Development of novel numerical methods for forward and inverse problems in mean field game — NSF Award to University of California
Mean field games is the study of strategic decision making in large populations where individual players interact through a certain quantity in the mean field. Mean field games have strong descriptive power in socioeconomics and biology, e.g. in the understanding of social cooperation, stock markets, trading and econom
| Award title | Development of novel numerical methods for forward and inverse problems in mean field game |
|---|---|
| Award ID | 2409903 |
| Awardee | University of California-Riverside |
| City | RIVERSIDE |
| State | CA |
| Amount obligated | $194,627 |
| Principal investigator | Yat Tin Chow |
| Program | COMPUTATIONAL MATHEMATICS |
| Start date | 07/01/2024 |
| Abstract | Mean field games is the study of strategic decision making in large populations where individual players interact through a certain quantity in the mean field. Mean field games have strong descriptive power in socioeconomics and biology, e.g. in the understanding of social cooperation, stock markets, trading and economics, biological systems, election dynamics, population games, robotic control, machine learning, dynamics of multiple populations, pandemic modeling and control as well as vaccinat |
| Source | NSF Awards |
$799/mo
Try NSFGrants →