a crime simulation model based on social networks and swarm intelligence
Experience in the domain of criminology has shown that spatial data distribution of crime in urban centers follows a Zipf law in which few places concentrate most of the crimes while several other places have few crimes. In order to reproduce and better understand the nuances of such a crime distribution profile, we introduce in this paper a novel multi-agent-based crime simulation model that is directly inspired by the swarm intelligence paradigm. In this model, criminals are regarded as distributed entities endowed with the capability to pursue self-organizing behavior by considering their individual (local) activities as well as the influence of other criminals. Through controlled experiments with the simulation model, we could indeed observe that self-organization phenomena (i.e. criminal behavior toward crime) emerge as the result of both individual and social learning factors. At the same time, our experiments reveal that the spatial distribution of crime achieved by experimenting with the simulation model closely follows the real crime data distribution as expected.
social networks swarm intelligence multiagent simulation application for law enforcement