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Testing Behavioral Simulation Models by Direct Experiment

Author

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  • John D. Sterman

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

The decision rules in simulation models of human behavior purport to describe decision-making behavior as it is and not as it should optimally be. Without the criterion of optimality to judge the appropriateness of a decision rule, simulation modelers must rely on empirical confirmation of the structure of their models. In models of small organizations, traditional social science methods may be used. But these methods are infeasible in models of larger systems such as industries or the macroeconomy. This paper shows how direct experiment can be used to confirm or disconfirm the decision rules in simulation models. Direct experiment uses interactive gaming in which people play a role in the system being modeled. Subjects play the game in an institutional context corresponding to that of the model to be tested, and are given the same information set, but are free to make decisions any way they wish. The behavior of subjects can then be directly compared against the behavior produced by the assumed decision rules of the model. Differences between experiments to test simulation models and experiments in psychology and economics are discussed. The decision rule for capital investment in a simple macroeconomic model is tested as an example of the experimental approach. The model considers interactions between the multiplier and accelerator, a well-known structure underlying popular theories of business fluctuations, but one which has not been tested experimentally. Through their investment decisions, subjects attempt to balance demand and supply over time. The results strongly support the decision rule of the original model. Both model and subjects produce dysfunctional oscillations. The behavior of the subjects is qualitatively and quantitatively similar to that of the original model. Explanations for the bounded rationality of the subjects' behavior are considered, and the correspondence of the experiment to reality is discussed. The results demonstrate the promise of experimental methods as a complementary approach to econometrics in testing simulation models.

Suggested Citation

  • John D. Sterman, 1987. "Testing Behavioral Simulation Models by Direct Experiment," Management Science, INFORMS, vol. 33(12), pages 1572-1592, December.
    • Handle: RePEc:inm:ormnsc:v:33:y:1987:i:12:p:1572-1592
    DOI: 10.1287/mnsc.33.12.1572
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    Citations

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    Cited by:

    1. John W. Boudreau, 2004. "50th Anniversary Article: Organizational Behavior, Strategy, Performance, and Design in Management Science," Management Science, INFORMS, vol. 50(11), pages 1463-1476, November.
    2. Arango, Santiago & Castañeda, Jaime A. & Larsen, Erik R., 2013. "Mothballing in power markets: An experimental study," Energy Economics, Elsevier, vol. 36(C), pages 125-134.
    3. 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.
    4. Stephen A. Spiller & Nicholas Reinholtz & Sam J. Maglio, 2020. "Judgments Based on Stocks and Flows: Different Presentations of the Same Data Can Lead to Opposing Inferences," Management Science, INFORMS, vol. 66(5), pages 2213-2231, May.
    5. Stephan Billinger & Kannan Srikanth & Nils Stieglitz & Terry R. Schumacher, 2021. "Exploration and exploitation in complex search tasks: How feedback influences whether and where human agents search," Strategic Management Journal, Wiley Blackwell, vol. 42(2), pages 361-385, February.
    6. Stephen J. Mezias & Mary Ann Glynn, 1993. "The three faces of corporate renewal: Institution, revolution, and evolution," Strategic Management Journal, Wiley Blackwell, vol. 14(2), pages 77-101, February.
    7. Yilun Luo & Esmaeil Ahmadi & Benjamin C. McLellan & Tetsuo Tezuka, 2022. "Will Capacity Mechanisms Conflict with Carbon Pricing?," Energies, MDPI, vol. 15(24), pages 1-25, December.
    8. Chan, Chi Kin & Lee, H.W.J. & Wong, K.H., 2008. "Optimal feedback production for a two-level supply chain," International Journal of Production Economics, Elsevier, vol. 113(2), pages 619-625, June.
    9. Fu, Lingxian & Meng, Fanyong, 2020. "A human disease transmission inspired dynamic model for closed-loop supply chain management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
    10. 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.
    11. Yinhe Bu & Xingping Zhang, 2021. "On the Way to Integrate Increasing Shares of Variable Renewables in China: Experience from Flexibility Modification and Deep Peak Regulation Ancillary Service Market Based on MILP-UC Programming," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    12. Sterman, John, 1987. "Misperceptions of feedback in dynamic decisionmaking," Working papers 1899-87., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    13. Sterman, John, 1987. "Modeling managerial behavior--misperceptions of feedback in a dynamic decisionmaking experiment," Working papers 1933-87., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    14. Sterman, John. & Diehl, Ernst-Walter., 1993. "Effects of feedback complexity on dynamic decision making," Working papers 3608-93., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    15. Federico Cosenz & Guido Noto, 2016. "Applying System Dynamics Modelling to Strategic Management: A Literature Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 33(6), pages 703-741, November.
    16. Fu, Lingxian & Tang, Jie & Meng, Fanyong, 2021. "A disease transmission inspired closed-loop supply chain dynamic model for product collection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    17. Paich, Mark. & Sterman, John., 1992. "Boom, bust and failures to learn in experimental markets," Working papers 3441-92., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    18. Stephan Billinger & Nils Stieglitz & Terry R. Schumacher, 2014. "Search on Rugged Landscapes: An Experimental Study," Organization Science, INFORMS, vol. 25(1), pages 93-108, February.
    19. Son, Joong Y. & Sheu, Chwen, 2008. "The impact of replenishment policy deviations in a decentralized supply chain," International Journal of Production Economics, Elsevier, vol. 113(2), pages 785-804, June.
    20. Sterman, John. & Özveren, Cüneyt M., 1988. "Control-theory heuristics for improving the behavior of economic models," Working papers WP 2074-88., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    21. Liang, Sai & Zhang, Tianzhu, 2011. "Interactions of energy technology development and new energy exploitation with water technology development in China," Energy, Elsevier, vol. 36(12), pages 6960-6966.
    22. Gencer, Busra & van Ackere, Ann, 2021. "Achieving long-term renewable energy goals: Do intermediate targets matter?," Utilities Policy, Elsevier, vol. 71(C).
    23. John Sterman, 2018. "System dynamics at sixty: the path forward," System Dynamics Review, System Dynamics Society, vol. 34(1-2), pages 5-47, January.

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