Agent Based Simulation

Agent-based simulation (ABS) is a computational modeling technique that simulates the interactions of autonomous agents to understand emergent behavior in complex systems. Current research emphasizes integrating qualitative expert knowledge, employing reinforcement learning and other advanced algorithms (like PSRO) for agent decision-making, and enhancing model realism through data assimilation and the incorporation of motivational factors and cognitive biases. ABS finds applications across diverse fields, from healthcare (e.g., gait analysis) and economics (e.g., market simulation) to transportation (e.g., optimizing freight throughput) and public health (e.g., pandemic modeling), offering valuable insights for policy evaluation and system optimization.