Evolutionary Game

Evolutionary game theory uses mathematical models to study how strategies evolve within populations interacting in games, aiming to understand the emergence of cooperation and other collective behaviors. Current research focuses on developing algorithms, such as evolutionary algorithms and reinforcement learning, to generate and analyze games, often employing models like finite state machines or neural networks to represent agents and their strategies. This field is significant for its applications in diverse areas, including artificial intelligence, network security, and the analysis of complex systems, providing insights into the dynamics of competition and cooperation in both natural and artificial settings.