Decentralized Fictitious Play in Near-Potential Games With Time-Varying Communication Networks Academic Article

abstract

• We study the convergence properties of decentralized fictitious play (DFP) for the class of near-potential games where the incentives of agents are nearly aligned with a potential function. In DFP, agents share information only with their current neighbors in a sequence of time-varying networks, keep estimates of other agents' empirical frequencies, and take actions to maximize their expected utility functions computed with respect to the estimated empirical frequencies. We show that empirical frequencies of actions converge to a set of strategies with potential function values that are larger than the potential function values obtained by approximate Nash equilibria of the closest potential game. This result establishes that DFP has identical convergence guarantees in near-potential games as the standard fictitious play in which agents observe the past actions of all the other agents.

authors

• Eksin, Ceyhun

published proceedings

• IEEE CONTROL SYSTEMS LETTERS

author list (cited authors)

• Aydin, S., Arefizadeh, S., & Eksin, C.

citation count

• 2

publication date

• January 2022

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Control Systems Letters  Journal

keywords

• Autonomous Systems
• Convergence
• Frequency Estimation
• Game Theory
• Games
• Heuristic Algorithms
• Nash Equilibrium
• Networked Control Systems
• Numerical Algorithms
• Standards
• Time-frequency Analysis

Digital Object Identifier (DOI)

• 10.1109/LCSYS.2021.3090651

start page

• 1226

end page

• 1231

volume

• 6

URL

• http://dx.doi.org/10.1109/lcsys.2021.3090651