Article ID: | iaor19991035 |
Country: | Netherlands |
Volume: | 98 |
Issue: | 3 |
Start Page Number: | 563 |
End Page Number: | 570 |
Publication Date: | May 1997 |
Journal: | European Journal of Operational Research |
Authors: | Ringuest Jeffrey L. |
Keywords: | decision theory, decision theory: multiple criteria |
Analyzing the sensitivity of decisions to probability estimation error in single‐ and multi-attribute problems and to errors in estimating additive multi-attribute value models in multi-attribute problems is an integral part of decision analysis. This paper presents an intuitive and tractable approach to this sensitivity analysis. Here a decision is considered insensitive if: 1) the probabilities or multi-attribute weights required for any other alternative to become preferred are not close to the original estimated probabilities and weights, and 2) the rank order of states implied by the probabilities or the rank order of attributes implied by the additive multi-attribute weights must change for any other alternative to become preferred. The sensitivity analysis is conducted using straightforward linear programming models. An example is used to demonstrate their application.