Probabilistic Approach to Mean Field Games and Mean Field Type Control Problems with Multiple Populations
arXiv:1911.11501
Abstract
In this work, we systematically investigate mean field games and mean field type control problems with multiple populations using a coupled system of forward-backward stochastic differential equations of McKean-Vlasov type stemming from Pontryagin's stochastic maximum principle. Although the same cost functions as well as the coefficient functions of the state dynamics are shared among the agents within each population, they can be different population by population. We study the mean field limit for the three different situations; (i) every agent is non-cooperative; (ii) the agents within each population are cooperative; and (iii) the agents in some populations are cooperative but those in the other populations are not. We provide several sets of sufficient conditions for the existence of a mean field equilibrium for each of these cases. Furthermore, under appropriate conditions, we show that the mean field solution to each of these problems actually provides an approximate Nash equilibrium for the corresponding game with a large but finite number of agents.
Forthcoming in Minimax Theory and its Applications. 50 pages
References in corpus (9)
- Solving high-dimensional partial differential equations using deep learning
- Linear-Quadratic Mean Field Games
- Mean Field Control and Mean Field Game Models with Several Populations
- A Mean Field Game of Portfolio Trading and Its Consequences On Perceived Correlations
- Mean Field Games with Partial Information for Algorithmic Trading
- On finite population games of optimal trading
- Extended Mean Field Games with Singular Controls
- Mean-Field Leader-Follower Games with Terminal State Constraint
- Policy Optimization for Linear-Quadratic Zero-Sum Mean-Field Type Games