Modeling pedestrian crowd behavior based on a cognitive model of social comparison theory

Modeling crowd behavior is an important challenge for cognitive modelers. Models of crowd behavior facilitate analysis and prediction of human group behavior, where people are close geographically or logically, and are affected by each other's presence and actions. Existing models of crowd behavior, in a variety of fields, leave many open challenges. In particular, psychology models often offer only qualitative description, and do not easily permit algorithmic replication, while computer science models are often not tied to cognitive theory and often focus only on a specific phenomenon (e.g., flocking, bi-directional pedestrian movement), and thus must be switched depending on the goals of the simulation. We propose a novel model of crowd behavior, based on Festinger's Social Comparison Theory (SCT), a social psychology theory known and expanded since the early 1950's. We propose a concrete algorithmic framework for SCT, and evaluate its implementations in several pedestrian movement phenomena such as creation of lanes in bidirectional movement and movement in groups with and without obstacle. Compared to popular models from the literature, the SCT model was shown to provide improved results. We also evaluate the SCT model on general pedestrian movement, and validate the model against human pedestrian behavior. The results show that SCT generates behavior more in-tune with human crowd behavior then existing non-cognitive models.