Agent Based Model

Agent-based modeling (ABM) simulates complex systems by modeling the interactions of individual agents within an environment, aiming to understand emergent system-level behavior. Current research emphasizes scaling ABMs to larger populations, integrating advanced techniques like large language models and neural networks to enhance agent realism and decision-making, and developing methods for efficient calibration and validation using techniques like process mining and variational inference. This approach finds applications across diverse fields, from predicting disease outbreaks and optimizing organizational efficiency to understanding financial markets and social movements, offering valuable insights for policy design and scientific discovery.