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Neural Networks and Contagion

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  • Berninghaus, Siegfried K.

    (Universität Karlsruhe)

  • Haller, Hans

    (Department of Economics, Virginia Polytechnic Institute and State University)

  • Outkin, Alexander

    (Decision Applications Division, Los Alamos National Laboratory)

Abstract

We analyze local as well as global interaction and contagion in population games, using the formalism of neural networks. In contrast to much of the literature, a state encodes not only the frequency of play, but also the spatial pattern of play. Stochastic best response dynamics with logistic noise gives rise to a log-linear or logit response model. The stationary distribution is of the Gibbs-Boltzmann type. The long-run equilibria are the maxima of a potential function.

Suggested Citation

  • Berninghaus, Siegfried K. & Haller, Hans & Outkin, Alexander, 2005. "Neural Networks and Contagion," Sonderforschungsbereich 504 Publications 05-35, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    • Handle: RePEc:xrs:sfbmaa:05-35
    Note: Financial support from the Deutsche Forschungsgemeinschaft, SFB 504, at the University of Mannheim, is gratefully acknowledged.
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    Cited by:

    1. Jacques Durieu & Philippe Solal, 2012. "Models of Adaptive Learning in Game Theory," Chapters, in: Richard Arena & Agnès Festré & Nathalie Lazaric (ed.), Handbook of Knowledge and Economics, chapter 11, Edward Elgar Publishing.
    2. Berninghaus, Siegfried & Haller, Hans, 2007. "Pairwise interaction on random graphs," Papers 06-16, Sonderforschungsbreich 504.

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