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Individual and Social Learning

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  • Nobuyuki Hanaki

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

Suggested Citation

  • Nobuyuki Hanaki, 2005. "Individual and Social Learning," Computational Economics, Springer;Society for Computational Economics, vol. 26(3), pages 31-50, November.
    • Handle: RePEc:kap:compec:v:26:y:2005:i:3:p:31-50
    DOI: 10.1007/s10614-005-9003-5
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    References listed on IDEAS

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    Cited by:

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    2. Shu‐Heng Chen & Shu G. Wang, 2011. "Emergent Complexity In Agent‐Based Computational Economics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(3), pages 527-546, July.
    3. Liangjie Zhao & Wenqi Duan, 2014. "Simulating the Evolution of Market Shares: The Effects of Customer Learning and Local Network Externalities," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 53-70, January.
    4. 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, vol. 36(1), pages 27-46, January.
    5. Shu-Heng Chan & Shu G. Wang, 2010. "Emergent Complexity in Agent-Based Computational Economics," ASSRU Discussion Papers 1017, ASSRU - Algorithmic Social Science Research Unit.

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