Agent-Based Simulation of Collective Cooperation: From Experiment to Model

TL;DR

This paper develops a psychological agent-based model for simulating collective cooperation in dense crowds, addressing limitations of classic models by incorporating perception, decision-making, and cooperative behaviors observed in experiments.

Contribution

It introduces a novel model that integrates cognitive and perceptual processes to simulate cooperative crowd behaviors, improving realism over traditional collision-avoidance models.

Findings

Agents' successful navigation emerges from psychological interactions.

The model accurately reproduces observed cooperative behaviors.

Experimental data supports the incorporation of perception and decision-making.

Abstract

Simulation models of pedestrian dynamics have become an invaluable tool for evacuation planning. Typically crowds are assumed to stream unidirectionally towards a safe area. Simulated agents avoid collisions through mechanisms that belong to each individual, such as being repelled from each other by imaginary forces. But classic locomotion models fail when collective cooperation is called for, notably when an agent, say a first-aid attendant, needs to forge a path through a densely packed group. We present a controlled experiment to observe what happens when humans pass through a dense static crowd. We formulate and test hypothesis on salient phenomena. We discuss our observations in a psychological framework. We derive a model that incorporates: agents' perception and cognitive processing of a situation that needs cooperation; selection from a portfolio of behaviours, such as being cooperative; and a suitable action, such as swapping places. Agents' ability to successfully get through a dense crowd emerges as an effect of the psychological model.