A data-driven classification framework for conflict and instability analysis

Kihoon Choi, Krishna R. Pattipati, Victor Asal

Abstract: Is it possible to identify and even forecast well in advance (6-12 months) the relative stability of a state to enable policy makers to successfully intervene? How does one acquire that understanding? One technique is to model and understand the social factors, which summarize the background conditions, attributes and performance factors of the country over time. The purpose of this paper is to: (1) present a generalized data-driven framework for conflict analysis and forecasting, (2) show that state-of-the-art pattern classification techniques provide significant improvements to forecasting accuracy, and (3) introduce classification problems arising in social sciences to the engineering community for further enhancement of analysis techniques. We evaluate the efficacy of our data-driven framework on macro-structural factors as relevant contributors to country instability, delineating the independent and dependent variables. The results demonstrate significant improvement over previous approaches in classification metrics of accuracy, precision, and recall.