A conceptual model of combat marksmanship

Purpose Marksmanship tests are often determined by inherited standards or individual subject-matter experts rather than via a consistent and repeatable framework based on firearms engagements. Better process development in evaluations and better outcome modeling will optimize performance among military and law enforcement, thereby producing greater safety and effectiveness in field performance. Design/methodology/approach Simulation experiments depicted the operational advantage provided by different night vision goggles using either a cognitive experimental approach, a simple Monte Carlo simulation or multiple Monte Carlo simulations integrated into a computation modeling approach. Findings Whereas the cognitive experimental approach could only quantify a combat advantage in terms of reaction times, computational modeling provided a more practical interpretation while fully accounting for data granularity. The combat advantage could be described as a 40% increased likelihood to win in combat rather than a one second advantage. Originality/value We provide a process-oriented conceptual approach to identify the sequence involved during firearms use. Eight common behavioral and decision nodes are situated with feedback loops that return, where applicable, to prior steps when addressing any errors in the marksmanship process. With this information, military or law enforcement personnel can structure their dialogue around the most important tasks to incorporate when creating a marksmanship evaluation or training program.

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Institutions

Publication Details

Journal
Journal of Defense Analytics and Logistics
Published
2026-06-12
DOI
https://doi.org/10.1108/jdal-12-2024-0026
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

A conceptual model of combat marksmanship

Joseph A. Hamilton, Joel Suss, Adam T. Biggs, Ken Javes et al.
Journal of Defense Analytics and Logistics
Human-Automation Interaction and Safety
article

A conceptual model of combat marksmanship

Joseph A. Hamilton, Joel Suss, Adam T. Biggs, Ken Javes, Rachel R. Markwald, Anthony Viggiani, Nathan Carnes, John Costa, Greg Huffman
article en

Abstract

Purpose Marksmanship tests are often determined by inherited standards or individual subject-matter experts rather than via a consistent and repeatable framework based on firearms engagements. Better process development in evaluations and better outcome modeling will optimize performance among military and law enforcement, thereby producing greater safety and effectiveness in field performance. Design/methodology/approach Simulation experiments depicted the operational advantage provided by different night vision goggles using either a cognitive experimental approach, a simple Monte Carlo simulation or multiple Monte Carlo simulations integrated into a computation modeling approach. Findings Whereas the cognitive experimental approach could only quantify a combat advantage in terms of reaction times, computational modeling provided a more practical interpretation while fully accounting for data granularity. The combat advantage could be described as a 40% increased likelihood to win in combat rather than a one second advantage. Originality/value We provide a process-oriented conceptual approach to identify the sequence involved during firearms use. Eight common behavioral and decision nodes are situated with feedback loops that return, where applicable, to prior steps when addressing any errors in the marksmanship process. With this information, military or law enforcement personnel can structure their dialogue around the most important tasks to incorporate when creating a marksmanship evaluation or training program.

Journal of Defense Analytics and Logistics
United States Army Medical Command (US), Unifor (NO), Uniformed Services University of the Health Sciences (US), Air Education and Training Command (US), Naval Air Warfare Center Weapons Division (US), Naval Health Research Center (US)
Office of Naval Research
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Human-Automation Interaction and Safety
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