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Conceptual modelling for simulation Part I: definition and requirements

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  • S Robinson

    (University of Warwick)

Abstract

Conceptual modelling is probably the most important aspect of a simulation study. It is also the most difficult and least understood. Over 40 years of simulation research and practice have provided only limited information on how to go about designing a simulation conceptual model. This paper, the first of two, discusses the meaning of conceptual modelling and the requirements of a conceptual model. Founded on existing literature, a definition of a conceptual model is provided. Four requirements of a conceptual model are described: validity, credibility, utility and feasibility. The need to develop the simplest model possible is also discussed. Owing to a paucity of advice on how to design a conceptual model, the need for a conceptual modelling framework is proposed. Built on the foundations laid in this paper, a conceptual modelling framework is described in the paper that follows.

Suggested Citation

  • S Robinson, 2008. "Conceptual modelling for simulation Part I: definition and requirements," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(3), pages 278-290, March.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:3:d:10.1057_palgrave.jors.2602368
    DOI: 10.1057/palgrave.jors.2602368
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    References listed on IDEAS

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    Cited by:

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    3. Hana M Dobrovolny & Micaela B Reddy & Mohamed A Kamal & Craig R Rayner & Catherine A A Beauchemin, 2013. "Assessing Mathematical Models of Influenza Infections Using Features of the Immune Response," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-20, February.
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    11. Brailsford, Sally C. & Eldabi, Tillal & Kunc, Martin & Mustafee, Navonil & Osorio, Andres F., 2019. "Hybrid simulation modelling in operational research: A state-of-the-art review," European Journal of Operational Research, Elsevier, vol. 278(3), pages 721-737.
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    14. Alireza Moumivand & Adel Azar & Abbas Toloie Eshlaghy, 2022. "Combined soft system methodology and agent‐based simulation for multi‐methodological modelling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(2), pages 200-217, March.
    15. Priscilla Avegliano & Jaime Simão Sichman, 2023. "Equation-Based Versus Agent-Based Models: Why Not Embrace Both for an Efficient Parameter Calibration?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(4), pages 1-3.
    16. D Champion & J M Wilson, 2010. "The impact of contingency factors on validation of problem structuring methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(9), pages 1420-1431, September.
    17. J H Powell & N Mustafee, 2017. "Widening requirements capture with soft methods: an investigation of hybrid M&S studies in health care," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1211-1222, October.
    18. A A Tako & S Robinson, 2009. "Comparing discrete-event simulation and system dynamics: users' perceptions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 296-312, March.
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    20. Gogi, Anastasia & Tako, Antuela A. & Robinson, Stewart, 2016. "An experimental investigation into the role of simulation models in generating insights," European Journal of Operational Research, Elsevier, vol. 249(3), pages 931-944.
    21. Martin Comis & Catherine Cleophas & Christina Büsing, 2021. "Patients, primary care, and policy: Agent-based simulation modeling for health care decision support," Health Care Management Science, Springer, vol. 24(4), pages 799-826, December.
    22. Tako, Antuela A. & Kotiadis, Kathy, 2015. "PartiSim: A multi-methodology framework to support facilitated simulation modelling in healthcare," European Journal of Operational Research, Elsevier, vol. 244(2), pages 555-564.
    23. Elias Hartvigsson & Erik Oscar Ahlgren & Sverker Molander, 2020. "Tackling complexity and problem formulation in rural electrification through conceptual modelling in system dynamics," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(1), pages 141-153, January.
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    25. Jason Madan & Meghan Bruce Kumar & Miriam Taegtmeyer & Edwine Barasa & Swaran Preet Singh, 2020. "SEEP-CI: A Structured Economic Evaluation Process for Complex Health System Interventions," IJERPH, MDPI, vol. 17(18), pages 1-12, September.

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