No, I do not believe it’s possible to simply break counterterrorism analysis down to a simple mathematical formula. The reason for this is simple: while predictable in most regards, the simple fact remains that so many factors play a role in human decision-making and in human emotions that it’s hard to simply reduce human nature to a mathematical formula. Not to be too dramatic, but every individual is different, and while a certain set of characteristics may exist that have been identified as describing a ‘terrorist,’ the fact remains that a terrorist is a person. Every person is different, having had a unique set of life experiences which may influence their decisions and responses in ways that no one can predict. A psychologist, psychiatrist, or therapist, having studied human nature and human responses to crisis and trauma, may be able to more accurately predict how a terrorist may behave in a given scenario, but these specialists are trained in human nature, and the best of them have years of experience. This process, of interpreting and navigating the recesses of the human mind, cannot be replicated by a simple mathematical formula. Furthermore, it’s difficult to quantify issues such as geopolitical instability and religious fervor, primarily because it’s difficult to translate socio-cultural or religious values to mathematical variables.
Admittedly, counterterrorist efforts often include data from social networks (Macskassy & Provost, 2007). As Macskassy & Provost (2007) observe, one can make safe calculations based on an individual’s associates – “suspicious people may interact with known malicious people” (p. 937). However, such assumptions require careful investigation for confirmation. As difficult as it is reduce counterterrorism analysis to a mathematical formula, it would be equally difficult to reduce the investigative process to a mathematical formula – neither of these processes would remain meaningful, if reduced in such a manner.
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- Macskassy, S., & Provost, F. (2007). Classification in networked data: A toolkit and a univariate case study. Journal Of Machine Learning Research, 8(5), 935-983.
