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Encyclopedia of Systems and Control pp 1–11Cite as

Learning in Games

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Abstract

In a Nash equilibrium, each player selects a strategy that is optimal with respect to the strategies of other players. This definition does not mention the process by which players reach a Nash equilibrium. The topic of learning in games seeks to address this issue in that it explores how simplistic learning/adaptation rules can lead to Nash equilibrium. This entry presents a selective sampling of learning rules and their long-run convergence properties, i.e., conditions under which player strategies converge or not to Nash equilibrium.

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Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, Georgia Institute of Technology, 30332, Atlanta, GA, USA

    Jeff S. Shamma

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  1. Jeff S. Shamma
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Correspondence to Jeff S. Shamma .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, Boston University, Boston, Massachusetts, USA

    John Baillieul

  2. Automation and Control Solutions, Honeywell, Golden Valley, Minnesota, USA

    Tariq Samad

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© 2013 Springer-Verlag London

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Shamma, J.S. (2013). Learning in Games. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_34-1

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  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_34-1

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  • Publisher Name: Springer, London

  • Online ISBN: 978-1-4471-5102-9

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

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