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A Comment on Blanning's “Metamodel for Sensitivity Analysis: The Regression Metamodel in Simulation”

Author

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  • Jack P. C. Kleijnen

    (IBM Research, San Jose)

Abstract

The purpose of this note is to provide some statistical tools to make operational the idea of “metamodels” as discussed by Blanning (Blanning, R. W. 1974. The sources and uses of sensitivity information. Interfaces 4 (4, August) 32--38.) in a recent issue of Interfaces . He distinguishes between the simulation model or “decision model” itself and its approximation called the “metamodel”. An example of a simulation model may be a GPSS program for a queuing system, or a FORTRAN program for an inventory system. The purpose of the metamodel is to measure the sensitivity of the output (e.g., mean waiting time, mean inventory cost) to the inputs, the inputs being either decision variables (e.g., number of service stations, reorder point) or environmental variables (e.g., arrival rate, demand). The sensitivity to decision variables is required for finding optimal or sufficient solutions. The sensitivity to the environmental variables is needed to determine the validity range of the proposed solution. If the solution is very sensitive to the values of the environmental variables, then good estimates of these variables are needed, or suboptimal but more robust solutions (i.e., decision rules) may be suggested, etc.; see also Blanning (1974). As a “metamodel” to study these sensitivities, we propose the linear regression model.

Suggested Citation

  • Jack P. C. Kleijnen, 1975. "A Comment on Blanning's “Metamodel for Sensitivity Analysis: The Regression Metamodel in Simulation”," Interfaces, INFORMS, vol. 5(3), pages 21-23, May.
  • Handle: RePEc:inm:orinte:v:5:y:1975:i:3:p:21-23
    DOI: 10.1287/inte.5.3.21
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    Cited by:

    1. Cheng, R.C.H. & Kleijnen, J.P.C., 1995. "Optimal design of simulation experiments with nearly saturated queues," Discussion Paper 1995-67, Tilburg University, Center for Economic Research.
    2. Jin, Ding & Hedtrich, Johannes & Henning, Christian, 2018. "Applying Meta modeling for extended CGE-modeling: Sample techniques and potential application," Conference papers 332947, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    3. Kleijnen, J.P.C., 1997. "Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models," Discussion Paper 1997-52, Tilburg University, Center for Economic Research.
    4. Kleijnen, J.P.C., 2006. "Regression Models and Experimental Designs : A Tutorial for Simulation Analaysts," Other publications TiSEM 7b8ecddb-f49e-4b80-865b-a, Tilburg University, School of Economics and Management.
    5. Jin, Ding & Hedtrich, Johannes & Henning, Christian H. C. A., 2018. "Applying meta-modeling for extended CGE-modeling: Sampling techniques and potential application," Working Papers of Agricultural Policy WP2018-03, University of Kiel, Department of Agricultural Economics, Chair of Agricultural Policy.
    6. Mukashov, Askar & Jin, Ding & Henning, Christian & Hedtrich, Johannes, 2019. "Estimation of Growth Elasticities in the General Equilibrium Framework under Model Uncertainty," Conference papers 333081, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    7. Jin, Ding & Thube, Sneha Dattatraya & Hedtrich, Johannes & Henning, Christian & Delzeit, Ruth, 2019. "A Baseline Calibration Procedure for CGE models: An Application for DART," Conference papers 333057, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.

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