Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Civil War Onset Analysis× | Machine Learning Conflict Prediction× | |
|---|---|---|
| Domeniu | International Relations | International Relations |
| Familie≠ | Process / pipeline | Machine learning |
| Anul apariției≠ | 2003 | 2016 |
| Autorul original≠ | Civil-war research program (e.g., James Fearon & David Laitin; Collier & Hoeffler) | Predictive conflict research (e.g., Muchlinski, Siroky, He & Kocher) |
| Tip≠ | Observational country-year analysis of civil-war onset | Supervised machine-learning prediction of conflict |
| Sursa seminală≠ | Fearon, J. D., & Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. American Political Science Review, 97(1), 75–90. DOI ↗ | 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. DOI ↗ |
| Denumiri alternative | 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 |
| Înrudite | 3 | 3 |
| Rezumat≠ | 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. |
| ScholarGateSet de date ↗ |
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