Mean-Field-Type Game Theory I

Mean-Field-Type Game Theory I is the first of two volumes that together form a comprehensive treatment of mean-field-type game theory and applications focused on finding state-of-the-art solutions to issues surrounding the next generation of cloud social networking, smart energy systems, transportation and wireless networks. The text shows how mean-field-type game theory provides the ideal framework for designing robust, accurate and efficient algorithms for the autonomous and distributed architectures on which future cities and networks will rely to improve the efficiency and flexibility, security and quality of life.

This first volume enables readers to develop a solid understanding of mean-field-type game theory. It covers key theoretical results such as the stochastic maximum principle and dynamic programming in both discrete and continuous time. The book also covers a wide range of techniques for modeling, designing and analyzing risk and uncertainties using game theory, as well as state-of-the-art distributed mean-field learning algorithm techniques.

Mean-Field-Type Game Theory I: Foundations and New Directions is an ideal resource for academic researchers, and advanced undergraduate and graduate students, surveying basic ideas and advanced topics.

Autorentext

Tamer Basar has received B.S.E.E. from Robert College, Istanbul, and M.S., M.Phil, and Ph.D. degrees in engineering and applied science from Yale University. After stints at Harvard University, Marmara Research Institute (Gebze, Turkey), and Bogaziçi University (Istanbul), he joined the University of Illinois Urbana-Champaign in 1981, where he is currently Swanlund Endowed Chair Emeritus; CAS Professor Emeritus of ECE; and Research Professor, CSL and ITI. He is a member of the US National Academy of Engineering, a Fellow of the American Academy of Arts and Sciences, and Foreign Member of Academia Europaea. He is also Fellow of IEEE, IFAC, SIAM, AAAI, and AIIA. He has received several awards and recognitions over the years, and has current research interests in stochastic teams, games, and networks (with finite- and infinite-population models); multi-agent systems and learning; data-driven distributed optimization; epidemics modeling and control over networks; design of incentive mechanisms; strategic information transmission, spread of disinformation, and deception; security and trust; energy systems; and cyber-physical systems.

Boualem Djehiche received his Ph.D. in Mathematics from KTH Royal Institute of Technology, Stockholm, in 1993. Since 2001, he has been Professor of Mathematical Statistics at KTH, where his research spans stochastic analysis, large deviations, stochastic partial differential equations, stochastic control, optimization, and mean-field-type game theory. He has made significant contributions to applications of stochastic systems, control, and game theory in diverse domains, including insurance mathematics, mathematical finance, and mathematical epidemiology, as well as emerging areas such as multi-level multi-compartment building evacuations, pedestrian flow management, blockchain token economics, and generative artificial intelligence. His work bridges rigorous mathematical theory with pressing real-world challenges, advancing the design of reliable, efficient, and adaptive strategies for decision-making under uncertainty.

Hamidou Tembine is co-founder of Timadie and Professor of Machine Intelligence at the School of Engineering, University of Québec (Canada). He received a master's degree in Applied Mathematics from École Polytechnique, Palaiseau (France), a master's degree in game theory and economics, and a Ph.D. in computer science from INRIA and the University of Avignon. He is founding director of the Learning and Game Theory Laboratory and one of the principal investigators of the Center on Stability, Instability, and Turbulence. He has also co-founded Grabal, WETE, and AI Mali, and founded Guinaga, SK1 Sogoloton, and WETE. He is the author of more than 300 publications and several books, including Distributed Strategic Learning for Engineers (CRC Press), Game Theory and Learning in Wireless Networks (Elsevier), Mean-Field-Type Games for Engineers, Machine Intelligence in Africa in 20 Questions, and GPT Meets Game Theory. He is a senior member of IEEE, recipient of the IEEE ComSoc Outstanding Young Researcher Award, and winner of more than ten best paper awards, all in game theory. He has been recognized as a Next Einstein Fellow (2017) and Simons Senior Fellow (2020). His current research interests span learning, evolution, and games, with applications in agriculture, food, water, energy, communications, transportation, healthcare, textless audio-to-audio machine intelligence and people-centered cyber-physical systems security.

Inhalt

Part 1. Discrete State Markov Games of Mean-Field Type.- Chapter 1. One-Shot Mean-Field-Type Games.- Chapter 2. Markov Games.- Chapter 3. Mean-Field-Type Games with Discrete State Spaces.- Part 2. Equilibrium Principles.- Chapter 4. Stochastic Maximum Principle.- Chapter 5. Dynamic Programming Principle.- Part 3. Classes of Mean-Field-Type Games .- Chapter 6. Non Asymptotic Mean-Field-Type Games.- Chapter 7. Linear-Quadratic Mean-Field and Mean-Field-Type Differential Games.- Chapter 8. Mean-Field-Type Games with Jump and Regime Switching.- Chapter 9. MASS: Master Adjoint Systems.- Chapter 10. Semi-Explicit Solutions in Non-Quadratic Mean-Field-Type Games.- Chapter 11. Stackelberg Mean-Field-Type Games.- Chapter 12. Mean-Field-Type Games Driven by Rosenblatt Noises.- Chapter 13. Mean-Field-Type Games with Asymmetric Information.- Chapter 14. Difference Games of Mean-Field Type.- Part 4. Wrap-up.- Chapter 15. Conclusions and New Directions.