Julian Barreiro-Gomez is a Post-Doctoral Associate in the Learning & Game Theory Laboratory (L&G-Lab) at the New York University in Abu Dhabi (NYUAD), United Arab Emirates, and since 2019, he is also with the Research Center on Stability, Instability and Turbulence (SITE) at the New York University in Abu Dhabi (NYUAD).

Hamidou Tembine is presently affiliated with New York University in Abu Dhabi (NYUAD), United Arab Emirates. He is a prolific Researcher and has been co-organizer of several scientific meetings on game theory in networking, wireless Communications, smart energy systems, and smart transportation systems.

Klappentext

This book comprises an appropriate background to work and do research on mean-field-type control and game theory. It starts with studying the deterministic optimal control and differential linear-quadratic games, and progressively moves to analyzing mean-field-type control and game problems incorporating several stochastic processes.

Inhalt

I. Preliminaries. 1. Introduction. II. Mean-Field-Free and Mean-Field Games. 2. Mean-Field-Free Games. 3. Mean-Field Games. III. One-Dimensional Mean-Field-Type Games. 4. Continuous-Time Mean-Field-Type Games. 5. Co-opetitive Mean-Field-Type Games. 6. Mean-Field-Type Games with Jump-Di usion and Regime Switching. 7. Mean-Field-Type Stackelberg Games. 8. Berge Equilibrium in Mean-Field-Type Games. IV. Matrix-Valued Mean-Field-Type Games. 9. Matrix-Valued Mean-Field-Type Games. 10. A Class of Constrained Matrix-Valued Mean-Field-Type Games. V. Discrete-Time Mean-Field-Type Games. 11. One-Dimensional Discrete-Time Mean-Field-Type Games. 12. Matrix-Valued Discrete-Time Mean-Field-Type Games. VI. Learning Approaches and Applications. 13. Constrained Mean-Field-Type Games: Stationary Case. 14. Mean-Field-Type Model Predictive Control. 15. Data-Driven Mean-Field-Type Games. 16. Applications.