Methoden vergelijken

	Civil War Onset Analysis	Machine Learning Conflict Prediction
Vakgebied	International Relations	International Relations
Familie	Process / pipeline	Machine learning
Jaar van ontstaan	2003	2016
Grondlegger	Civil-war research program (e.g., James Fearon & David Laitin; Collier & Hoeffler)	Predictive conflict research (e.g., Muchlinski, Siroky, He & Kocher)
Type	Observational country-year analysis of civil-war onset	Supervised machine-learning prediction of conflict
Oorspronkelijke bron	Fearon, J. D., & Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. American Political Science Review, 97(1), 75–90.	Muchlinski, D., Siroky, D., He, J., & Kocher, M. (2016). Comparing random forest with logistic regression for predicting class-imbalanced civil war onset data. Political Analysis, 24(1), 87–103.
Aliassen	Civil Conflict Onset Analysis, Greed vs. Grievance Analysis, Insurgency Onset Analysis, Determinants of Civil War	ML Conflict Prediction, Random Forest Civil War Prediction, Algorithmic Conflict Prediction, Supervised Learning for Conflict Onset
Verwant	3	3
Samenvatting	Civil war onset analysis is the observational study of why internal armed conflict begins in some countries and years but not others. Organized as country-year data with a binary onset outcome, it models the probability of onset against structural, economic, and political conditions. Fearon and Laitin's (2003) influential analysis argued that civil war is best understood as insurgency, and that the conditions favoring insurgency — weak states, poverty, rough terrain, large populations — predict onset far better than ethnic or religious diversity, reframing the long 'greed versus grievance' debate.	Machine learning conflict prediction uses flexible supervised algorithms — random forests, gradient boosting, neural networks, regularized regression — to forecast the onset of armed conflict from large sets of features, prioritizing out-of-sample predictive accuracy over coefficient interpretation. Muchlinski, Siroky, He, and Kocher (2016) showed that random forests substantially outperform logistic regression at predicting class-imbalanced civil-war onset, catalyzing a shift in conflict research toward algorithmic prediction, rigorous out-of-sample validation, and the recognition that explanation and prediction are distinct goals.