Using Monte Carlo simulations to translate military and law enforcement training results to operational metrics

Abstract

There are numerous challenges comparing research initiatives due to methodological differences and scenario-specific problems. Military and law enforcement issues present an extreme variant of this challenge. Specifically, assessment and training scenarios strive for realism, but operators cannot engage one another with live rounds or induce the full spectrum of environmental stressors for obvious safety reasons. Instead, particular factors are evaluated in a given scenario via experimental statistics despite the inherent difficulty in communicating inferential statistics to the intended audience of military and law enforcement professionals. The current investigation explores how Monte Carlo simulations can use probabilistic distribution sampling to convert statistical inferences into concrete operational outcomes. Using this type of distribution sampling, statistical inferences can be translated into operational metrics such as the probability of winning a gunfight. Describing these statistical values and effect sizes in terms of survival provides a more appreciable operational metric that military and law enforcement personnel can use when evaluating the advantages of various training platforms or equipment. Several approaches are examined that each accomplish this general goal, including circumstances outside of marksmanship and lethal force decision-making.

Digital twin: Data exploration, architecture, implementation and future

“…Data analysis approaches encompass many methods to analyze and simulate aircraft performance, stress analysis, and the effectiveness of the weapon system. These techniques often entail the utilization of computational fluid dynamics (CFD) simulations [151] , finite element analysis (FEA) [152] , and Monte Carlo simulations [153] , [154] . The utilization of data analytics and machine learning techniques is prevalent in the domain of cybersecurity threat analysis.…”

Small arms combat modeling: a superior way to evaluate marksmanship data

“…Currently, the Monte Carlo technique enables converting raw human performance metrics into a small arms lethality simulation that is both simple to understand and easy to calculate (Biggs and Hirsch, 2021). This approach uses marksmanship data collected from two shooters to determine which shooter is more lethal.…”

“…Simple Monte Carlo applications can model warfighter lethality by comparing the performance of two different shooters (cf. Biggs and Hirsch, 2021). However, the technique proposed is too simple to adequately measure lethality.…”

A primer on using Monte Carlo simulations to evaluate marksmanship

“…Warfighter performance can be simulated as a single shot from a head-to-head outcome—one shooter fires upon another shooter and the engagement ends. At close proximity, where accuracy is high, speed is likely to be the determining factor and many engagements will have a victor in only few shots (Biggs and Hirsch, 2022). At greater distances, where accuracy is lower, a single shot simulation will produce a high number of non-lethal draws, or indeterminate outcomes.…”

“…Here the intent is to use performance metrics to simulate a combat engagement (cf. Biggs and Hirsch, 2022).…”