Minimization of Attack Risk with Bayesian Detection Criteria

Vaughn Standley, Frank Nuño, Jacob Sharpe

2019

Abstract

Strategic deterrence operates in and on a vast interstate network of rational actors seeking to minimize risk. Risk can be minimized by employing a likelihood ratio test (LRT) derived from Bayes’ Theorem. The LRT is comprised of prior, detection, and false-alarm probabilities. The power-law, known for its applicability to complex systems, has been used to model the distribution of combat fatalities. However, it cannot be used as a Bayesian prior for war when its area is unbounded. Analytics applied to Correlates of War data reveals that combat fatalities follow a log-gamma or log-normal probability distribution depending on a state’s escalation strategy. Results are used to show that nuclear war level fatalities pose increasing risk despite decreasing probability, that LRT-based decisions can minimize attack risk if an upper limit of impending fatalities is indicated by the detection system and commensurate with nominal false-alarm maximum, and that only successful defensive strategies are stable.

Paper Citation

in Harvard Style

Standley V., Nuño F. and Sharpe J. (2019). Minimization of Attack Risk with Bayesian Detection Criteria.In Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS, ISBN 978-989-758-366-7, pages 17-26. DOI: 10.5220/0007656100170026

in Bibtex Style

@conference{complexis19,
author={Vaughn Standley and Frank Nuño and Jacob Sharpe},
title={Minimization of Attack Risk with Bayesian Detection Criteria},
booktitle={Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,},
year={2019},
pages={17-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007656100170026},
isbn={978-989-758-366-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,
TI - Minimization of Attack Risk with Bayesian Detection Criteria
SN - 978-989-758-366-7
AU - Standley V.
AU - Nuño F.
AU - Sharpe J.
PY - 2019
SP - 17
EP - 26
DO - 10.5220/0007656100170026