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Response surface methodology as a sensitivity analysis tool in decision analysis
Author(s) -
Bauer Kenneth W.,
Parnell Gregory S.,
Meyers David A.
Publication year - 1999
Publication title -
journal of multi‐criteria decision analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 47
eISSN - 1099-1360
pISSN - 1057-9214
DOI - 10.1002/(sici)1099-1360(199905)8:3<162::aid-mcda241>3.0.co;2-x
Subject(s) - sensitivity (control systems) , decision analysis , decision tree , computer science , response surface methodology , function (biology) , influence diagram , data mining , operations research , machine learning , mathematics , engineering , statistics , electronic engineering , evolutionary biology , biology
This paper proposes the use of Response Surface Methodology (RSM) as a sensitivity analysis tool for single attribute and multi‐attribute decision analysis (DA). It is shown that any single or multi‐attribute decision analysis value (or utility) function can be transformed to a response function of the uncertain variables. A sensitivity analysis framework designed to facilitate simultaneous perturbation of a number of uncertain variables is proposed. Specifically, RSM is used with influence diagrams, but the methodology can also be used with decision trees. This approach is illustrated with the well‐known Oil Wildcatter Problem. This new framework exceeds the current sensitivity analysis capability of decision analysis software tools. The approach is shown to be more efficient than current DA sensitivity analysis techniques and can provide improved insights for decision makers. Copyright © 1999 John Wiley & Sons, Ltd.