Research Article
Learning Correlated Equilibria in Noncooperative Games with Cluster Structure
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@INPROCEEDINGS{10.1007/978-3-642-35582-0_9, author={Omid Gharehshiran and Vikram Krishnamurthy}, title={Learning Correlated Equilibria in Noncooperative Games with Cluster Structure}, proceedings={Game Theory for Networks. Third International ICST Conference, GameNets 2012, Vancouver, BC, Canada, May 24-26, 2012, Revised Selected Papers}, proceedings_a={GAMENETS}, year={2012}, month={12}, keywords={Adaptive learning correlated equilibrium differential inclusions stochastic approximation}, doi={10.1007/978-3-642-35582-0_9} }- Omid Gharehshiran
Vikram Krishnamurthy
Year: 2012
Learning Correlated Equilibria in Noncooperative Games with Cluster Structure
GAMENETS
Springer
DOI: 10.1007/978-3-642-35582-0_9
Abstract
We consider learning correlated equilibria in noncooperative repeated games where players form clusters. In each cluster, players observe the action profile of cluster members and receive local payoffs, associated to performing localized tasks within clusters. Players also acquire global payoffs due to global interaction with players outside cluster, however, are oblivious to actions of those players. A novel adaptive learning algorithm is presented which generates trajectories of empirical frequency of joint plays that converge almost surely to the set of correlated -equilibria. Thus, sophisticated rational global behavior is achieved by individual player’s simple local behavior.
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