IDEAS home Printed from https://ideas.repec.org/a/sae/joudef/v22y2025i2p207-214.html

Challenges of incorporating wounded personnel into small arms combat simulations

Author

Listed:
  • Adam T Biggs
  • Joseph A Hamilton
  • Rachel R Markwald

Abstract

Small arms combat modeling is one method to describe raw human performance data in terms of lethality. This process uses a series of Monte Carlo simulations based on observed data to convert measurements of speed and accuracy into a quantifiable chance of winning a combat engagement. A major issue within these modeling efforts involves the assumptions of incorporating wounded personnel. Realistic combat will have scenarios where shots fired strike adversaries without killing them, and therefore, this element cannot be ignored. However, there are at least four significant assumptions made during the modeling and simulation effort when incorporating wounded personnel: (1) assigning damage inflicted by shots, (2) tracking wounded personnel, (3) reducing combat effectiveness of wounded personnel, and (4) burdening other fighters in the simulation. Here, we outline the challenges posed by each assumption and discuss possible solutions. Whatever the final decision for a particular modeling effort, the assumptions made should always be clearly documented in the “Methods†section. Wounded personnel will likely require several such assumptions be made that could affect the outcome; nevertheless, wounded personnel should be represented in some capacity in any small arms combat modeling effort.

Suggested Citation

  • Adam T Biggs & Joseph A Hamilton & Rachel R Markwald, 2025. "Challenges of incorporating wounded personnel into small arms combat simulations," The Journal of Defense Modeling and Simulation, , vol. 22(2), pages 207-214, April.
  • Handle: RePEc:sae:joudef:v:22:y:2025:i:2:p:207-214
    DOI: 10.1177/15485129231203704
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/15485129231203704
    Download Restriction: no

    File URL: https://libkey.io/10.1177/15485129231203704?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kress, Moshe & Caulkins, Jonathan P. & Feichtinger, Gustav & Grass, Dieter & Seidl, Andrea, 2018. "Lanchester model for three-way combat," European Journal of Operational Research, Elsevier, vol. 264(1), pages 46-54.
    2. Manh D Hy & My A Vu & Nam H Nguyen & Anh N Ta & Dinh V Bui, 2020. "Optimization in an asymmetric Lanchester (n, 1) model," The Journal of Defense Modeling and Simulation, , vol. 17(1), pages 117-122, January.
    3. W. K. Hastings, 1970. "Monte Carlo sampling methods using Markov chains and their applications," Biometrika, Biometrika Trust, vol. 57(1), pages 97-109.
    4. M P Atkinson & A Gutfraind & M Kress, 2012. "When do armed revolts succeed: lessons from Lanchester theory," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(10), pages 1363-1373, October.
    5. Moshe Kress & Roberto Szechtman, 2009. "Why Defeating Insurgencies Is Hard: The Effect of Intelligence in Counterinsurgency Operations---A Best-Case Scenario," Operations Research, INFORMS, vol. 57(3), pages 578-585, June.
    6. S. J. Deitchman, 1962. "A Lanchester Model of Guerrilla Warfare," Operations Research, INFORMS, vol. 10(6), pages 818-827, December.
    7. Seth Bonder, 2002. "Army Operations Research---Historical Perspectives and Lessons Learned," Operations Research, INFORMS, vol. 50(1), pages 25-34, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adam T Biggs & Joseph A Hamilton & Rachel R Markwald, 2025. "Identifying appropriate scenario termination rules for squad-level simulations of warfighter lethality," The Journal of Defense Modeling and Simulation, , vol. 22(4), pages 521-528, October.
    2. Nam H Nguyen & My A Vu & Anh N Ta & Dinh V Bui & Manh D Hy, 2022. "Optimizing fire allocation in a Network Centric Warfare-type model," The Journal of Defense Modeling and Simulation, , vol. 19(4), pages 691-701, October.
    3. Timothy A. McLennan-Smith & Alexander C. Kalloniatis & Zlatko Jovanoski & Harvinder S. Sidhu & Dale O. Roberts & Simon Watt & Isaac N. Towers, 2021. "A Mathematical Model of Humanitarian Aid Agencies in Attritional Conflict Environments," Operations Research, INFORMS, vol. 69(6), pages 1696-1714, November.
    4. Moshe Kress, 2020. "Lanchester Models for Irregular Warfare," Mathematics, MDPI, vol. 8(5), pages 1-14, May.
    5. Gerardo Minguela-Castro & Ruben Heradio & Carlos Cerrada, 2021. "Automated Support for Battle Operational–Strategic Decision-Making," Mathematics, MDPI, vol. 9(13), pages 1-15, June.
    6. Kolebaje, Olusola & Popoola, Oyebola & Khan, Muhammad Altaf & Oyewande, Oluwole, 2020. "An epidemiological approach to insurgent population modeling with the Atangana–Baleanu fractional derivative," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    7. Vikram Mittal, 2026. "Estimating attrition coefficients for the Lanchester equations from small-unit combat models," The Journal of Defense Modeling and Simulation, , vol. 23(2), pages 193-205, April.
    8. Manh D Hy & My A Vu & Nam H Nguyen & Anh N Ta & Dinh V Bui, 2020. "Optimization in an asymmetric Lanchester (n, 1) model," The Journal of Defense Modeling and Simulation, , vol. 17(1), pages 117-122, January.
    9. Edward H. Kaplan & Moshe Kress & Roberto Szechtman, 2010. "Confronting Entrenched Insurgents," Operations Research, INFORMS, vol. 58(2), pages 329-341, April.
    10. Sergey Salishev, 2025. "The Narrow Corridor of Stable Solutions in an Extended Osipov--Lanchester Model with Constant Total Population," Papers 2512.18515, arXiv.org.
    11. Anelí Bongers & José L. Torres, 2021. "A bottleneck combat model: an application to the Battle of Thermopylae," Operational Research, Springer, vol. 21(4), pages 2859-2877, December.
    12. Kress, Moshe & Caulkins, Jonathan P. & Feichtinger, Gustav & Grass, Dieter & Seidl, Andrea, 2018. "Lanchester model for three-way combat," European Journal of Operational Research, Elsevier, vol. 264(1), pages 46-54.
    13. Li, Meixuan Jade & Zhu, Cheng & Zhu, Xianqiang & Tse, Chi K., 2026. "Competitiveness of competing complex systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 682(C).
    14. Lu Yao & Taotao Cheng & Jiao Luo & Xintian Liu, 2026. "Weibull distribution-based reliability evaluation of cutting tool via improved Bayesian-Bootstrap method," Journal of Risk and Reliability, , vol. 240(1), pages 185-199, February.
    15. Mitra Kharabati & Morteza Amini & Mohammad Arashi, 2026. "Variational inference for sparse poisson regression," Computational Statistics, Springer, vol. 41(3), pages 1-50, April.
    16. Akanksha Kumari & Vikas Kumar Sharma, 2025. "Bayes estimation of defective proportion for single shot device testing data with information on masking and manufacturing defects," Journal of Risk and Reliability, , vol. 239(5), pages 1041-1060, October.
    17. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    18. Franco Bagnoli & Tommaso Matteuzzi, 2025. "Metastability in the diluted parallel Ising model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 98(10), pages 1-10, October.
    19. Yuanying Zhao & Xingde Duan, 2022. "Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    20. Kjell Hausken & Jun Zhuang, 2011. "Governments' and Terrorists' Defense and Attack in a T -Period Game," Decision Analysis, INFORMS, vol. 8(1), pages 46-70, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:joudef:v:22:y:2025:i:2:p:207-214. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.