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General Information
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2020 Vol.10(1): 8-12 ISSN: 2010-3697
DOI: 10.7763/IJMO.2020.V10.739

Modeling Interstate War Combat Deaths

Vaughn H. Standley, Frank G. Nuño, and Jacob W. Sharpe
Abstract—A prolonged campaign of peaceful interstate competition is an ideal strategic application of artificial intelligence. Monte Carlo simulation, based on validated war analytics, must be at the heart of this capability. Otherwise the system will not know how to assess the potential consequences of failed solutions, chief among them combat fatalities resulting from interstate war. Although the power law has been used since 1960 to model the statistical distribution of deaths resulting from violent conflict, it is not a valid candidate for use in Monte Carlo simulation because it is mathematically divergent for the case of interstate war. Probing Correlates of War Project data, investigators found that combat fatalities in interstate war follow log-gamma or log-normal distributions, depending on whether a state is attacking or defending. Both distributions are valid for use in Monte Carlo simulations. Moreover, they are strong quantitative evidence that war should be modeled as a zero-sum, non-cooperative, high-risk game.

Index Terms—Combat deaths, interstate war, log-gamma, log-normal, monte carlo, power-law.

V. H. Standley, F. G. Nuno, J. W. Sharpe are with College of Information and Cyberspace, National Defense University, 300 5th Ave., Fort Leslie McNair, Washington D.C., 20319 (e-mail: vaughn.stanldey.civ@ndu.edu, frank.g.nuno.mil@ndu.edu, jacob.w.sharpe.civ@ndu.edu)

[PDF]

Cite: Vaughn H. Standley, Frank G. Nuño, and Jacob W. Sharpe, "Modeling Interstate War Combat Deaths," International Journal of Modeling and Optimization vol. 10, no. 1, pp. 8-12, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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