5th Annual
Conference
Mathematical Methods in Counterterrorism
Abstracts
Algorithmic Prediction of Terrorism Surges: Non-linear dynamics approach
Vladimir Keilis-Borok
Institute of Geophysics and Planetary Physics and Department of Earth and Space Science
University of California, Los Angeles
Abstract:
Surges of terrorism are explored as rare extreme events (critical phenomena) in a
hierarchical complex system. Prediction is based on the system's background activity: we
detect there premonitory patterns that emerge more frequently, as an extreme event
approaches. Such patterns might be either perpetrators contributing to preparation of an
extreme event, or witnesses merely signaling that the system has become unstable, ripe
for such an event. Predictions are formulated by a discrete sequence of alarms, each
indicating time interval, area, and magnitude range of a future extreme event. Methodology
integrates modeling of statistical mechanics kind and pattern recognition of infrequent
events, developed by the artificial intelligence school of I. Gelfand. Pilot analysis of
real data on terrorism in Israel, Pakistan, Russia, Turkey, and Spain suggests common types
of self-adjusting premonitory patterns, previously found also before other extreme events.
These patterns can be used also in combination with other prediction methods.
Inevitably for complex systems expected accuracy of predictions is limited. Theory of
decision-making is applied to choose optimal counterterrorism preparedness measures,
undertaken in response to prediction.