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Simultaneous Evolution of Learning Rules and Strategies

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Author Info

  • Kirchkamp, Oliver

Abstract

We study a model of local evolution. Players are located on a network and play games agains their neighbors. Players are characterized by three properties: (1) The stage game strategies they use agains their neighbors. (2) The repeated game strategy that determines the former. (3) A learning rule that selects the repeated game strategy, on the basis of the player's own and the neighbors' payoff and repeated game strategy. The dynamics that specifies learning rules is given exogenously. Players sample their neighbors' learning rules and their respective payoff. Then they construct a model that related parameters of the learning rules to payoffs. Given this model they choose an optimal learning rule. We find that under this dynamics learning rules emerge in the long run which behave deterministically but which are asymmetric in the sense that while learning they put more weight on the learning players experience then on the observed players one. Nevertheless stage game behavior under these learning rules is similar to behavior using symmetric learning rules.

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Bibliographic Info

Paper provided by University of Bonn, Germany in its series Discussion Paper Serie B with number 379.

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Length: pages
Date of creation: May 1996
Date of revision:
Handle: RePEc:bon:bonsfb:379

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Postal: Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany
Fax: +49 228 73 6884
Web page: http://www.bgse.uni-bonn.de

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Keywords: Evolutionary Game Theory; Networks.;

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References

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  1. Ken Binmore & Larry Samuelson, 1994. "Muddling Through: Noisy Equilibrium Selection," Game Theory and Information 9410002, EconWPA.
  2. Schlag,Karl, . "Dynamic stability in the repeated prisoners dilemma," Discussion Paper Serie B 243, University of Bonn, Germany.
  3. K. Schlag, 2010. "Why Imitate, and if so, How? Exploring a Model of Social Evolution," Levine's Working Paper Archive 454, David K. Levine.
  4. Glen Ellison, 2010. "Learning, Local Interaction, and Coordination," Levine's Working Paper Archive 391, David K. Levine.
  5. Eshel, I. & Samuelson, L. & Shaked, A., 1996. "Altruists, Egoists and Hooligans in a Local Interaction Model," Working papers 9612r, Wisconsin Madison - Social Systems.
  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 157-79, March.
  7. Kirchkamp, Oliver, 1995. "Spatial Evolution of Automata in the Prisoners' Dilemma," Discussion Paper Serie B 330, University of Bonn, Germany.
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Citations

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Cited by:
  1. Juan D. Montoro-Pons, 2000. "Collective Action, Free Riding And Evolution," Computing in Economics and Finance 2000 279, Society for Computational Economics.
  2. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2013. "An Evolutionary Model of Price Competition Among Spatially Distributed Firms," Computational Economics, Society for Computational Economics, vol. 42(4), pages 373-391, December.
  3. Jurjen Kamphorst & Gerard van der Laan, 2006. "Learning in a Local Interaction Hawk-Dove Game," Tinbergen Institute Discussion Papers 06-034/1, Tinbergen Institute.
  4. Kirchkamp, Oliver & Nagel, Rosemarie, 2003. "No imitation - on local and group interaction, learning and reciprocity in prisoners\," Sonderforschungsbereich 504 Publications 03-04, Sonderforschungsbereich 504, Universit├Ąt Mannheim & Sonderforschungsbereich 504, University of Mannheim.
  5. Juan Montoro-Pons & Francisco Garcia-Sobrecases, 2003. "A Computational Approach to the Collective Action Problem: Assessment of Alternative Learning Rules," Computational Economics, Society for Computational Economics, vol. 21(1), pages 137-151, February.
  6. Jurjen Kamphorst & Gerard van der Laan, 2006. "Learning in a Local Interaction Hawk-Dove Game," Tinbergen Institute Discussion Papers 06-034/1, Tinbergen Institute.
  7. Kirchkamp, Oliver & Nagel, Rosemarie, 2007. "Naive learning and cooperation in network experiments," Games and Economic Behavior, Elsevier, vol. 58(2), pages 269-292, February.

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