Theoretical and Practical Considerations Regarding the Use of Conditional Probability Assessment Algorithm in Early Warning Tasks Aimed to Prevent Acts of Terrorism and Violent Extremism

Abstract

Over the last decade, the diversified spectrum of hybrid threats forced security agencies around the world to refine the tools used by intelligence analysts in early warning tasks. Thus, in order to anticipate and prevent violent acts, law enforcement analysts from the departments responsible for prevention and countering terrorism, violent extremism, and radicalization that lead to terrorism are constantly striving to identify tools and methods that are capable to measure the psychosocial–behavioral characteristics of individuals in terms of mathematical probabilities. Therefore, risk assessment tools targeting terrorists and violent extremists are a major topic of interest for international security experts and law enforcement professionals. In such context, the present study proposes a tool to assess conditional probabilities, built on the Bayes’ rule, which is based on a subjective way of defining probability. Bayes’ rule allows analysts to correctly quantify their estimates, which are often qualitative, subjective, and based on a limited number of indices and observations. The risk assessment tools developed for common violent crimes and for members of the organized crime groups should not be used for assessing terrorists, violent extremists, or individuals suspected of engaging in ideologically motivated violent actions, nor vice versa. Specific risk assessments with relevant indicators should be used separately for regular violent offenders and for terrorists and extremists.