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Dynamic Voluntary Contribution to a Public Good: Learning to be a Free Rider

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  • Clemens, Christiane
  • Riechmann, Thomas

Abstract

This paper explores the question whether boundedly rational agents learn to behave optimally when asked to voluntarily contribute to a public good. The decision process of individuals is described by an Evolutionary Algorithm. We analyze the learning process of purely and impurely altruistic agents and find that in both cases the contribution level converges towards the Nash equilibrium although, with pure altruism, exact free rider-behavior is never observed. The latter result corresponds to findings from experiments on voluntary contribution to a public good. Crucial determinants of the learning process are the population size and the propensity to experiment.

Suggested Citation

  • Clemens, Christiane & Riechmann, Thomas, 2001. "Dynamic Voluntary Contribution to a Public Good: Learning to be a Free Rider," Hannover Economic Papers (HEP) dp-240, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  • Handle: RePEc:han:dpaper:dp-240
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    File URL: http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-240.pdf
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    References listed on IDEAS

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    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    3. Binmore, K. & Samuelson, L., 1993. "An Economist's Perspective on the Evolution of Norms," Working papers 9323, Wisconsin Madison - Social Systems.
    4. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
    5. Jack Hirshleifer & Juan Carlos Martinez Coll, 1992. "Selection, Mutation, and the Preservation of Diversity in Evolutionary Games," UCLA Economics Working Papers 648, UCLA Department of Economics.
    6. Thomas Riechmann, 2001. "Evolutionary Learning in the Ultimatum Game," Computing in Economics and Finance 2001 91, Society for Computational Economics.
    7. Gale, John & Binmore, Kenneth G. & Samuelson, Larry, 1995. "Learning to be imperfect: The ultimatum game," Games and Economic Behavior, Elsevier, vol. 8(1), pages 56-90.
    8. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    9. Binmore, K. & Samuelson, L. & Gale, J., 1993. "Learning to be Imperfect: The Ultimatum Game," Working papers 9325, Wisconsin Madison - Social Systems.
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    Citations

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    Cited by:

    1. Alexis Belianin & Marco Novarese, 2005. "Trust, communication and equlibrium behaviour in public goods," Experimental 0506001, EconWPA.

    More about this item

    Keywords

    bounded rationality; evolutionary games; experiments; learning; public goods;

    JEL classification:

    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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