Mean-Field-Type Game Theory I: Foundations and New Directions
Foundations Directions: Mean-Field-Type Game Theory I is a theory-focused volume on foundational results and applications of mean-field-type games. It covers stochastic maximum principles, dynamic programming in discrete and continuous time, methods for modeling risk and uncertainty, and distributed mean-field learning. The listing describes it as the first of two volumes and positions it for researchers and advanced undergraduate and graduate students.
At a glance
- Treats foundational and advanced topics in mean-field-type game theory.
- Covers stochastic maximum principles and dynamic programming in both discrete and continuous time.
- Includes techniques for modeling risk and uncertainty, plus distributed mean-field learning algorithms.
- Presented as the first of two volumes, with applications including cloud social networking, smart energy, transportation and wireless networks.
Reference price: $119.99
Price checked: Nov 14, 2026
View offerAhead of Black FridayExplore the selectionPros and cons
Pros
- Broad coverage connects core theory with modeling, analysis and distributed learning topics.
- The listing identifies both advanced students and academic researchers as intended readers.
Technical specifications
8 specifications| Publisher | Birkhäuser |
|---|---|
| Publication date | February 7, 2026 |
| Language | English |
| Print length | 916 pages |
| ISBN-13 | 978-3032070265 |
| ISBN-10 | 3032070260 |
| Dimensions | 6.14 x 1.88 x 9.21 inches |
|---|---|
| Weight listed | 3.2 pounds |
Who is it for?
A good fit for
- Academic researchers working on mean-field-type game theory and related applications.
- Advanced undergraduate and graduate students seeking a substantial theory reference.
Less suited to
- Readers seeking an introductory, nontechnical overview or a short survey.
Frequently asked questions
What topics does this book cover?
The listing describes foundational results, stochastic maximum principles, discrete- and continuous-time dynamic programming, risk and uncertainty modeling, 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.
Is this the only volume?
No. The listing identifies it as the first of two volumes that together provide a broader treatment of mean-field-type game theory and applications.
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