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.
|Date of creation:||Mar 2001|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (0511) 762-5350
Fax: (0511) 762-5665
Web page: http://www.wiwi.uni-hannover.de
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:han:dpaper:dp-240. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Heidrich, Christian)
If references are entirely missing, you can add them using this form.