(Abstract) An Agent-based Simulation of Riot Behavior

A riot, in its most basic form, is an unorganized crowd where widespread aggression occurs. The facts that not every crowd leads to a riot, and that not every act of aggression constitutes a riot, make it clear that there have to be certain factors present that are either necessary for, or at least strongly encourage rioting behavior. The goals of this project are to validate candidates for these factors and to develop a computer model that captures the “turnover-point” where a crowd becomes a riot. In a meta-analysis of studies on rioting, Postmes and Spears [1] argue that, contrary to a more intuitive understanding of the psychology of the riot phenomenon, deindiviudation plays a much smaller role than previously thought: Psychology-oriented accounts do in fact stress the role of individual agents within the pre-riot crowd. Of special interest for this project are, for example, those individuals Haddock and Polsby [2] call the entrepreneurs; in a sense the nuclei of aggression, around which larger patterns of violent behavior begin to grow. Further archetypes identified within a rioting crowd are hangers-on, as well as more passive bystanders.

The simulation itself is realized in NetLogo, a multi-agent based simulation suite particularly suited for the modeling of social phenomena and other distributed processes. A generic crowd situation with two opposed groups of individual agents is realized in a simple simulated environment. The up to 400 agents are able to scan their surroundings, and to change their behavior depending on both the given situation and certain individual traits. The program incorporates elements of an approach-avoidance model by Jager et al. [3], and of a cognitive system developed by Wijermans et al. [4]. The assumption is made that agents within a crowd are influenced by factors on three different levels: the environment; the groups present in the situation; and the individuals. More specifically, factors like crowd density, noise levels and temperature on the environmental level, and in/out-group, friendship, and leadership relationships on the group level are considered. On the intra-individual level of agents, physiological factors like energy, and functional factors such as saliency, motivation, and insecurity influence and modify behavior selection.

The results indicate that the simulation is capable to reproduce some important characteristic roles agents might play within a riot, such as the aforementioned entrepreneurs, hangers-on, and bystanders. These roles and their associated behavioral patterns are observable within the graphical simulation of the crowd, and can be linked to properties based on the assumed factors described above. It is hoped that further work on this first model can provide helpful insights and concepts for the understanding of the mechanisms and dynamics that underlie riot behavior.

References:

[1] Postmes, R.; Spears, R. (1998) Deindividuation and Antinormative Behavior: A Meta-Analysis. Psychological Bulletin, No. 123, pp. 238–259
[2] Haddock, D.D.; Polsby, D.D. (1994) Understanding Riots. Cato Journal, Vol. 14, No. 1, pp. 147-157.
[3] Jager, W.; Popping, R.; van de Sande, H. (2001) Clustering and Fighting in Two-party crowds. Journal of Artificial Societies and Social Simulation Vol. 4, No. 3.
[4] Wijermans, N.; Jorna, R.; Jager, W.; van Vliet, T. (2007) Modelling Crowd dynamics, influence factors related to the probability of a riot. In: F. Ablard (Ed.). Proceedings of the Fourth European Social Simulation Association Conference. ESSA: Toulouse, pp. 531-541.