A Bayesian belief network of threat anticipation and terrorist motivations
2010
Recent events highlight the need for efficient tools for anticipating the threat posed by terrorists, whether individual or
groups. Antiterrorism includes fostering awareness of potential threats, deterring aggressors, developing security
measures, planning for future events, halting an event in process, and ultimately mitigating and managing the
consequences of an event. To analyze such components, one must understand various aspects of threat elements like
physical assets and their economic and social impacts. To this aim, we developed a three-layer Bayesian belief network
(BBN) model that takes into consideration the relative threat of an attack against a particular asset (physical layer) as
well as the individual psychology and motivations that would induce a person to either act alone or join a terrorist group
and commit terrorist acts (social and economic layers). After researching the many possible motivations to become a
terrorist, the main factors are compiled and sorted into categories such as initial and personal indicators, exclusion
factors, and predictive behaviors. Assessing such threats requires combining information from disparate data sources
most of which involve uncertainties. BBN combines these data in a coherent, analytically defensible, and understandable
manner. The developed BBN model takes into consideration the likelihood and consequence of a threat in order to draw
inferences about the risk of a terrorist attack so that mitigation efforts can be optimally deployed. The model is
constructed using a network engineering process that treats the probability distributions of all the BBN nodes within the
broader context of the system development process.
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