This volume is based on lectures delivered at the 2020 AMS Short
Course ""Mean Field Games: Agent Based Models to Nash Equilibria,""
held January 13-14, 2020, in Denver, Colorado. Mean field game
theory offers a robust methodology for studying large systems of
interacting rational agents. It has been extraordinarily successful
and has continued to develop since its inception. The six chapters
that make up this volume provide an overview of the subject, from
the foundations of the theory to applications in economics and
finance, including computational aspects. The reader will find a
pedagogical introduction to the main ingredients, from the
forward-backward mean field game system to the master equation.
Also included are two detailed chapters on the connection between
finite games and mean field games, with a pedestrian description of
the different methods available to solve the convergence problem.
The volume concludes with two contributions on applications of mean
field games and on existing numerical methods, with an opening to
machine learning techniques.
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