Equilibrium Selection

Equilibrium selection in multi-agent systems focuses on identifying which of potentially many equilibrium outcomes will emerge in strategic interactions, a crucial problem in fields like economics and AI. Current research emphasizes developing algorithms and models, including mean-field approximations, occupation measure optimization, and equivariant neural networks, to predict and even guide the selection of equilibria, often aiming for Pareto optimality or social welfare maximization. This work is significant because it improves our understanding of complex interactions and enables the design of more efficient and socially beneficial systems, with applications ranging from online auctions to traffic management and multi-agent reinforcement learning. The development of more robust and scalable methods for equilibrium selection remains a key challenge.