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Simultaneous Evolution of Learning Rules and Strategies

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  • Kirchkamp, Oliver

    () (Sonderforschungsbereich 504)

Abstract

We study a model of local evolution. Agents are located on a network and interact strategically with their neighbours. Strategies are chosen with the help of learning rules that are based on the success of strategies observed in the neighbourhood. The standard literature on local evolution assumes learning rules to be exogenous and fixed. In this paper we consider a specific evolutionary dynamics that determines learning rules endogenously. We find with the help of simulations that in the long run learning rules behave rather deterministically but are asymmetric in the sense that while learning they put more weight on the learning players' experience than on the observed players' one. Nevertheless stage game behaviour under these learning rules is similar to behaviour with symmetric learning rules.

Suggested Citation

  • Kirchkamp, Oliver, 1996. "Simultaneous Evolution of Learning Rules and Strategies," Sonderforschungsbereich 504 Publications 98-46, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
  • Handle: RePEc:xrs:sfbmaa:98-46
    Note: I am grateful for financial support from the Deutsche Forschungsgemeinschaft through SFB 303 and SFB 504. Parts of this paper were written at the European University Institute (EUI), Florence. I am very grateful for the hospitality and the support that I received from the EUI. I thank Georg N\
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    References listed on IDEAS

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    1. Ken Binmore & Larry Samuelson, 1994. "Muddling Through:Noisy Equilibrium Selection," Game Theory and Information 9403005, EconWPA, revised 29 Mar 1994.
    2. 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.
    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. Kirchkamp, Oliver, 2000. "Spatial evolution of automata in the prisoners' dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 43(2), pages 239-262, October.
    5. Schlag,Karl, "undated". "Dynamic stability in the repeated prisoners dilemma," Discussion Paper Serie B 243, University of Bonn, Germany.
    6. Ellison, Glenn, 1993. "Learning, Local Interaction, and Coordination," Econometrica, Econometric Society, vol. 61(5), pages 1047-1071, September.
    7. Ken Binmore & Larry Samuelson, 1994. "Muddling Through: Noisy Equilibrium Selection," Game Theory and Information 9410002, EconWPA.
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    Citations

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    Cited by:

    1. Juan Montoro-Pons & Francisco Garcia-Sobrecases, 2003. "A Computational Approach to the Collective Action Problem: Assessment of Alternative Learning Rules," Computational Economics, Springer;Society for Computational Economics, vol. 21(1), pages 137-151, February.
    2. Kirchkamp, Oliver & Nagel, Rosemarie, 2000. "Repeated Game Strategies in Local and Group Prisoner`s Dilemma," Sonderforschungsbereich 504 Publications 00-50, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    3. Kirchkamp, Oliver & Nagel, Rosemarie, 2000. "Local and group interaction in prisoners' dilemma experiments," Papers 00-11, Sonderforschungsbreich 504.
    4. Kirchkamp, Oliver & Nagel, Rosemarie, 2003. "No imitation : on local and group interaction, learning and reciprocity in prisoners' dilemma experiments," Papers 03-04, Sonderforschungsbreich 504.
    5. Kirchkamp, Oliver & Nagel, Rosemarie, 2003. "No imitation - on local and group interaction, learning and reciprocity in prisoners\," Sonderforschungsbereich 504 Publications 03-04, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    6. Juan D. Montoro-Pons, 2000. "Collective Action, Free Riding And Evolution," Computing in Economics and Finance 2000 279, Society for Computational Economics.
    7. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2013. "An Evolutionary Model of Price Competition Among Spatially Distributed Firms," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 373-391, December.
    8. Kirchkamp, Oliver & Nagel, Rosemarie, 2007. "Naive learning and cooperation in network experiments," Games and Economic Behavior, Elsevier, vol. 58(2), pages 269-292, February.
    9. Kirchkamp, Oliver & Nagel, Rosemarie, 2005. "Learning and cooperation in network experiments," Sonderforschungsbereich 504 Publications 05-27, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.

    More about this item

    JEL classification:

    • R13 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General Equilibrium and Welfare Economic Analysis of Regional Economies
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D73 - Microeconomics - - Analysis of Collective Decision-Making - - - Bureaucracy; Administrative Processes in Public Organizations; Corruption
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • D62 - Microeconomics - - Welfare Economics - - - Externalities
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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