Mean Field Game (MFG) theory studies strategic decision problems in large populations of interacting agents; it is now widely applied in economics, financial markets, engineering, social science and many other areas. The generalization of classical mean field game theory to the study of problems on networks that exhibit heterogeneity, bounded local connections, dynamic dependence and uncertainties in structure is extremely important in terms of theoretical development and practical applications, and that is the focus of this workshop.

The objective of this workshop is to bring together researchers in applied mathematics, mean field games, network science, network games, and systems and control theory to exchange ideas and to work on the extensions of mean field game theory to dynamic game problems on heterogeneous large-scale networks.