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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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    References listed on IDEAS

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    1. Outkin, Alexander V., 2003. "Cooperation and local interactions in the Prisoners' Dilemma Game," Journal of Economic Behavior & Organization, Elsevier, vol. 52(4), pages 481-503, December.
    2. Eichberger, J. & Haller, H. & Milne, F., 1993. "Naive Bayesian learning in 2 x 2 matrix games," Journal of Economic Behavior & Organization, Elsevier, vol. 22(1), pages 69-90, September.
    3. Michihiro, Kandori & Rob, Rafael, 1998. "Bandwagon Effects and Long Run Technology Choice," Games and Economic Behavior, Elsevier, vol. 22(1), pages 30-60, January.
    4. Fudenberg, D. & Harris, C., 1992. "Evolutionary dynamics with aggregate shocks," Journal of Economic Theory, Elsevier, vol. 57(2), pages 420-441, August.
    5. Kandori, Michihiro & Mailath, George J & Rob, Rafael, 1993. "Learning, Mutation, and Long Run Equilibria in Games," Econometrica, Econometric Society, vol. 61(1), pages 29-56, January.
    6. Eshel, Ilan & Samuelson, Larry & Shaked, Avner, 1998. "Altruists, Egoists, and Hooligans in a Local Interaction Model," American Economic Review, American Economic Association, vol. 88(1), pages 157-179, March.
    7. Ellison, Glenn, 1993. "Learning, Local Interaction, and Coordination," Econometrica, Econometric Society, vol. 61(5), pages 1047-1071, September.
    8. Neyman, Abraham, 1985. "Bounded complexity justifies cooperation in the finitely repeated prisoners' dilemma," Economics Letters, Elsevier, vol. 19(3), pages 227-229.
    9. Kirchkamp, Oliver, 2000. "Spatial evolution of automata in the prisoners' dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 43(2), pages 239-262, October.
    10. Ulrich Schwalbe & Siegfried K. Berninghaus, 1996. "Conventions, local interaction, and automata networks," Journal of Evolutionary Economics, Springer, vol. 6(3), pages 297-312.
    11. Rubinstein, Ariel, 1986. "Finite automata play the repeated prisoner's dilemma," Journal of Economic Theory, Elsevier, vol. 39(1), pages 83-96, June.
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    Cited by:

    1. Jacques Durieu & Philippe Solal, 2012. "Models of Adaptive Learning in Game Theory," Chapters,in: 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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