Evolutionary Game Theory

Evolutionary game theory uses mathematical models to understand how the strategies of interacting agents evolve over time, driven by selection pressures and learning dynamics. Current research focuses on applying these models to diverse areas, including the adoption of new technologies, the emergence of cooperation in heterogeneous populations, and the design of effective AI regulation, often employing agent-based modeling, reinforcement learning, and replicator dynamics. These studies offer valuable insights into the dynamics of complex systems, informing fields ranging from social sciences and biology to the development of robust and ethical AI systems. The resulting frameworks provide a powerful tool for analyzing and potentially controlling the behavior of populations in various contexts.