Paper Info
Title
A learning-based model of repeated games with incomplete information
Abstract
This paper tests a learning-based model of strategic teaching in repeated games with incomplete information. The repeated game has a long-run player whose type is unknown to a group of short-run players. The proposed model assumes a fraction of ‘short-run’ players follow a one-parameter learning model (self-tuning EWA). In addition, some ‘long-run’ players are myopic while others are sophisticated and rationally anticipate how short-run players adjust their actions over time and “teach” the short-run players to maximize their long-run payoffs. All players optimize noisily. The proposed model nests an agent-based quantal-response equilibrium (AQRE) and the standard equilibrium models as special cases. Using data from 28 experimental sessions of trust and entry repeated games, including 8 previously unpublished sessions, the model fits substantially better than chance and much better than standard equilibrium models. Estimates show that most of the long-run players are sophisticated, and short-run players become more sophisticated with experience.
Year
DOI
Venue
2006
10.1016/j.geb.2005.03.009
Games and Economic Behavior
Keywords
Field
DocType
C72,C92,D83
Computer science,Repeated game,Artificial intelligence,Complete information,Machine learning
Journal
Volume
Issue
ISSN
55
2
0899-8256
Citations 
PageRank 
References 
5
0.92
0
Authors
3
Name
Order
Citations
PageRank
Juin-Kuan Chong19316.62
Colin F. Camerer220237.90
Teck H. Ho318117.75