CaltechAUTHORS
  A Caltech Library Service

Behavioral Game Theory: Thinking, Learning and Teaching

Camerer, Colin F. and Ho, Teck-Hua and Chong, Juin Kuan (2001) Behavioral Game Theory: Thinking, Learning and Teaching. Working Paper Series, California Institute of Technology , Pasadena, CA. http://resolver.caltech.edu/CaltechAUTHORS:20110303-150738273

[img]
Preview
PDF - Published Version
See Usage Policy.

1162Kb

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20110303-150738273

Abstract

This paper describes a parametric approach to weakening rationality assumptions in game theory to fit empirical data better. The central features of game theory are: The concept of a game (players, strategies, information, timing, outcomes); strategic thinking; mutual consistency of beliefs and strategies; and strategic foresight and Bayesian updating of unobserved "types" in repeated games. This paper describes a general four-parameter behavioral approach which relaxes the mutual consistency and foresight properties, while retaining much of the structure and hence the precision of game theory. One parameter measures the number of steps of iterated thinking that the average player does. This "thinking steps" model generates predictions about one-shot games and provides initial conditions for a theory of learning in repeated games. The learning theory adds one parameter (to measure response sensitivity) and adjusts learning parameters for environmental change (e.g., old experience is rapidly decayed when other players' moves are changing). It predicts behavior in new games more accurately than comparable models like fictitious play and reinforcement learning. The teaching theory assumes some fraction of players realize the impact of their current choices on future behavior of other players who learn, but does not impose equilibrium or updating assumptions as in standard approaches. The thinking-learning-teaching model is fit to many experimental data sets (a total of several thousand observations) including entry, mixed-equilibrium, "beauty contest", coordination, matrix games, and repeated trust games with incomplete information.


Item Type:Report or Paper (Working Paper)
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.2139/ssrn.295585DOIUNSPECIFIED
http://ssrn.com/abstract=295585SSRNUNSPECIFIED
Additional Information:This research was supported by NSF grants SBR 9730364, SBR 9730187, SES-0078911. Thanks to many people for helpful comments on this research, particularly Caltech colleagues (especially Richard McKelvey, Tom Palfrey, and Charles Plott), Mónica Capra, Vince Crawford, John Duffy, Drew Fudenberg, John Kagel, members of the MacArthur Preferences Network, our research assistants and collaborators Dan Clendennig, Graham Free, David Hsia, Ming Hsu, Hongjai Rhee, and Xin Wang, and seminar audience members too numerous to mention. Dan Levin gave the shooting-ahead military example. Dave Cooper, Ido Erev, and Bill Frechette wrote helpful emails.
Funders:
Funding AgencyGrant Number
NSFSBR 9730364
NSFSBR 9730187
NSFSES-0078911
Subject Keywords:experimental economics, game theory, behavioral game theory, bounded rationality, behavioral economics, learning
Classification Code:JEL Classifications: C7, C9.
Record Number:CaltechAUTHORS:20110303-150738273
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20110303-150738273
Official Citation:Camerer, Colin F., Ho, Teck and Chong, Kuan, Behavioral Game Theory: Thinking, Learning and Teaching (November 13, 2001). Caltech Working Paper. Available at SSRN: http://ssrn.com/abstract=295585 or doi:10.2139/ssrn.295585
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:22647
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:21 Feb 2012 21:59
Last Modified:26 Dec 2012 12:59

Repository Staff Only: item control page