Evolutionary Dynamics in Public Good Games
AbstractThis paper explores the question whether boundedly rational agents learn to behave optimally when asked to voluntarily contribute to a public good. The dynamic game is described by an Evolutionary Algorithm, which is shown to extend the applicability of ordinary replicator dynamics of evolutionary game theory to problem sets characterized by finite populations and continuous strategy spaces. We analyze the learning process of purely and impurely altruistic agents and find in both cases the contribution level to converge towards the Nash equilibrium. The group size, the degree of initial heterogeneity and the propensity to experiment are key factors of the learning process. Copyright Springer Science+Business Media, Inc. 2006
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Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 28 (2006)
Issue (Month): 4 (November)
bounded rationality; learning; pubic goods; evolutionary games; evolutionary algorithms;
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