Date of Award
2022
Document Type
Thesis (Undergraduate)
Department or Program
Computer Science
First Advisor
Soroush Vosoughi
Second Advisor
Alberto Quattrini Li
Third Advisor
Charles Palmer
Abstract
Terrorism is a threat to global security and instills fear in the lives of people across the world. Over the past decades, billions in \$USD have been invested in counter-terrorism efforts. One approach to counter-terrorism is to destabilize terrorist organizations such that they are less effective at carrying out attacks. Previous work has investigated how to best proceed in this direction, such as which terrorists to target. Terrorist organizations have also been modeled as networks, where nodes can represent factions and/or terrorists. Research has been done to understand the network dynamics and link the structure of such networks to their lethality, and these measures have been used to determine the set of actions that minimize a network's lethality. These actions typically consist of modifying or removing nodes from the network, but this paper considers a new action: modifying the edges in the network, that is, changing the relationship between two factions or terrorists. We use existing methods of network lethality analysis on faction-faction networks, and we assess the viability of this action against more established ones in terms of reducing the lethality. We find that adjusting relationships between factions is a viable and cost-effective method, and it calls for further investigation.
Recommended Citation
Keane, John, "Destabilizing Terrorist Networks" (2022). Dartmouth College Undergraduate Theses. 268.
https://digitalcommons.dartmouth.edu/senior_theses/268