Assessing nation-state instability and failure

• Published in: 2006 IEEE Aerospace Conference p. 18 pp.
• Main Authors: Popp, R., Kaisler, S.H., Allen, D., Cioffi-Revilla, C., Carley, K.M., Azam, M., Russell, A., Choucri, N., Kugler, J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2006
• Subjects: Bayesian methods; Cascading style sheets; Computational modeling; Decision making; Dynamic range; Equations; Hidden Markov models; Predictive models; Principal component analysis; Stability
• ISBN: 9780780395459; 078039545X
• ISSN: 1095-323X; 2996-2358
• DOI: 10.1109/AERO.2006.1656054

DARPA initiated a six-month Pre-Conflict Anticipation and Shaping (PCAS) initiative to demonstrate the utility of quantitative and computational social science models (Q/CSS) applied to assessing the instability and failure of nation-states. In this program ten different teams of Q/CSS researchers and practitioners developed nation state instability models and then applied them to two different countries to assess their current stability levels as well as forecast their stability levels 6-12 months hence. The models developed ranged from systems dynamics, structural equations, cellular automata, Bayesian networks and hidden Markov models, scale-invariant geo-political distributions, and multi agent-based systems. In the PCAS program we also explored a mechanism for sensitivity analysis of Q/CSS model results to selected parameters, and we also implemented a mechanism to automatically categorize, parse, extract and auto-populate a bank of Q/CSS models from large-scale open source text streams. Preliminary yet promising results were achieved, and the utility of the results can provide added value for decision-making problems around planning, intelligence analysis, information operations and training. This paper describes the motivation and rationale for the program, the Q/CSS models and mechanisms, and presents results from some of the models. In addition, future research and key challenges in using these Q/CSS models within an operational decision making environment will be discussed