Emergent Behavior

Emergent behavior studies how complex, unpredictable patterns arise from the interactions of simpler components within a system, aiming to understand and potentially control these collective phenomena. Current research focuses on diverse areas, including the development of quantitative frameworks to measure emergence in neural networks and the use of large language models (LLMs) and multi-agent systems to simulate and analyze emergent behaviors in various contexts, such as robot swarms and social interactions. These investigations are significant for advancing our understanding of complex systems across disciplines, from neuroscience and robotics to social sciences and economics, and for developing more robust and adaptable AI systems.