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Minimization of Attack Risk with Bayesian Detection CriteriaTopics: Monte Carlo, Black Scholes, Bayesian, ARIMA, Heston and Stochastic Techniques or Analysis; Risk Analysis and Management; Risk Minimization, Analytics and Deep Machine Learning

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.(More)

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.

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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 ISSN 2184-5034, pages 17-26. DOI: 10.5220/0007656100170026

@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}, issn={2184-5034}, }

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 IS - 2184-5034 AU - Standley, V. AU - Nuño, F. AU - Sharpe, J. PY - 2019 SP - 17 EP - 26 DO - 10.5220/0007656100170026