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Learning Dynamics in Mechanism Design: An Experimental Comparison of Public Goods Mechanisms

Healy, Paul J. (2003) Learning Dynamics in Mechanism Design: An Experimental Comparison of Public Goods Mechanisms. Social Science Working Paper, 1182. California Institute of Technology , Pasadena, CA.

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In a repeated-interaction public goods economy, dynamic behavior may affect the efficiency of various mechanisms thought to be efficient in one-shot games. Inspired by results obtained in previous experiments, the current paper proposes a simple best response model in which players' beliefs are functions of previous strategy profiles. The predictions of the model are found to be highly consistent with new experimental data from five mechanisms with various types of equilibria. Interesting properties of a 2-parameter Vickrey-Clarke-Groves mechanism help to draw out this result. The simplicity of the model makes it useful in predicting dynamic stability of other mechanisms.

Item Type:Report or Paper (Working Paper)
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URLURL TypeDescription ItemLater version published in Journal of Economic Theory
Additional Information:The author wishes to thank John Ledyard for financial support, advice, and encouragement in writing this paper, as well as Ken Binmore, Matt Jackson, Tim Cason, Bob Sherman, Ivana Komunjer, Peter Bossaerts, Tatsuyoshi Saijo, and David Grether for their valuable suggestions and comments. Published as Healy, P.J. (2006). Learning dynamics for mechanism design: An experimental comparison of public goods mechanisms. Journal of Economic Theory, 129(1), 114-149.
Group:Social Science Working Papers
Subject Keywords:Mechanism design, experiments, best response, public goods, dynamics
Series Name:Social Science Working Paper
Issue or Number:1182
Classification Code:JEL: C72, C91, D83, H41
Record Number:CaltechAUTHORS:20170731-161011632
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79635
Deposited By: Jacquelyn Bussone
Deposited On:01 Aug 2017 20:42
Last Modified:03 Oct 2019 18:22

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