Distributed filters for Bayesian network games

Ceyhun Eksin, Pooya Molavi, Alejandro Ribeiro, Ali Jadbabaie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
StatePublished - Jan 1 2013
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: Sep 9 2013Sep 13 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Other2013 21st European Signal Processing Conference, EUSIPCO 2013


  • Bayesian learning
  • distributed algorithms
  • repeated network games

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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