Paper Info
Title
Information aggregation in a beauty contest game
Abstract
We consider a repeated game in which a team of agents share a common, but only partially known, task. The team also has the goal to coordinate while completing the task. This creates a trade-off between estimating the task and coordinating with others reminiscent of the kind of trade-off exemplified by the Keynesian beauty contest game. The agents thus can benefit from learning from others. This paper provides a survey of results from [1-4]. We first present a recent result that states repeated play of the game by myopic but Bayesian agents, who observe the actions of their neighbors over a connected network, eventually yield coordination on a single action. Furthermore, the coordinated action is equal to the mean estimate of the common task given individual's information. This indicates that agents in the network have the same mean estimate in the limit despite the differences in the quality of local information. Finally, we state that if the space of signals is a finite set, the coordinated action is equal to the estimate of the common task given full information, that is, agents eventually aggregate the information available throughout the network on the common task optimally.
Year
DOI
Venue
2014
10.1109/ICASSP.2014.6854510
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
game theory,learning (artificial intelligence),multi-agent systems,Bayesian agents,Keynesian beauty contest game,agent coordination,agent learning,information aggregation,local information quality,Repeated Bayesian games,coordination games,learning,social networks
Keynesian beauty contest,Finite set,Computer science,CONTEST,Beauty,Repeated game,Artificial intelligence,Sequential game,Bayesian game,Bayesian probability
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
4
4
Name
Order
Citations
PageRank
Ceyhun Eksin15510.84
Pooya Molavi2727.38
Alejandro Ribeiro3156.75
Ali Jadbabaie44806581.69