Convex Game

Convex games, a class of cooperative games with concave utility functions, are a focus of current research in multi-agent systems and machine learning, primarily addressing the challenge of fair and efficient reward allocation among agents. Researchers are developing algorithms, such as those based on the Shapley value and optimistic multiplicative weight updates, to learn optimal strategies and achieve no-regret learning in various settings, including stochastic and risk-averse scenarios. These advancements have implications for diverse applications, such as energy networks and online markets, by enabling improved cooperation and resource allocation in multi-agent systems.