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Published October 2012 | public
Journal Article

Individual evolutionary learning, other-regarding preferences, and the voluntary contributions mechanism

Abstract

The data from experiments with the Voluntary Contributions Mechanism suggest five stylized facts, including the restart effect. To date, no theory has explained all of these facts simultaneously. We merge our Individual Evolutionary Learning model with a variation of heterogeneous other-regarding preferences and a distribution of types to provide a new theory that does. In addition, our theory answers some open questions concerning the data on partners–strangers experiments. One interesting feature of the theory is that being a conditional cooperator is not a type but arises endogenously as a behavior. The data generated by our model are quantitatively similar to data from a variety of experiments, and experimenters, and are insensitive to moderate variations in the parameters of the model. That is, we have a robust explanation for most behavior in VCM experiments.

Additional Information

© 2012 Elsevier B.V. Received 25 October 2010. Received in revised form 7 May 2012. Accepted 30 May 2012. Available online 15 June 2012. We thank Olena Kostyshyna and Lilong Shi for their help in generating the simulations in this paper. We would also like to thank participants at the 3rd annual Social Dilemmas Workshop at Florida State in 2008; the 2008 Econometric Society Summer Meetings, Pittsburgh; 14th International Conference on Computing and Finance, 2008, Paris; 2008 North-American ESA Conference, Tucson; the Decentralization Conference April 2009, Amsterdam Symposium on Behavioral and Experimental Economics, September 2011; and the participants at the seminars at University of Amsterdam, Paris 2 University, University of NewSouthWales, University of Michigan, Chapman University, and George Mason University for helpful discussion. We also thank Lise Vesterlund for very helpful comments on an earlier draft of the paper.

Additional details

Created:
August 22, 2023
Modified:
October 19, 2023