Benjamin Leiby and Darryl Ahner
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.
Abstract
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.
Details
Keywords
Sarah Neumann, Darryl Ahner and Raymond R. Hill
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…
Abstract
Purpose
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/approach
In 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.
Findings
In 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/implications
The study is based on actual historical data and is purely data driven.
Practical implications
The study demonstrates the utility of the analytical methodology but carries not implementation recommendations.
Originality/value
This 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.
Details
Keywords
Darryl Ahner and Luke Brantley
This paper aims to address the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015. During this time, higher rates of…
Abstract
Purpose
This paper aims to address the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015. During this time, higher rates of conflict transition occurred than normally observed in previous studies for certain Middle Eastern and North African countries.
Design/methodology/approach
Previous prediction models decrease in accuracy during times of volatile conflict transition. Also, proper strategies for handling the Arab Spring have been highly debated. This paper identifies which countries were affected by the Arab Spring and then applies data analysis techniques to predict a country’s tendency to suffer from high-intensity, violent conflict. A large number of open-source variables are incorporated by implementing an imputation methodology useful to conflict prediction studies in the future. The imputed variables are implemented in four model building techniques: purposeful selection of covariates, logical selection of covariates, principal component regression and representative principal component regression resulting in modeling accuracies exceeding 90 per cent.
Findings
Analysis of the models produced by the four techniques supports hypotheses which propose political opportunity and quality of life factors as causations for increased instability following the Arab Spring.
Originality/value
Of particular note is that the paper addresses the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015 through data analytics. This paper considers various open-source, readily available data for inclusion in multiple models of identified Arab Spring nations in addition to implementing a novel imputation methodology useful to conflict prediction studies in the future.