Empirical Game

Empirical game theory uses simulations and machine learning to analyze strategic interactions, aiming to find equilibrium solutions in complex scenarios where traditional game-theoretic methods are insufficient. Current research focuses on applying reinforcement learning algorithms like PSRO and DreamerV3, often within agent-based modeling frameworks, to solve diverse problems ranging from economic modeling and structural optimization to analyzing the behavior of large language models in classic game theory settings. This approach bridges artificial intelligence and game theory, offering valuable insights into multi-agent systems and informing the design of more robust and efficient algorithms for decision-making in various fields.