|dc.description.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.||en