1 research outputs found
Mean Field Game Systems with Common Noise and Markovian Latent Processes
In many stochastic games stemming from financial models, the environment
evolves with latent factors and there may be common noise across agents'
states. Two classic examples are: (i) multi-agent trading on electronic
exchanges, and (ii) systemic risk induced through inter-bank lending/borrowing.
Moreover, agents' actions often affect the environment, and some agent's may be
small while others large. Hence sub-population of agents may act as minor
agents, while another class may act as major agents. To capture the essence of
such problems, here, we introduce a general class of non-cooperative
heterogeneous stochastic games with one major agent and a large population of
minor agents where agents interact with an observed common process impacted by
the mean field. A latent Markov chain and a latent Wiener process (common
noise) modulate the common process, and agents cannot observe them. We use
filtering techniques coupled with a convex analysis approach to (i) solve the
mean field game limit of the problem, (ii) demonstrate that the best response
strategies generate an -Nash equilibrium for finite populations, and
(iii) obtain explicit characterisations of the best response strategies.Comment: 28 page