A behavioral model for mechanism design: Individual evolutionary learning
Abstract 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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- Thomas Muench & Mark Walker, 1983. "Are Groves-Ledyard Equilibria Attainable?," Review of Economic Studies, Oxford University Press, vol. 50(2), pages 393-396.
- Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
- Hanaki, Nobuyuki & Sethi, Rajiv & Erev, Ido & Peterhansl, Alexander, 2005.
Journal of Economic Behavior & Organization,
Elsevier, vol. 56(4), pages 523-542, April.
- 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, 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.
- 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.
- 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.
- 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.
- 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, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
- Arifovic, Jasmina, 2000. "Evolutionary Algorithms In Macroeconomic Models," Macroeconomic Dynamics, Cambridge University Press, vol. 4(03), pages 373-414, September.
- Milgrom, Paul & Roberts, John, 1990. "Rationalizability, Learning, and Equilibrium in Games with Strategic Complementarities," Econometrica, Econometric Society, vol. 58(6), pages 1255-1277, November.
- Colin F. Camerer & Teck-Hua Ho & Juin-Kuan Chong, 2004. "A Cognitive Hierarchy Model of Games," The Quarterly Journal of Economics, Oxford University Press, vol. 119(3), pages 861-898.
- 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.
- 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.
- McKelvey Richard D. & Palfrey Thomas R., 1995.
"Quantal Response Equilibria for Normal Form Games,"
Games and Economic Behavior,
Elsevier, vol. 10(1), pages 6-38, July.
- R. McKelvey & T. Palfrey, 2010. "Quantal Response Equilibria for Normal Form Games," Levine's Working Paper Archive 510, David K. Levine.
- McKelvey, Richard D. & Palfrey, Thomas R., 1994. "Quantal Response Equilibria For Normal Form Games," Working Papers 883, California Institute of Technology, Division of the Humanities and Social Sciences.
- Healy, Paul J. & Mathevet, Laurent, 2012. "Designing stable mechanisms for economic environments," Theoretical Economics, Econometric Society, vol. 7(3), September.
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