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

Author

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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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    Cited by:

    1. Alexis Belianin & Marco Novarese, 2005. "Trust, communication and equlibrium behaviour in public goods," Experimental 0506001, University Library of Munich, Germany.

    More about this item

    Keywords

    bounded rationality; evolutionary games; experiments; learning; public goods;
    All these keywords.

    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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