Date of Award

9-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Operational Sciences

First Advisor

Darryl K. Ahner, PhD

Abstract

Many disparate datasets exist that provide country attributes covering political, economic, and social aspects. Unfortunately, this data often does not include all countries nor is the data complete for those countries included, as measured by the dataset’s missingness. This research addresses these dataset shortfalls in predicting country instability by considering country attributes in all aspects as well as in greater thresholds of missingness. First, a structured summary of past research is presented framed by a developed casual taxonomy and functional ontology. Additionally, a novel imputation technique for very large datasets is presented to account for moderate missingness in the expanded dataset. This method is further extended to establish the MASS-impute algorithm, a multicollinearity applied stepwise stochastic imputation method that overcomes numerical problems present in preferred commercial packages. Finally, the imputed datasets with 932 variables are used to develop a hierarchical clustering approach that accounts for geographic and cultural influences that are desired in the practical use of modeling country conflict. These additional insights and tools provide a basis for improving future country conflict and peace research.

AFIT Designator

AFIT-ENS-DS-22-S-065

DTIC Accession Number

AD1181271

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