A Computational Model of Resources and Resilency: Deploying the Elements of National Power for Strategic Influence
Abstract
This project involves development of an innovative new agent-based model o f the strategic use of elements of national power. The PI draws on research from environmental science to model how resources are accessed and deployed in strategic decision-making. The aim is to determine how !SIS has emerged after being dormant for many years and to document and model how the movement has acquired resources and launched its information campaign that has attracted recruits across the globe. The modeling strategy will advance the PI s course of action simulator model of state-level motivators of deterrence, which is largely recognized by scientists as a leading data analytic tool to assess state capacities to resist challenge and state vulnerabilities to challenge. The result will be a computational model and new theoretical insights on how political, social, and technological systems interact to determine sociopolitical (in)stabil ity. The PI s approach involves leveraging existing data on state responses to challenges, such as those currently being enacted through ISIS, Boko Harem, Al-Qaeda. The Pl s approach entails modeling resource dynamics us ing models from economics, computational sciences, environmental sciences, and neuroscience to understand how resources are managed and deployed, as well as examining resource fungibility to explain how some states are able to resist challenges by re-deploying resources, while others have limited opportunities due to resource constraings. The PI will begin by refining the model against a unique one-of-a-kind database representing dynamics from the Iranian-Saudi Cold War. Once refined, the model will be tested against new data being collected on the Daesh/ISIS conflict in Syria and Iraq. The PI has already developed a proof-of-concept model of dyadic deterrence that will provide the foundation for the new agent-based modeling strategy. It uses an eight-dimensional space dynamic regression modeling approach, employing an adaptive genetic algorithm to capture state adaptive behaviors. In addition, the PI has identified a strategically selected case sgudy (i.e., Iran-Saudi Arabia Cold War) to lend interpretation and validity to the computational model.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 12, 2017
- Source ID
- W911NF1510291
Entities
People
- William Rivera
Organizations
- Army Contracting Command
- Duke University
- Office of the Secretary of Defense