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Forecasting country conflict using statistical learning methods

Forecasting country conflict using statistical learning methods This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict.Design/methodology/approachIn this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction.FindingsIn this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict.Research limitations/implicationsThe study is based on actual historical data and is purely data driven.Practical implicationsThe study demonstrates the utility of the analytical methodology but carries not implementation recommendations.Originality/valueThis is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Defense Analytics and Logistics Emerald Publishing

Forecasting country conflict using statistical learning methods

Forecasting country conflict using statistical learning methods

Journal of Defense Analytics and Logistics , Volume 6 (1): 14 – Jun 22, 2022

Abstract

This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict.Design/methodology/approachIn this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction.FindingsIn this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict.Research limitations/implicationsThe study is based on actual historical data and is purely data driven.Practical implicationsThe study demonstrates the utility of the analytical methodology but carries not implementation recommendations.Originality/valueThis is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions.

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References (8)

Publisher
Emerald Publishing
Copyright
© In accordance with section 105 of the US Copyright Act, this work has been produced by a US government employee and shall be considered a public domain work, as copyright protection is not available
ISSN
2399-6439
DOI
10.1108/jdal-10-2021-0014
Publisher site
See Article on Publisher Site

Abstract

This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict.Design/methodology/approachIn this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction.FindingsIn this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict.Research limitations/implicationsThe study is based on actual historical data and is purely data driven.Practical implicationsThe study demonstrates the utility of the analytical methodology but carries not implementation recommendations.Originality/valueThis is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions.

Journal

Journal of Defense Analytics and LogisticsEmerald Publishing

Published: Jun 22, 2022

Keywords: Conflict; Cluster analysis; Forecasting; Combatant commands

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