Large-scale games in large-scale systems
It provides a survey for researchers interested in reducing complexity of stochastic games with huge state/action spaces via mean field limits.
This paper reviews recent advances in large-scale games, focusing on population games, stochastic population games, and mean field stochastic games, and characterizes mean field systems using Bellman and Kolmogorov forward equations for long-term payoffs.
Many real-world problems modeled by stochastic games have huge state and/or action spaces, leading to the well-known curse of dimensionality. The complexity of the analysis of large-scale systems is dramatically reduced by exploiting mean field limit and dynamical system viewpoints. Under regularity assumptions and specific time-scaling techniques, the evolution of the mean field limit can be expressed in terms of deterministic or stochastic equation or inclusion (difference or differential). In this paper, we overview recent advances of large-scale games in large-scale systems. We focus in particular on population games, stochastic population games and mean field stochastic games. Considering long-term payoffs, we characterize the mean field systems using Bellman and Kolmogorov forward equations.