We model the hierarchical evolution of an organized criminal network via
antagonistic recruitment and pursuit processes. Within the recruitment phase, a
criminal kingpin enlists new members into the network, who in turn seek out
other affiliates. New recruits are linked to established criminals according to
a probability distribution that depends on the current network structure. At
the same time, law enforcement agents attempt to dismantle the growing
organization using pursuit strategies that initiate on the lower level nodes
and that unfold as self-avoiding random walks. The global details of the
organization are unknown to law enforcement, who must explore the hierarchy
node by node. We halt the pursuit when certain local criteria of the network
are uncovered, encoding if and when an arrest is made; the criminal network is
assumed to be eradicated if the kingpin is arrested. We first analyze
recruitment and study the large scale properties of the growing network; later
we add pursuit and use numerical simulations to study the eradication
probability in the case of three pursuit strategies, the time to first
eradication and related costs. Within the context of this model, we find that
eradication becomes increasingly costly as the network increases in size and
that the optimal way of arresting the kingpin is to intervene at the early
stages of network formation. We discuss our results in the context of dark
network disruption and their implications on possible law enforcement
strategies.Comment: 16 pages, 11 Figures; New title; Updated figures with color scheme
better suited for colorblind readers and for gray scale printin