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Critical Learning Incidents in system dynamics modelling engagements


  • Thompson, James P.
  • Howick, Susan
  • Belton, Valerie


This paper reports in-depth behavioural operational research to explore how individual clients learned to resolve dynamically complex problems in system dynamics model-based engagements. Consultant-client dyads involved in ten system dynamics consulting engagements were interviewed to identify individual clients' Critical Learning Incidents—defined as the moment of surprise caused after one's mental model produces unexpected failure and a change in one's mental model produces the desired result. The cases, which are reprised from interviews, include assessments of the nature of the engagement problem, the form of system dynamics model, and the methods employed by consultants during each phase of the engagement. Reported Critical Learning Incidents are noted by engagement phase and consulting method and constructivist learning theory is used to describe a pattern of learning. Research outcomes include descriptions of: the role of different methods applied in engagement phases (for example, the role of concept models to commence problem identification and to introduce iconography and jargon to the engagement participants); how model form associates with the timing of Critical Learning Incidents; and the role of social mediation and negotiation in the learning process.

Suggested Citation

  • Thompson, James P. & Howick, Susan & Belton, Valerie, 2016. "Critical Learning Incidents in system dynamics modelling engagements," European Journal of Operational Research, Elsevier, vol. 249(3), pages 945-958.
  • Handle: RePEc:eee:ejores:v:249:y:2016:i:3:p:945-958
    DOI: 10.1016/j.ejor.2015.09.048

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    References listed on IDEAS

    1. Tako, Antuela A. & Robinson, Stewart, 2010. "Model development in discrete-event simulation and system dynamics: An empirical study of expert modellers," European Journal of Operational Research, Elsevier, vol. 207(2), pages 784-794, December.
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    4. Hämäläinen, Raimo P. & Luoma, Jukka & Saarinen, Esa, 2013. "On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems," European Journal of Operational Research, Elsevier, vol. 228(3), pages 623-634.
    5. Sterman, John & Booth Sweeney, Linda, 2002. "Cloudy Skies: Assessing Public Understanding of Global Warming," Working papers 4361-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Mingers, John & White, Leroy, 2010. "A review of the recent contribution of systems thinking to operational research and management science," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1147-1161, December.
    7. John D. Sterman, 1989. "Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment," Management Science, INFORMS, vol. 35(3), pages 321-339, March.
    8. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, January.
    9. Schaffernicht, Martin & Groesser, Stefan N., 2011. "A comprehensive method for comparing mental models of dynamic systems," European Journal of Operational Research, Elsevier, vol. 210(1), pages 57-67, April.
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    Cited by:

    1. Elsawah, Sondoss & McLucas, Alan & Mazanov, Jason, 2017. "An empirical investigation into the learning effects of management flight simulators: A mental models approach," European Journal of Operational Research, Elsevier, vol. 259(1), pages 262-272.
    2. repec:eee:ejores:v:265:y:2018:i:2:p:673-684 is not listed on IDEAS
    3. repec:eee:ejores:v:268:y:2018:i:3:p:1178-1191 is not listed on IDEAS


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