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Purpose: This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

Design/methodology/approach: This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

Findings: This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

Originality/value: This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.


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Source Publication

Journal of Defense Analytics and Logistics