Collective Behavior

Collective behavior studies how the interactions of individual agents lead to emergent patterns at the group level, aiming to understand and predict these complex dynamics across diverse systems. Current research focuses on developing and applying models like neural networks (including graph neural networks and spiking neural networks), Gaussian processes, and agent-based models to analyze both biological (e.g., fish schools, ant colonies) and artificial (e.g., robot swarms, LLM interactions) systems, often leveraging techniques like inverse reinforcement learning and topological data analysis. This field is significant for advancing our understanding of natural systems and for enabling the design and control of sophisticated multi-agent systems in robotics, AI, and other domains.