New insights into terrorism radicalization: uncertainty quantification through stochastic modelling
Vasileios Ε. Papageorgiou
Abstract
Radicalization, the progressive shift towards radical political or religious ideologies, can escalate into terrorism, posing a persistent threat to national security. In this paper, a novel stochastic modelling framework, based on a continuous-time Markov chain is introduced, to accurately capture the severity and dynamics of extremism transmission. Unlike deterministic approaches, our method accounts for both average trends and the inherent randomness of social dynamics. While prior studies largely relied on the basic reproduction number (R0) to assess the intensity of extremism proliferation, our framework offers robust stochastic descriptors that quantify the uncertainty surrounding this complex phenomenon. We examine the distribution and moments of the number of extremists and the probability of a susceptible individual adopting extremist tendencies. Additionally, formulas for computing elasticities are derived, offering insights into how model parameters influence key stochastic descriptors. This enables the identification of the most influential factors driving the spread of extremism and informs the design of more targeted and effective anti-terrorism interventions. The usefulness of the proposed model is evaluated using annual data on terrorism convictions in Sweden, with particular emphasis on estimating the potential effectiveness of the 2023–2024 counterterrorism programme.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.