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Prediction and Prescription in Systems Modeling

Author

Listed:
  • Herbert A. Simon

    (Carnegie-Mellon University, Pittsburgh, Pennsylvania)

Abstract

Modeling is a principal tool for studying complex systems. Since models may be used for predictions, for analysis, or for prescription, we must ask what our goals are before we build our models. Historically, predictive numerical models have dominated our practice. Since the world we are modeling is orders of magnitude more complex than even the largest models our computers can handle, we must conserve computational power, first, by asking how much temporal detail we need and how much can be supported by available data and theories, second, by asking whether knowledge of steady states may not be more important than knowledge of temporal paths, third, by using the hierarchical properties of systems to aggregate and thereby simplify them, and, fourth, by substituting symbolic modeling, where appropriate, for numerical modeling.

Suggested Citation

  • Herbert A. Simon, 1990. "Prediction and Prescription in Systems Modeling," Operations Research, INFORMS, vol. 38(1), pages 7-14, February.
    • Handle: RePEc:inm:oropre:v:38:y:1990:i:1:p:7-14
    DOI: 10.1287/opre.38.1.7
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    Citations

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    Cited by:

    1. Julie Chytilová & Natálie Reichlová, 2007. "Komplexní systémy v teoriích F. A. Hayeka a H. A. Simona [Complex systems in the theories of F. A. Hayek and H. A . Simon]," Politická ekonomie, Prague University of Economics and Business, vol. 2007(5), pages 694-707.
    2. Makowski, Marek, 2000. "Modeling paradigms applied to the analysis of European air quality," European Journal of Operational Research, Elsevier, vol. 122(2), pages 219-241, April.
    3. Benedikt Martens & Frank Teuteberg, 2012. "Decision-making in cloud computing environments: A cost and risk based approach," Information Systems Frontiers, Springer, vol. 14(4), pages 871-893, September.
    4. Eriksson, D. M., 2003. "A framework for the constitution of modelling processes: A proposition," European Journal of Operational Research, Elsevier, vol. 145(1), pages 202-215, February.
    5. Bartoli, J.A. & Le Moigne, J.L., 1994. "Qualitative reasoning and complex symbol processing," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 36(2), pages 129-136.
    6. Le Moigne, Jean-Louis, 1995. "On theorizing the complexity of economic systems," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 24(3), pages 477-499.
    7. Amy Greenwald & Karthik Kannan & Ramayya Krishnan, 2010. "On Evaluating Information Revelation Policies in Procurement Auctions: A Markov Decision Process Approach," Information Systems Research, INFORMS, vol. 21(1), pages 15-36, March.
    8. Arthur Gaudron & Simon Tamayo & Arnaud de La Fortelle, 2019. "Interactive Simulation For Collective Decision Making In City Logistics," Post-Print hal-02532774, HAL.
    9. T. S. Raghu & P. K. Sen & H. R. Rao, 2003. "Relative Performance of Incentive Mechanisms: Computational Modeling and Simulation of Delegated Investment Decisions," Management Science, INFORMS, vol. 49(2), pages 160-178, February.
    10. Bart Verhees & Kees Van Kuijk & Lianne Simonse, 2017. "Care Model Design for E-Health: Integration of Point-of-Care Testing at Dutch General Practices," IJERPH, MDPI, vol. 15(1), pages 1-16, December.

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