TITLE: Terror Queues

SPEAKER: Professor Edward Kaplan

ABSTRACT:

This article presents the first model developed
specifically for understanding the infiltration and interdiction of ongoing
terror plots by undercover intelligence agents, and does so via novel
application of ideas from queueing theory and Markov population processes. The
resulting "terror queue" models predict the number of undetected
terror threats in an area from agent activity/utilization data, and also
estimate the rate with which such threats can be interdicted.  The models
treat terror plots as customers and intelligence agents as servers. Agents
spend all of their time either detecting and infiltrating new terror plots (in
which case they are "available"), or interdicting already detected
terror plots (in which case they are "busy"). Initially we examine a
Markov model assuming that intelligence agents, while unable to detect all
plots, never err by falsely detecting fake plots.  While this model can be
solved numerically, a simpler Ornstein-Uhlenbeck diffusion approximation yields
some results in closed form while providing nearly identical numerical
performance.  The transient behavior of the terror queue model is
discussed briefly along with a sample sensitivity analysis to study how model
predictions compare to simulated results when using estimated versus known
terror plot arrival rates. The diffusion model is then extended to allow for
the false detection of fake plots. Such false detection is a real feature of
counterterror intelligence given that intelligence agents or informants can
make mistakes, as well as the proclivity of terrorists to deliberately
broadcast false information. The false detection model is illustrated using
suicide bombing data from Israel.