An Imputation Approach to Developing Alternative Futures of Country Conflict

Abstract

Understanding what causes countries to be in a state of violent conflict is of vital importance to developing realistic national strategies on both a regional and global scale. Given these causes, it is important to understand the effects of missing data, how to impute that data, and the interrelation between data elements. Utilizing both open source data and previously generated equations that predict a country's likelihood to transition conflict statuses, this research projects data into the future and predicts each nations' subsequent conflict statuses. Future data is populated using a novel approach inspired by stochastic regression imputation. The replicated future data and predictions were interpreted as alternative futures of regional conflict in both the Arab world and Southeast Asia. The conflict occurrences in the Arab world region were projected to trend upward compared to the region's historic behavior. In Southeast Asia, the next ten years forecasted a decline in total violent conflicts. Regional scenarios where the elements of national power influenced a data element were implemented to learn how alternative futures might be.

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Document Details

Document Type
Technical Report
Publication Date
Mar 21, 2019
Accession Number
AD1077401

Entities

People

  • Zachary J Kane

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Neural Networks
  • Asia
  • Bayesian Networks
  • Computational Science
  • Data Mining
  • Data Science
  • Databases
  • Delphi Method
  • Department Of Defense
  • Digital Data
  • Equations
  • Geography
  • Governments
  • Information Operations
  • Information Processing
  • Information Science
  • Knowledge Management
  • Military Operations
  • Monte Carlo Method
  • Operations Research
  • Political Science
  • Predictive Modeling
  • Probability
  • Random Variables
  • Social Sciences
  • Southeast Asia
  • Statistical Algorithms
  • United States Government

Readers

  • Computational Modeling and Simulation
  • Strategic Security Studies
  • Theoretical Analysis.