New general mechanistic model for predicting civil disturbances and their characteristics

Abstract

Since the wave of civil violence in the USA in the 1960s, many social theorists have tried to explain why riots occur. Despite at least 50 years of research since then, there is still not enough insight to anticipate large events like the 2011 Arab Spring and London riots. The main goal of this thesis is therefore to improve understanding about how underlying conditions influence and drive riot dynamics, such as the intensity, spread, and duration. I develop a new mechanistic and stochastic agent-based model for riots. Previous models have either only targeted general phenomena associated with riots, or aimed at behaviour specific to a single event. In this thesis I combine both approaches: I demonstrate how the model in which the motivation of the agents is based on general concepts, can be applied to the specific situation of the 2011 London riots. The model reproduces the majority of the behaviour observed in the London riots (r = 0.4-0.8). One of the key factors under investigation is the relationship between protests and outbursts of civil violence. Riots are often preceded by protests, such that a large pool of potential rioters is directly available. I find that the number of times a protest is repeated has greater influence on riot dynamics than the protest crowd size. The support shown during demonstrations might incite false confidence in individuals, potentially leading to quicker escalation. Another question is how contact networks and collective identity influence the spread of violence between different locations. The role of online social media (e.g. Twitter) has been a major focus in trying to explain why the violence in the 2011 Arab spring spread so quickly and so far. I investigate the role of social similarity as another factor that might have contributed to the diffusion of unrest, and demonstrate the existence of a critical transition in riot activity when increasing the density of the contact network in the model. Such increases in density beyond the critical thresholds might have been introduced by online social networks. Finally, I explore the sensitivity to cooperation of different potential riot groups. In some cases, mixed populations with different collective identities can form coalitions within neighbourhoods based on shared grievances, which could lead to increases in riot size and riot probability. I examine the influence of the social structure and spread of these populations over different neighbourhoods, as well as the overlap in grievances and different demographic structures.