Anticipating the good, the bad, and the ugly: An early warning approach to conflict and instability analysis

Abstract

One way to demonstrate progress in a field of scientific inquiry is to show that factors believed to explain some phenomenon can also be used effectively to predict both its occurrence and its nonoccurrence. This study draws on the state strength literature to identify relevant country macrostructural factors that can contribute to different kinds and levels of intensity of conflict and country instabilities. A pattern classification algorithm - fuzzy analysis of statistical evidence (FASE) - is used to analyze the relationships between country macrostructural factors and historical instances of country instability. A split-sample validation design is used to evaluate the ability of FASE to generate competent predictions, using the standard forecasting performance metrics overall accuracy, recall, and precision. The results demonstrate the potential for FASE to accurately forecast not just the occurrence but also the level of intensity of country-specific instabilities out 5 years with about 80% overall accuracy.