Stackelberg Game

Stackelberg games model strategic interactions where a leader commits to a strategy, influencing a follower's best response. Current research focuses on extending the framework to handle uncertainties, incomplete information (e.g., about follower objectives), and decentralized learning scenarios, often employing reinforcement learning, game-theoretic algorithms, and optimization techniques to find optimal or near-optimal strategies for both leader and follower. These advancements are improving the applicability of Stackelberg games to diverse fields, including security, resource allocation, and multi-agent systems, by providing robust and efficient solutions for complex decision-making problems under uncertainty. The development of algorithms that handle non-myopic followers and address distributional robustness are particularly significant recent contributions.