LP-metric sensitivity analysis for single and multi-attribute decision analysis |
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| Institution: | 1. LPMC and Institute of Statistics, Nankai University, Tianjin 300071, China;2. Department of Mathematics, Jiangsu University of Technology, Changzhou, 213001, China;1. R&D, National Instruments Corp., Berkeley, CA 94704, USA;2. Department of EEcS,, University of California, Berkeley, CA 94720, USA;1. Department of Analytical Chemistry and Pharmaceutical Technology, Center for Pharmaceutical Research, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium;2. Université Libre de Bruxelles (ULB), Boulevard du Triomphe accès 2, B-1050 Bruxelles, Belgium;3. Laboratory of Micro- and Photoelectronics, LAMI-ETRO, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium;1. Institute of Cybernetics at Tallinn University of Technology, Estonia;2. Institute of Automatic Control and Control Systems Technology, Johannes Kepler University of Linz, Austria |
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| 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 straight forward linear programming models. An example is used to demonstrate their application. |
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