Mean Field Games with Diverse Behavioral Patterns, Informational Constraints, and Learning
Abstract
This research program will address several fundamental problems in noncooperative nonzero-sum dynamic games with asymmetric information across a heterogeneous group of players (agents), with diverse behavioral patterns and misaligned objectives, beliefs and perceptions, operating under only partial or no modeling information and not even sharing a common probabilistic outlook, and with possibly multiple layers in decision making. The focus will be on stochastic dynamic games with a large population of networked players, and study of the precise relationship between such games and the corresponding ones with an infinite population (that is, mean-field games), such as the extent to which the mean-field equilibria (MFE) obtained for the latter (under various game-theoretic solution concepts, such as Nash, Stackelberg-Nash, team-optimal, or saddle-point, as appropriate for the underlying game) provide approximate equilibria of a similar type for the former.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 06, 2025
- Source ID
- FA95502410152
Entities
People
- Tamer Başar
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Illinois Urbana–Champaign