Advancing U.S. CWMD/CWMT Capabilities in Support of SIGMA Plus Program through Development of Anticipatory Human Social Systems Models and Adversary Weapon Selection Decision Processes
Abstract
This proposed research addresses challenges in the technical domains of complex social systems and anticipating surprise. Specifically, this research will contribute to the topic areas of Òaccurate and scientifically validated models of the social dynamics underlying different kinds of conflictÓ under the complex social systems domain, and Òdefense against Weapons of Mass Destruction/Weapons of Mass Terror (WMD/WMT) threatsÓ under the anticipating surprise domain in support of the ongoing SIGMA+, Modeling Adversarial Activity (MAA), and Next Generation Social Science (NGS2) programs. Most existent adversary decision-making models specific to the WMD/WMT problem space have been developed based primarily on subject matter expert elicitations and theories presented in the literature. While some efforts have been made previously to allow empirical data to inform these models, very little work has been done to empirically test and validate these models. The state of current adversary decision-making models limits our ability to truly understand the probability space of adversary activities, which limits our ability to plan and execute our intelligence collection strategies and our ability to identify potential WMD/WMT adversary signals. To improve the model fit and the accuracy of the adversary decision-making model in the WMD/WMT problem space, this research develops an empirically grounded and quantitatively bounded Anticipatory Human Social Systems Decision Model and a set of probabilistically weighted Modeled Archetype Adversary Graph Templates to be applied in the counter WMD/WMT problem space.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 24, 2020
- Source ID
- W911NF2010031
Entities
People
- Steve Sin
Organizations
- Army Contracting Command
- Defense Advanced Research Projects Agency
- University of Maryland