@inproceedings{5e711da83d3c4aa084fd6650fd047d3e,

title = "Distributed filters for Bayesian network games",

abstract = "We consider a repeated network game where agents' utilities are quadratic functions of the state of the world and actions of all the agents. The state of the world is represented by a vector on which agents receive private signals with Gaussian noise. We define the solution concept as Bayesian Nash equilibrium and present a recursion to compute equilibrium strategies locally if an equilibrium exists at all stages. We further provide conditions under which a unique equilibrium exists. We conclude with an example of the proposed recursion in a repeated Cournot competition game and discuss properties of convergence such as efficient learning and convergence rate.",

keywords = "Bayesian learning, distributed algorithms, repeated network games",

author = "Ceyhun Eksin and Pooya Molavi and Alejandro Ribeiro and Ali Jadbabaie",

year = "2013",

month = jan,

day = "1",

language = "English (US)",

isbn = "9780992862602",

series = "European Signal Processing Conference",

publisher = "European Signal Processing Conference, EUSIPCO",

booktitle = "2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013",

note = "2013 21st European Signal Processing Conference, EUSIPCO 2013 ; Conference date: 09-09-2013 Through 13-09-2013",

}