Product guide
Mean-Field-Type Game Theory I: Foundations and New Directions
Directions: Mean-Field-Type Game Theory I is a 916-page English-language volume on theoretical foundations and applications. It covers stochastic maximum principles, dynamic programming, uncertainty modelling and distributed mean-field learning, aimed at researchers and advanced students.
At a glance
- Introduces mean-field-type game theory and its theoretical foundations.
- Covers stochastic maximum principles and dynamic programming in discrete and continuous time.
- Discusses modelling and analysing risk and uncertainty using game theory.
- Includes techniques for distributed mean-field learning and applications to networks and systems.
Pros and cons
Pros
- Broad coverage combines mathematical theory, modelling approaches and distributed learning techniques.
- The stated scope connects the theory to cloud social networking, smart energy, transportation and wireless networks.
- The description positions it for both academic research and advanced undergraduate or graduate study.
Cons
- At 916 pages and 1.45 kg, this is a substantial reference rather than a lightweight introductory read.
Technical specifications
8 specifications| Publisher | Birkhäuser |
|---|---|
| Publication date | 7 February 2026 |
| Language | English |
| Print length | 916 pages |
| ISBN-10 | 3032070260 |
| ISBN-13 | 978-3032070265 |
| Weight listed | 1.45 kg |
|---|---|
| Dimensions | 15.6 x 4.78 x 23.39 cm |
Who is it for?
A good fit for
- Academic researchers working on mean-field-type games, stochastic control or distributed systems.
- Advanced undergraduate and graduate students seeking a broad treatment of the subject.
Less suited to
- Readers looking for a short, non-technical introduction or a practical guide focused on a single application.
Frequently asked questions
What topics does this volume cover?
The description lists stochastic maximum principles, dynamic programming in discrete and continuous time, risk and uncertainty modelling, and distributed mean-field learning techniques.
Who is the book intended for?
It is presented as a resource for academic researchers and advanced undergraduate and graduate students.
What applications are discussed?
The description connects mean-field-type game theory with cloud social networking, smart energy systems, transportation and wireless networks.
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