Cooperative Game Theory

Cooperative game theory explores how multiple agents can collaborate to achieve mutual benefit, focusing on fair and efficient allocation of resources or rewards. Current research emphasizes developing computationally tractable methods for calculating solution concepts like the Shapley value and the core, particularly within machine learning contexts (e.g., explainable AI, multi-agent reinforcement learning) and for large-scale applications such as energy networks and freight forwarding. These advancements are significant because they provide frameworks for understanding and optimizing collaboration in complex systems, leading to improved resource allocation, fairer reward distribution, and more insightful interpretations of machine learning models.