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Learning dynamics for mechanism design: An experimental comparison of public goods mechanisms

Healy, Paul J. (2006) Learning dynamics for mechanism design: An experimental comparison of public goods mechanisms. Journal of Economic Theory, 129 (1). pp. 114-149. ISSN 0022-0531. https://resolver.caltech.edu/CaltechAUTHORS:20171103-153304548

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Abstract

In a repeated-interaction public goods economy, incomplete information and dynamic behavior may affect the realized outcomes of mechanisms known to be efficient in a complete information oneshot game. An experimental test of five public goods mechanisms indicates that subjects with private information appear to best respond to recent observations. This provides predictions about which mechanisms will generate convergence to their efficient equilibrium allocations. These predictions match the experimental result that globally stable efficient mechanisms realize the highest efficiency in practice. The simplicity of the suggested best response model makes it useful in predicting stability of mechanisms not yet tested.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.jet.2005.03.002DOIArticle
http://www.sciencedirect.com/science/article/pii/S0022053105000931PublisherArticle
http://resolver.caltech.edu/CaltechAUTHORS:20170731-161011632Related ItemEarlier version published as Social Science Working Paper 1182
Additional Information:© 2005 Elsevier Inc. All rights reserved. Received 5 December 2003; final version received 17 March 2005; Available online 13 May 2005 Formerly SSWP 1182.
Subject Keywords:Mechanism design; Experiments; Best response; Public goods; Dynamics
Issue or Number:1
Classification Code:JEL: C72; C91; D83; H41
Record Number:CaltechAUTHORS:20171103-153304548
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20171103-153304548
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:82953
Collection:CaltechAUTHORS
Deposited By: Jacquelyn Bussone
Deposited On:07 Nov 2017 18:13
Last Modified:03 Oct 2019 19:00

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