Anticipating the Good, the Bad, and the Ugly

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.Since the end of the cold war, economic dislocations, civil war, famine, and ancient ethnic and religious animosities have contributed to conflict and political instability in states extending from Haiti to the vast archipelago of Indonesia. These conflicts and instabilities frequently challenge national security interests; at other times, the human rights atrocities that often accompany these dislocations offend the moral imperatives of individual states as well as the international community.Increasingly, Western powers, acting alone or in concert with international organizations, have responded to these post-cold war crises in myriad ways, including 791 AUTHOR'S NOTE: The opinions and interpretations expressed herein are my own and are not necessarily shared by the U.S. Army, the Department of Defense, or any other agency or organization of the U.S. government. For helpful comments on earlier drafts, I express my gratitude to

Conflict forecasting and its limits

“…Scholars and policy-makers have also relied on algorithms analyzing large amounts of data using various techniques, ranging from the more traditional regression techniques -typically logistic, due to the binary nature of the outcome -to more intricate random forest or neural network models (Rummel [41]; Brandt and Freeman [6]; Muchlinski et al [34]; Hegre et al [28]). In the fields of international conflict and civil war, the majority of this work has until recently relied on structural variables such as regime type, GDP, ethnicity, or terrain (Beck, King, and Zeng [2]; O'Brien [35]). Unfortunately, these variables are typically measured yearly and vary slowly or not at all (Gleditsch and Ward [20]).…”

Prediction of Intrastate Conflict Using State Structural Factors and Events Data

“…State failure/instability, as forecasted by PITF, are two years forward, which can be valuable for mid-to long-term planning, but is inadequate for more immediate crisis response, as previously suggested by O'Brien (2002). Kalil and Waghorn (2008) applied a logistic regression model to factors associated with the onset of civil war for predicting intrastate conflict within a five-year period.…”

The potential value of seasonal forecasts in a changing climate in southern Africa

“…Seasonal forecasts provide information on the development of the climate up to 6 to 12 months ahead of time rather than detailed day-by-day variations. Probabilistic seasonal forecast systems have significantly more skill than deterministic forecasts (Molteni et al, 2011), and such probabilistic information can be used by end users in support of decisionmaking (see O'brien, 2002;Ramos et al, 2010Ramos et al, , 2013. In this study we use the ECMWF (European Centre for Medium-Range Weather Forecasts) System 4 seasonal forecast system (Molteni et al, 2011), based on an atmosphere-ocean coupled model, which has been operational since November 2011.…”