Social Behavior

Social behavior research aims to understand how individuals interact and influence each other, focusing on both the micro-level (individual actions and interactions) and macro-level (group dynamics and societal outcomes). Current research employs diverse approaches, including agent-based modeling, deep reinforcement learning, and transformer-based natural language processing, to simulate and analyze social interactions in various contexts, from pedestrian navigation to resource allocation. These advancements are improving our understanding of human social dynamics and informing the design of more socially intelligent robots and systems, with implications for fields like robotics, human-computer interaction, and even energy management.