Radicalization under deep uncertainty: a multi-model exploration of activism, extremism, and terrorism

摘要: Many security-related phenomena are both dynamically complex and deeply uncertain. The consequences of failing to address these two characteristics may be severe in the security domain. Radicalization, possibly culminating terrorism, is a phenomenon with characteristics. In this article, we use an exploratory multi-model approach generate explore plausible dynamics radicalization under deep uncertainty. Three system simulation models related phenomenon-based introduced used ensembles dynamics. These analyzed machine-learning techniques design adaptive policies that robust Finally, counter-intuitive findings—related as well model-based analysis radicalization-like phenomena—are presented. findings would not have been discovered without multi-models approach, broad exploration uncertainty space, advanced machine learning techniques. Copyright © 2014 System Dynamics Society