Effectiveness Calculation of Multiple Rounds Simultaneous Impact Shooting Method Based on Monte Carlo Method

Hu, Xiaoyi; Wang, Hang Yu

doi:10.4028/www.scientific.net/amm.397-400.2459

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…Monte Carlo simulations represent another method for simulating a combat engagement, although the technique is underutilized. Common applications involve the effectiveness of weapon strikes (Chusilp et al, 2014;Hu and Wang, 2013) or even marksmanship with small arms (Mihaylov, 2017). Some applications emerged during Vietnam War-era modeling to describe both small unit, tactical-level activities as well as the larger unit and operationallevel activities (Adams et al, 1961;Bonder, 2002;De Laquil, 1980;Monahan and DuBois, 1979).…”

Section: Monte Carlo Simulations and Warfighter Lethalitymentioning

confidence: 99%

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

Biggs,

Huffman,

Hamilton

et al. 2023

JDAL

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PurposeMarksmanship data is a staple of military and law enforcement evaluations. This ubiquitous nature creates a critical need to use all relevant information and to convey outcomes in a meaningful way for the end users. The purpose of this study is to demonstrate how simple simulation techniques can improve interpretations of marksmanship data.Design/methodology/approachThis study uses three simulations to demonstrate the advantages of small arms combat modeling, including (1) the benefits of incorporating a Markov Chain into Monte Carlo shooting simulations; (2) how small arms combat modeling is superior to point-based evaluations; and (3) why continuous-time chains better capture performance than discrete-time chains.FindingsThe proposed method reduces ambiguity in low-accuracy scenarios while also incorporating a more holistic view of performance as outcomes simultaneously incorporate speed and accuracy rather than holding one constant.Practical implicationsThis process determines the probability of winning an engagement against a given opponent while circumventing arbitrary discussions of speed and accuracy trade-offs. Someone wins 70% of combat engagements against a given opponent rather than scoring 15 more points. Moreover, risk exposure is quantified by determining the likely casualties suffered to achieve victory. This combination makes the practical consequences of human performance differences tangible to the end users. Taken together, this approach advances the operations research analyses of squad-level combat engagements.Originality/valueFor more than a century, marksmanship evaluations have used point-based systems to classify shooters. However, these scoring methods were developed for competitive integrity rather than lethality as points do not adequately capture combat capabilities. The proposed method thus represents a major shift in the marksmanship scoring paradigm.

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Section: Monte Carlo Simulations and Warfighter Lethalitymentioning

confidence: 99%

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

Biggs,

Huffman,

Hamilton

et al. 2023

JDAL

View full text Add to dashboard Cite

show abstract

“…Monte Carlo simulation, when used to estimate performance, presumes that we can transform basic military performance metrics into a simulation of warfighter performance (see Table 1). Typical military uses examine whether weapon strikes effectively damage or disable a target (Chusilp et al ., 2014; Hu and Wang, 2013). Here the intent is to use performance metrics to simulate a combat engagement (cf.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

“…Monte Carlo simulations are one alternative to bridge the gap between human performance observations and actionable applications to marksmanship training doctrine. Monte Carlo simulations have been suggested to explore the effectiveness of weapon strikes (Chusilp et al ., 2014; Hu and Wang, 2013), including weapons applications for platforms to include small arms combat (Mihaylov, 2017). In this sense, the technique utilizes known distribution patterns in accuracy to evaluate the operational effectiveness of different weapons systems.…”

Section: Introductionmentioning

confidence: 99%

A primer on using Monte Carlo simulations to evaluate marksmanship

Biggs,

Hamilton

2023

JDAL

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PurposeEvaluating warfighter lethality is a critical aspect of military performance. Raw metrics such as marksmanship speed and accuracy can provide some insight, yet interpreting subtle differences can be challenging. For example, is a speed difference of 300 milliseconds more important than a 10% accuracy difference on the same drill? Marksmanship evaluations must have objective methods to differentiate between critical factors while maintaining a holistic view of human performance.Design/methodology/approachMonte Carlo simulations are one method to circumvent speed/accuracy trade-offs within marksmanship evaluations. They can accommodate both speed and accuracy implications simultaneously without needing to hold one constant for the sake of the other. Moreover, Monte Carlo simulations can incorporate variability as a key element of performance. This approach thus allows analysts to determine consistency of performance expectations when projecting future outcomes.FindingsThe review divides outcomes into both theoretical overview and practical implication sections. Each aspect of the Monte Carlo simulation can be addressed separately, reviewed and then incorporated as a potential component of small arms combat modeling. This application allows for new human performance practitioners to more quickly adopt the method for different applications.Originality/valuePerformance implications are often presented as inferential statistics. By using the Monte Carlo simulations, practitioners can present outcomes in terms of lethality. This method should help convey the impact of any marksmanship evaluation to senior leadership better than current inferential statistics, such as effect size measures.

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“…Monte Carlo simulation has been used to investigate a wide variety of topics, including basketball shooting, 39 the likelihood of a large caliber weapons system to impact a moving target with multiple shots, 40 the probability of target damage as a part of weapon effectiveness analyses, 41 and even likely shot dispersions in a cone of fire for a particular weapon. 42 The latter examples explore damage inflicted by weapons and will sometimes utilize exact solutions rather than Monte Carlo simulations because exact solutions incorporate more precise physical variance into a problem rather than more probabilistic dimensions.…”

Section: Introductionmentioning

confidence: 99%

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

Biggs¹,

Hirsch

2021

Journal of Defense Modeling & Simulation

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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.

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Effectiveness Calculation of Multiple Rounds Simultaneous Impact Shooting Method Based on Monte Carlo Method

Cited by 5 publications

References 1 publication

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

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

A primer on using Monte Carlo simulations to evaluate marksmanship

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

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