Modeling Geographic Behavior in Riotous Crowds

Authored by Paul M. Torrens, Aaron W. McDaniel

Date Published: 2013

DOI: 10.1080/00045608.2012.685047

Sponsors: United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Pseudocode

Model Code URLs: Model code not found

Abstract

Under some conditions, tensions among crowd members, harbored a priori or developed on site, might catalyze a crowd to riot, with dramatic consequences. We know perhaps less than we would like to about the processes that drive rioting in crowds because they are difficult to study. In particular, we know relatively little about the influence of geographic behavior on rioting, although there exists a general sense that it is important. In lieu of pragmatic avenues for studying riots, we could use simulation as a synthetic laboratory for exploration. To be useful, simulations must be based on realistic behavioral models, but the extant science for riot modeling has not traditionally provided that support. In this article, we introduce a new approach to modeling riot-prone and riotous crowds using behavior-driven computational agents. We demonstrate a simulation architecture based on socioemotional agents, modeled at atomic levels and characteristic times for riot activity, but extended with the use of geographical functionality that endows agents with spatial perception, cognition, and action that helps to determine where, when, how, and in what contexts and company their agency should be deployed and interpreted. In essence, our agents are polyspatial, with the ability to adapt their behavioral geography under shifting circumstances and to differentially process geographic information from diverse sources. We illustrate the usefulness of this scheme through simulation of a varying set of scenarios for riot formation, evolution, and dissolution, as well as in exploring the interplay among different characteristics, traits, and goals of riot participants.

Tags

Agent-based model Complexity geographic information science riots spatial analysis and modeling