112, January.
  7. M. Rabin, 2001. "Incorporating Fairness into Game Theory and Economics," Levine's Working Paper Archive 511, David K. Levine.
  8. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, Econometric Society, vol. 67(4), pages 827-874, July.
  9. Hanaki, Nobuyuki & Sethi, Rajiv & Erev, Ido & Peterhansl, Alexander, 2005. "Learning strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 56(4), pages 523-542, April.
  10. Arifovic, Jasmina & McKelvey, Richard D. & Pevnitskaya, Svetlana, 2006. "An initial implementation of the Turing tournament to learning in repeated two-person games," Games and Economic Behavior, Elsevier, vol. 57(1), pages 93-122, October.
  11. 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.
  12. Jorgen W. Weibull, 1997. "Evolutionary Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262731215, December.
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Cited by:
  1. Liangjie Zhao & Wenqi Duan, 2014. "Simulating the Evolution of Market Shares: The Effects of Customer Learning and Local Network Externalities," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 43(1), pages 53-70, January.
  2. Cynthia Chen & Jason Chen, 2009. "What is responsible for the response lag of a significant change in discretionary time use: the built environment, family and social obligations, temporal constraints, or a psychological delay factor?," Transportation, Springer, Springer, vol. 36(1), pages 27-46, January.

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