A behavioral model for mechanism design: Individual evolutionary learning
AbstractAbstract We are interested in how Groves-Ledyard mechanisms perform when used repeatedly in a sequence of one-shot games where agents know only their own preferences. In particular, how fast do the mechanisms converge to the stage game Nash equilibrium and how does that speed of convergence depend on the mechanism parameter [gamma]. Prior theoretical and experimental work provide little guidance. Neither do existing behavioral models designed for small games with a small finite number of strategies. For example, even though experience weighted attraction learning is very successful in modeling behavior in one-shot games with very small, finite strategy spaces, it is not successful in modeling behavior in repeated games with a continuum strategy space unless one wants to be involved in fine tuning. We provide a behavioral model, individual evolutionary learning. The time to first convergence is predicted to be smooth and U-shaped in [gamma]. These predictions are robust to a wide range of parameter values. To test the IEL predictions, we ran our own experiments at the California Institute of Technology. Qualitatively, the data from those experiments are consistent with the IEL predictions about convergence and the U-shaped curve. Quantitatively, the human subjects are a little faster, a little less stable, and slightly less efficient than IEL. But for [gamma]Â =Â 50 and 100, the differences between humans and IEL are very small.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Economic Behavior & Organization.
Volume (Year): 78 (2011)
Issue (Month): 3 (May)
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Web page: http://www.elsevier.com/locate/jebo
Learning Experiment Public goods Mechanism design;
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- Jasmina Arifovic & John Ledyard, 2004. "Scaling Up Learning Models in Public Good Games," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 6(2), pages 203-238, 05.
- Yan Chen & Robert Gazzale, 2004.
"When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting,"
American Economic Review,
American Economic Association, vol. 94(5), pages 1505-1535, December.
- Yan Chen & Robert S. Gazzale, 2004. "When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting," Department of Economics Working Papers 2004-02, Department of Economics, Williams College.
- Colin F. Camerer & Teck-Hua Ho & Juin-Kuan Chong, 2004. "A Cognitive Hierarchy Model of Games," The Quarterly Journal of Economics, MIT Press, vol. 119(3), pages 861-898, August.
- Healy, Paul J., 2006. "Learning dynamics for mechanism design: An experimental comparison of public goods mechanisms," Journal of Economic Theory, Elsevier, vol. 129(1), pages 114-149, July.
- Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
- Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
- Milgrom, Paul & Roberts, John, 1990. "Rationalizability, Learning, and Equilibrium in Games with Strategic Complementarities," Econometrica, Econometric Society, vol. 58(6), pages 1255-77, November.
- LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
- Arifovic, Jasmina, 2000. "Evolutionary Algorithms In Macroeconomic Models," Macroeconomic Dynamics, Cambridge University Press, vol. 4(03), pages 373-414, September.
- Arifovic, Jasmina & Ledyard, John, 2007. "Call market book information and efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1971-2000, June.
- Hanaki, Nobuyuki & Sethi, Rajiv & Erev, Ido & Peterhansl, Alexander, 2005.
Journal of Economic Behavior & Organization,
Elsevier, vol. 56(4), pages 523-542, April.
- Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
- Yan Chen & Fang-Fang Tang, 1998. "Learning and Incentive-Compatible Mechanisms for Public Goods Provision: An Experimental Study," Journal of Political Economy, University of Chicago Press, vol. 106(3), pages 633-662, June.
- Jasmina Arifovic & John Ledyard, 2012. "Individual Evolutionary Learning, Other-regarding Preferences, and the Voluntary Contributions Mechanism," Discussion Papers wp12-01, Department of Economics, Simon Fraser University.
- Healy, Paul J. & Mathevet, Laurent, 2012. "Designing stable mechanisms for economic environments," Theoretical Economics, Econometric Society, vol. 7(3), September.
- Arifovic, Jasmina & Ledyard, John, 2012. "Individual evolutionary learning, other-regarding preferences, and the voluntary contributions mechanism," Journal of Public Economics, Elsevier, vol. 96(9-10), pages 808-823.
- Dietrichson, Jens, 2013. "Coordination Incentives, Performance Measurement and Resource Allocation in Public Sector Organizations," Working Papers 2013:26, Lund University, Department of Economics.
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