Behavioral dynamics seeks to explain stable adaptive behavior as emerging from the interaction between an agent and its environment. Within this framework, we are developing a perceptually-grounded model of locomotor behavior that accounts for pedestrian and crowd dynamics.
At the individual level, consider visually-controlled locomotion through a cluttered environment. Based on experiments in virtual reality, we have developed a pedestrian model that captures basic behaviors such as steering, obstacle avoidance, and pedestrian interactions. Locomotor trajectories emerge on-line from the agent-environment interaction, without appeal to an internal world model or explicit path planning.
At the collective level, crowd behavior is thought to emerge from local interactions between individual pedestrians. Multi-agent simulations of the pedestrian model reproduce patterns of crowd data from key scenarios like Grand Central Station, Swarm, and Counterflow. Individual trajectories and crowd dynamics can be modeled with just a few basic behaviors.
The results support the view that pedestrian and crowd dynamics emerge from local interactions, without internal models or plans, consistent with principles of self-organization.