A dynamic state-based model of crowds

As a discipline, crowd science has acknowledged the need to understand the nature of human collective phenomena before trying to explain them, and a number of attempts have been made to specify and classify different crowd types and behaviours. However, these typologies are often partial, over-fitted to a specific crowd type, or use arbitrary and/or subjective labels for behaviours of complex origin (for example, "panic"). Moreover, they tend to be relatively inflexible, and do not reflect the fluid nature of crowd behaviour (and how this might influence the crowd's structure and impact over time). For example, a static typology might not capture a situation in which a peaceful demonstration can quickly turn into a riot, or how a physical crowd moving around a shopping mall can suddenly become united into a psychological crowd in response to a shared grievance or an external threat. In this paper, we present an alternative to the typology approach; a dynamic, state-based model of crowds, structured around an existing assembly-action-dispersal framework. Our model draws on the statechart formalism from computer science. This approach is relatively objective, can capture the dynamic evolution of a crowd over time, and (unlike existing typologies, which are relatively static) allows for the natural description of how sub-groups emerge within a crowd. This new model may be useful for describing the evolution of incidents such as riots or emergencies, but it is equally well-suited to the study of expected, "normal" crowds.