IDEAS home Printed from https://ideas.repec.org/a/sae/joudef/v21y2024i2p205-227.html
   My bibliography  Save this article

Verification, validation, and accreditation for models and simulations in the Australian defence context: a review

Author

Listed:
  • Kerryn R Owen
  • Ripon K Chakrabortty

Abstract

Building simulation models that are valid and credible is an enduring challenge in the Australian Defence Organisation (ADO) context. Establishing validity and credibility can be achieved through the rigorous use of appropriate Verification, Validation, and Accreditation (VVA) processes. Such processes are well-known in modeling and simulation (M&S) practice. However, these are generally not applied within the ADO, typically due to resourcing concerns and a lack of authoritative guidance. Even if there are any, due to security concerns and commercial reasons, the application of M&S within ADO is generally not published in open-access platforms. Depending on where in the M&S life-cycle VVA is started, it may also serve a secondary aim of risk reduction, assisting in the early discovery of possible problems or mistakes. This research reviews current VVA practices from academic literature and recommends processes that are appropriate for application to combat simulation tools within the ADO context. A scoping review has been conducted to gather insight into current VVA practice in the M&S community. The results of this review are presented in the form of charting relevant characteristics from selected references. The scoping review shows that executable validation of simulation results against referent data sourced from physical experiments is the most prevalent form of VVA, with referent data from comparative models being a prevalent alternative. Furthermore, there is evident reliance on graphical comparison of data; this could be enhanced with objective data comparators, such as aggregate error measures or statistical techniques. Finally, there is an evident gap in VVA references from Australia, which could be addressed through the propagation and reporting of prevalent VVA practices within the ADO context.

Suggested Citation

  • Kerryn R Owen & Ripon K Chakrabortty, 2024. "Verification, validation, and accreditation for models and simulations in the Australian defence context: a review," The Journal of Defense Modeling and Simulation, , vol. 21(2), pages 205-227, April.
  • Handle: RePEc:sae:joudef:v:21:y:2024:i:2:p:205-227
    DOI: 10.1177/15485129221134632
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/15485129221134632
    Download Restriction: no

    File URL: https://libkey.io/10.1177/15485129221134632?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. Kleijnen, Jack P. C., 1995. "Verification and validation of simulation models," European Journal of Operational Research, Elsevier, vol. 82(1), pages 145-162, April.
    2. Paul Glover & Paul Pearce, 2020. "Rapid assessment and review of simulation modelling," Journal of Simulation, Taylor & Francis Journals, vol. 14(2), pages 145-155, April.
    3. Nageler, P. & Zahrer, G. & Heimrath, R. & Mach, T. & Mauthner, F. & Leusbrock, I. & Schranzhofer, H. & Hochenauer, C., 2017. "Novel validated method for GIS based automated dynamic urban building energy simulations," Energy, Elsevier, vol. 139(C), pages 142-154.
    4. David Oakley & Bhakti Stephan Onggo & Dave Worthington, 2020. "Symbiotic simulation for the operational management of inpatient beds: model development and validation using Δ-method," Health Care Management Science, Springer, vol. 23(1), pages 153-169, March.
    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. De Jaeger, Ina & Reynders, Glenn & Ma, Yixiao & Saelens, Dirk, 2018. "Impact of building geometry description within district energy simulations," Energy, Elsevier, vol. 158(C), pages 1060-1069.
    2. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    3. Pau Fonseca i Casas, 2023. "A Continuous Process for Validation, Verification, and Accreditation of Simulation Models," Mathematics, MDPI, vol. 11(4), pages 1-25, February.
    4. Johan Simonsson & Khalid Tourkey Atta & Gerald Schweiger & Wolfgang Birk, 2021. "Experiences from City-Scale Simulation of Thermal Grids," Resources, MDPI, vol. 10(2), pages 1-20, January.
    5. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.
    6. Edris Yousefi Rad & Mohammad Reza Mahpeykar, 2017. "A Novel Hybrid Approach for Numerical Modeling of the Nucleating Flow in Laval Nozzle and Transonic Steam Turbine Blades," Energies, MDPI, vol. 10(9), pages 1-37, August.
    7. Tunali, S. & Batmaz, I., 2003. "A metamodeling methodology involving both qualitative and quantitative input factors," European Journal of Operational Research, Elsevier, vol. 150(2), pages 437-450, October.
    8. Patrick Afflerbach & Christopher Dun & Henner Gimpel & Dominik Parak & Johannes Seyfried, 2021. "A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 329-348, August.
    9. Diaz, Rafael & Behr, Joshua G. & Acero, Beatriz, 2022. "Coastal housing recovery in a postdisaster environment: A supply chain perspective," International Journal of Production Economics, Elsevier, vol. 247(C).
    10. Kleijnen, J.P.C., 1995. "Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments," Other publications TiSEM 87ee6ee0-592c-4204-ac50-6, Tilburg University, School of Economics and Management.
    11. Janová, Jitka & Hampel, David & Nerudová, Danuše, 2019. "Design and validation of a tax sustainability index," European Journal of Operational Research, Elsevier, vol. 278(3), pages 916-926.
    12. Sander van der Hoog, 2017. "Deep Learning in (and of) Agent-Based Models: A Prospectus," Papers 1706.06302, arXiv.org.
    13. Aumann, Craig A., 2007. "A methodology for developing simulation models of complex systems," Ecological Modelling, Elsevier, vol. 202(3), pages 385-396.
    14. Giuliano Rancilio & Alexandre Lucas & Evangelos Kotsakis & Gianluca Fulli & Marco Merlo & Maurizio Delfanti & Marcelo Masera, 2019. "Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis," Energies, MDPI, vol. 12(17), pages 1-26, August.
    15. Zhang Deng & Yixing Chen & Xiao Pan & Zhiwen Peng & Jingjing Yang, 2021. "Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling," Energies, MDPI, vol. 14(4), pages 1-17, February.
    16. Nageler, P. & Schweiger, G. & Schranzhofer, H. & Mach, T. & Heimrath, R. & Hochenauer, C., 2018. "Novel method to simulate large-scale thermal city models," Energy, Elsevier, vol. 157(C), pages 633-646.
    17. MacPherson, Brian & Gras, Robin, 2016. "Individual-based ecological models: Adjunctive tools or experimental systems?," Ecological Modelling, Elsevier, vol. 323(C), pages 106-114.
    18. Ao, Dan & Hu, Zhen & Mahadevan, Sankaran, 2017. "Design of validation experiments for life prediction models," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 22-33.
    19. Labaka, Leire & Hernantes, Josune & Sarriegi, Jose M., 2015. "Resilience framework for critical infrastructures: An empirical study in a nuclear plant," Reliability Engineering and System Safety, Elsevier, vol. 141(C), pages 92-105.
    20. Kleijnen, Jack P. C. & Sargent, Robert G., 2000. "A methodology for fitting and validating metamodels in simulation," European Journal of Operational Research, Elsevier, vol. 120(1), pages 14-29, January.

    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:21:y:2024:i:2:p:205-227. 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.