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Validation of Simulation Results

Author

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  • Richard L. Van Horn

    (Carnegie-Mellon University)

Abstract

Simulation models are designed and used with a goal of learning about a process. Validation is the act of increasing to an acceptable level the confidence that an inference about a simulated process is correct for the actual process. There is no such thing as "the test". The experimenter selects a set of tests from the many possible--a standard decision problem of balancing the cost of testing against the cost of an incorrect inference. This paper outlines possible validation actions and suggests considerations that enter into their choice. Three major classes of actions are finding models with face validity, testing assumptions and testing input-output transformations. Actions range from such highly technical approaches as spectral analysis to behaviorally oriented ones such as the "Turing" test. Validation often becomes more tractable if simulation is viewed as one of several modes of investigation. Complementary research activities--related experiments, empirical analysis, analytic models, or prototypes--are widely used in the physical sciences to increase validity and appear equally appropriate for the management sciences. Some of the ideas and problems of validation are illustrated briefly in an example that involves all-machine simulation, man-machine simulation, related psychological experiments and a field test.

Suggested Citation

  • Richard L. Van Horn, 1971. "Validation of Simulation Results," Management Science, INFORMS, vol. 17(5), pages 247-258, January.
  • Handle: RePEc:inm:ormnsc:v:17:y:1971:i:5:p:247-258
    DOI: 10.1287/mnsc.17.5.247
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    Cited by:

    1. Pachepsky, L. B. & Haskett, J. D. & Acock, B., 1996. "An adequate model of photosynthesis--I Parameterization, validation and comparison of models," Agricultural Systems, Elsevier, vol. 50(2), pages 209-225.
    2. Oliva, Rogelio, 2003. "Model calibration as a testing strategy for system dynamics models," European Journal of Operational Research, Elsevier, vol. 151(3), pages 552-568, December.
    3. Benjamin L. Turner & Vincent Tidwell & Alexander Fernald & José A. Rivera & Sylvia Rodriguez & Steven Guldan & Carlos Ochoa & Brian Hurd & Kenneth Boykin & Andres Cibils, 2016. "Modeling Acequia Irrigation Systems Using System Dynamics: Model Development, Evaluation, and Sensitivity Analyses to Investigate Effects of Socio-Economic and Biophysical Feedbacks," Sustainability, MDPI, vol. 8(10), pages 1-30, October.
    4. Ali Akhavan & Paulo Gonçalves, 2021. "Managing the trade‐off between groundwater resources and large‐scale agriculture: the case of pistachio production in Iran," System Dynamics Review, System Dynamics Society, vol. 37(2-3), pages 155-196, April.
    5. Arnold Reisman & Muhittin Oral, 2005. "Soft Systems Methodology: A Context Within a 50-Year Retrospective of OR/MS," Interfaces, INFORMS, vol. 35(2), pages 164-178, April.
    6. Pat-Anthony Federico & Paul W. Figliozzi, 1981. "Computer Simulation of Social Systems," Sociological Methods & Research, , vol. 9(4), pages 513-533, May.
    7. Vagnani, Gianluca, 2009. "The Black-Scholes model as a determinant of the implied volatility smile: A simulation study," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 103-118, October.
    8. Rogelio Oliva & John D. Sterman, 2001. "Cutting Corners and Working Overtime: Quality Erosion in the Service Industry," Management Science, INFORMS, vol. 47(7), pages 894-914, July.
    9. Lane, David C. & Oliva, Rogelio, 1998. "The greater whole: Towards a synthesis of system dynamics and soft systems methodology," European Journal of Operational Research, Elsevier, vol. 107(1), pages 214-235, May.
    10. Barry L. Nelson, 2004. "50th Anniversary Article: Stochastic Simulation Research in Management Science," Management Science, INFORMS, vol. 50(7), pages 855-868, July.
    11. Ning Nan & Robert Zmud & Emre Yetgin, 2014. "A complex adaptive systems perspective of innovation diffusion: an integrated theory and validated virtual laboratory," Computational and Mathematical Organization Theory, Springer, vol. 20(1), pages 52-88, March.
    12. Qudrat-Ullah, Hassan & Seong, Baek Seo, 2010. "How to do structural validity of a system dynamics type simulation model: The case of an energy policy model," Energy Policy, Elsevier, vol. 38(5), pages 2216-2224, May.
    13. David C. Lane & Özge Pala & Yaman Barlas & David C. Lane, 2015. "Validity is a Matter of Confidence—But Not Just in System Dynamics," Systems Research and Behavioral Science, Wiley Blackwell, vol. 32(4), pages 450-458, July.
    14. Saleh, Mohamed & Oliva, Rogelio & Kampmann, Christian Erik & Davidsen, Pål I., 2010. "A comprehensive analytical approach for policy analysis of system dynamics models," European Journal of Operational Research, Elsevier, vol. 203(3), pages 673-683, June.
    15. Anderson, Jock R., 1972. "An Overview of Modelling in Agricultural Management," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 40(03), pages 1-12, September.
    16. Throsby, C.D., 1973. "New Methodologies in Agricultural Production Economics: a Review," 1973 Conference, August 19-30, 1973, São Paulo, Brazil 181385, International Association of Agricultural Economists.
    17. Tian Heong Chan & Jürgen Mihm & Manuel E. Sosa, 2018. "On Styles in Product Design: An Analysis of U.S. Design Patents," Management Science, INFORMS, vol. 64(3), pages 1230-1249, March.

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