Bayesian Quadratic Network Game Filters Academic Article

abstract

• A repeated network game where agents have quadratic utilities that depend on information externalities-an unknown underlying state-as well as payoff externalities-the actions of all other agents in the network-is considered. Agents play Bayesian Nash Equilibrium strategies with respect to their beliefs on the state of the world and the actions of all other nodes in the network. These beliefs are refined over subsequent stages based on the observed actions of neighboring peers. This paper introduces the Quadratic Network Game (QNG) filter that agents can run locally to update their beliefs, select corresponding optimal actions, and eventually learn a sufficient statistic of the network's state. The QNG filter is demonstrated on a Cournot market competition game and a coordination game to implement navigation of an autonomous team. 2014 IEEE.

authors

• Eksin, Ceyhun

published proceedings

• IEEE TRANSACTIONS ON SIGNAL PROCESSING

author list (cited authors)

• Eksin, C., Molavi, P., Ribeiro, A., & Jadbabaie, A.

citation count

• 15

complete list of authors

• Eksin, Ceyhun||Molavi, Pooya||Ribeiro, Alejandro||Jadbabaie, Ali

publication date

• May 2014

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Signal Processing  Journal

keywords

• Learning In Networks
• Linear Quadratic Gaussian Games
• Repeated Bayesian Games

Digital Object Identifier (DOI)

• 10.1109/TSP.2014.2309073

start page

• 2250

end page

• 2264

volume

• 62

issue

• 9

URL

• http://dx.doi.org/10.1109/tsp.2014.2309073