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

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  • Christiane Clemens and Thomas Riechmann

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 find that the contribution level converges towards the Nash equilibrium although 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

  • Christiane Clemens and Thomas Riechmann, 2001. "Dynamic Voluntary Contribution to a Public Good:Learning to be a Free Rider," Computing in Economics and Finance 2001 92, Society for Computational Economics.
    • Handle: RePEc:sce:scecf1:92
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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; genetic algorithms; learning; public goods;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • 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
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods

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