Exploring the Determinants of Crime-Terror Cooperation using Machine Learning

ConclusionsThe study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data.
Source: Journal of Quantitative Criminology - Category: Criminology Source Type: research