LP-metric sensitivity analysis for single‐ and multi-attribute decision analysis

LP-metric sensitivity analysis for single‐ and multi-attribute decision analysis

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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:
Keywords: decision theory, decision theory: multiple criteria
Abstract:

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.

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