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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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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