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A Bayesian Sequential Experimental Study of Learning in Games

El-Gamal, Mahmoud and McKelvey, Richard D. and Palfrey, Thomas R. (1993) A Bayesian Sequential Experimental Study of Learning in Games. Journal of the American Statistical Association, 88 (422). pp. 428-435. ISSN 0162-1459.

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We apply a sequential Bayesian sampling procedure to study two models of learning in repeated games. In the first model individuals learn only about an opponent when they play her or him repeatedly but do not update from their experience with that opponent when they move on to play the same game with other opponents. We label this the nonsequential model. In the second model individuals use Bayesian updating to learn about population parameters from each of their opponents, as well as learning about the idiosyncrasies of that particular opponent. We call this the sequential model. We sequentially sample observations on the behavior of experimental subjects in the so-called “centipede game.” This game allows for a trade-off between competition and cooperation, which is of interest in many economic situations. At each point in time, the “state” of our dynamic problem consists of our beliefs about the two models and beliefs about the nuisance parameters of the two models. Our “choice” set is to sample or not to sample one more data point and, if we should not sample, which of the models to select. After 19 matches (4 subjects per match), we stop and reject the nonsequential model in favor of the sequential model.

Item Type:Article
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URLURL TypeDescription ItemWorking Paper
Palfrey, Thomas R.0000-0003-0769-8109
Additional Information:© 1993 American Statistical Association. Financial support was provided by National Science Foundation Grant SES9011828. The authors thank Mark Fey, Lynell Jackson, and Jeffrey Prisbrey for their assistance in setting up the experiments, recruiting subjects, and running the experiments; the Jet Propulsion Laboratory staff for giving us access to their Cray XMP / 18 and subsequently their Cray YMP2E/ 116; Gary Lorden for a valuable discussion; and two editors, one associate editor, and three referees for their very careful and useful comments on earlier versions.
Group:Social Science Working Papers
Funding AgencyGrant Number
Subject Keywords:Bayesian learning, Experimental design, Game theory, Preposterior analysis, Sequential sampling
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Other Numbering System NameOther Numbering System ID
Social Science Working Paper757
Issue or Number:422
Record Number:CaltechAUTHORS:20160307-111846023
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65118
Deposited By: Susan Vite
Deposited On:05 Apr 2016 22:21
Last Modified:22 Nov 2019 09:58

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