Mean-Field Games Among Teams

EasyChair Preprint no. 11165

Abstract

In this paper, we present a model of a game among teams. Each team consists of a homogeneous population of agents. Agents within a team are cooperative while the teams compete with other teams. The dynamics and the costs are coupled through the empirical distribution (or the mean field) of the state of agents in each team. This mean-field is assumed to be observed by all agents. Agents have asymmetric information (also called a non-classical information structure). We propose a mean-field based refinement of the Team-Nash equilibrium of the game, which we call mean-field Markov perfect equilibrium (MF-MPE). We identify a dynamic programming decomposition to characterize MF-MPE. We then consider the case where each team has a large number of players and present a mean-field approximation which approximates the game among large-population teams as a game among infinite-population teams. We show that MF-MPE of the game among teams of infinite population is easier to compute and is an $\varepsilon$-approximate MF-MPE of the game among teams of finite population.

Keyphrases: approximate information state, approximate Markov perfect equilibrium, games among teams, Markov Perfect Equilibrium, Mean Field Games

@Booklet{EasyChair:11165,
  author = {Jayakumar Subramanian and Akshat Kumar and Aditya Mahajan},
  title = {Mean-Field Games Among Teams},
  howpublished = {EasyChair Preprint no. 11165},

  year = {EasyChair, 2023}}