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.

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.

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.