The age of gossip: spatial mean field regime
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Mean field asymptotics of Markov decision evolutionary games and teams
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
ACM SIGMETRICS Performance Evaluation Review
Mean Field Games: Numerical Methods
SIAM Journal on Numerical Analysis
Mean field stochastic games for SINR-based medium access control
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Modeling crowd dynamics by the mean-field limit approach
Mathematical and Computer Modelling: An International Journal
Distributed control of multi-agent systems with random parameters and a major agent
Automatica (Journal of IFAC)
Distributed Strategic Learning for Wireless Engineers
Distributed Strategic Learning for Wireless Engineers
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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 optimality equations by using mean field dynamic programming principle and Kolmogorov forward equations.