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The Sources and Uses of Sensitivity Information

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  • Robert W. Blanning

    (The Wharton School, University of Pennsylvania)

Abstract

The central concept of management science is the concept of a model---that is, a relationship between those variables under the control of a decision-maker (decision variables), those not under his control (environmental variables), and one or more measures of cost or performance. To solve a model means to (1) experiment with the model to calculate the anticipated cost and performance of proposal decisions (simulation) or to calculate the decision variables that minimize or maximize a single measure of cost or performance with constraints on other measures (optimization), and (2) to perform sensitivity analyses that measure the rate of change of the “output” of the model (the cost and performance measures) with respect to the “inputs” (the decisions and the environment).The management science literature has emphasized the former objective, and in many cases the latter has been a byproduct. Optimization techniques often provide some useful sensitivity information with little or no additional computational effort once an optimal solution has been calculated. Simulations do not admit sensitivity calculations as easily. Since the sensitivity studies are usually accomplished by performing multiple simulations with marginally different inputs, the cost of performing such studies can be quite large. Therefore, it appears useful to outline the way in which sensitivity analyses are used in decision-making and to examine the way in which they are generated, with a view to reducing the computational effort needed to produce useful sensitivity information.

Suggested Citation

  • Robert W. Blanning, 1974. "The Sources and Uses of Sensitivity Information," Interfaces, INFORMS, vol. 4(4), pages 32-38, August.
  • Handle: RePEc:inm:orinte:v:4:y:1974:i:4:p:32-38
    DOI: 10.1287/inte.4.4.32
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    Cited by:

    1. Hawre Jalal & Bryan Dowd & François Sainfort & Karen M. Kuntz, 2013. "Linear Regression Metamodeling as a Tool to Summarize and Present Simulation Model Results," Medical Decision Making, , vol. 33(7), pages 880-890, October.
    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. Poropudas, Jirka & Virtanen, Kai, 2011. "Simulation metamodeling with dynamic Bayesian networks," European Journal of Operational Research, Elsevier, vol. 214(3), pages 644-655, November.
    4. Reis dos Santos, M. Isabel & Porta Nova, Acacio M.O., 2006. "Statistical fitting and validation of non-linear simulation metamodels: A case study," European Journal of Operational Research, Elsevier, vol. 171(1), pages 53-63, May.
    5. Kleijnen, J.P.C., 1977. "Generalizing simulation results through metamodels," Other publications TiSEM e671b2d6-c334-4938-99aa-3, Tilburg University, School of Economics and Management.
    6. 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.
    7. Kleijnen, J.P.C., 1978. "The role of statistical methodology in simulation," Other publications TiSEM 05085a08-4669-4775-adc5-4, Tilburg University, School of Economics and Management.

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