Dynamic Voluntary Contribution to a Public Good: Learning to be a Free Rider
AbstractThis 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.
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Bibliographic InfoPaper provided by Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover with number dp-240.
Length: 21 pages
Date of creation: Mar 2001
Date of revision:
bounded rationality; evolutionary games; experiments; learning; public goods;
Other versions of this item:
- 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.
- 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, and Information
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
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- Alexis Belianin & Marco Novarese, 2005. "Trust, communication and equlibrium behaviour in public goods," Experimental 0506001, EconWPA.
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