Aggregative Game

Aggregative games model strategic interactions where each player's cost depends on their own actions and the aggregate actions of all players. Current research focuses on developing efficient algorithms to find Nash equilibria in these games, particularly within complex settings like multi-cluster systems and those incorporating risk-sensitive decision-making using techniques such as multi-agent reinforcement learning and cumulative prospect theory. These models find applications in diverse areas such as energy management and resource allocation, offering valuable tools for analyzing and optimizing large-scale systems with interacting agents. The development of linearly convergent algorithms and the incorporation of risk aversion are key advancements improving the practicality and applicability of these models.