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

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.