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Adaptation vs. Anticipation in Public-Good Games
Unformatted Document Text:  Adaptation vs. Anticipation in Public-Good Games By Marco A. Janssen and T.K. Ahn ∗ August 4, 2003 1 Introduction This study compares the empirical performance of an experience-weightedattraction (EWA) learning model and a best-response with signaling (BRS)model in the context of experimental public-goods provision games. Thedata are drawn from Isaac and Walker (1998) and Isaac, Walker, and Williams(1994). Genetic algorithms are used to estimate the parameters of the mod-els. Multiple measures of the goodness-of-Þt are combined into a singlemeasure to assess the relative performance of the models. Those measuresrepresent how a model replicates the experimental data in terms of theaverage contribution level, variance in contribution level across individu-als, and distribution of relative changes in individual contribution betweenrounds. The EWA model (Camerer and Ho, 1999) is one of the most gen-eral, and empirically based learning models, and subsumes reinforcement- ∗ Prepared for delivery at the 2003 Annual Meeting of the American Political Science Association, Philadelphia, August 28-31, 2003. Copyright by the American Political Sci-ence Association. Marco A. Janssen is an Associate Research Scientist at the Center forthe study of Institutions, Population and Environmental Change, Indiana University andT.K. Ahn is an Assistant Professor in the Department of Political Science, Florida StateUniversity. The authors gratefully acknowledge the support of the Center for the Studyof Institutions, Population and Environmental Change, Indiana University and the Work-shop in Political Theory and Policy Analysis, Indiana University, through National ScienceFoundation grants SBR9521918, SES0083511 and SES0232072. The authors would like tothank James M. Walker for providing the experimental data and Colin Camerer for hiscomments at multiple stages of this research project. Dan Friedman, and Werner Güth,Elinor Ostrom and other participants to a workshop meeting at Indiana University, Jan-uary 24-26, 2003 provided helpful comments on an earlier version of this paper. We wouldalso like to thank the Indiana University computing systems for running the experimentson the UITS Research SP System. 1

Authors: Janssen, Marco. and Ahn, T.K..
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background image
Adaptation vs. Anticipation in Public-Good
Games
By Marco A. Janssen and T.K. Ahn
August 4, 2003
1
Introduction
This study compares the empirical performance of an experience-weighted
attraction (EWA) learning model and a best-response with signaling (BRS)
model in the context of experimental public-goods provision games. The
data are drawn from Isaac and Walker (1998) and Isaac, Walker, and Williams
(1994). Genetic algorithms are used to estimate the parameters of the mod-
els. Multiple measures of the goodness-of-Þt are combined into a single
measure to assess the relative performance of the models. Those measures
represent how a model replicates the experimental data in terms of the
average contribution level, variance in contribution level across individu-
als, and distribution of relative changes in individual contribution between
rounds. The EWA model (Camerer and Ho, 1999) is one of the most gen-
eral, and empirically based learning models, and subsumes reinforcement-
Prepared for delivery at the 2003 Annual Meeting of the American Political Science
Association, Philadelphia, August 28-31, 2003. Copyright by the American Political Sci-
ence Association. Marco A. Janssen is an Associate Research Scientist at the Center for
the study of Institutions, Population and Environmental Change, Indiana University and
T.K. Ahn is an Assistant Professor in the Department of Political Science, Florida State
University. The authors gratefully acknowledge the support of the Center for the Study
of Institutions, Population and Environmental Change, Indiana University and the Work-
shop in Political Theory and Policy Analysis, Indiana University, through National Science
Foundation grants SBR9521918, SES0083511 and SES0232072. The authors would like to
thank James M. Walker for providing the experimental data and Colin Camerer for his
comments at multiple stages of this research project. Dan Friedman, and Werner Güth,
Elinor Ostrom and other participants to a workshop meeting at Indiana University, Jan-
uary 24-26, 2003 provided helpful comments on an earlier version of this paper. We would
also like to thank the Indiana University computing systems for running the experiments
on the UITS Research SP System.
1


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