Finding the fuel of the Arab Spring fire: a historical data analysis. Issue 2 (30th October 2018)

• Record Type: Journal Article
• Authors: Ahner, Darryl; Brantley, Luke
• Is Part Of: Journal of defense analytics and logistics. Volume 2:Issue 2(2018)
• Page Start: 58
• Page End: 68
• Publication Date: 2018-10-30
• Subjects: Data analytics -- Logistic regression -- Conflict predicition -- data imputation; Military art and science -- Periodicals; Military research -- Periodicals; Defense industries -- Periodicals; Logistics -- Periodicals; 355.4
• DOI: 10.1108/JDAL-03-2018-0008
• Languages: English
• ISSNs: 2399-6439

Abstract

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.