Thesis Research: Modeling Crowd Behavior Based on Social Comparison Theory(Extended Abstract)

摘要

Modeling crowd behavior is an important challenge for agent-based simulation. My overall goal is to provide a single computational cognitive mechanism that, when executed by individual agents, would give rise to different crowd behaviors, depending on the perceptions and actions available to each individual. I propose a novel model of crowd behavior, based on Social Comparison Theory (SCT), a popular social psychology theory that has been continuously evolving. I am pursuing a concrete algorithmic framework for SCT and evaluating it on different social behaviors. Moreover, I have begun to explore the use of qualitative reasoning techniques to model global (macro-level) social phenomena in demonstrations. I believe that this is the first use of QR techniques for such purposes.