Multicriteria analysis with unknown preferences: an application of the smaa-2 method
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multicriteria decision aid, stochastic preferences, modelling of uncertainty - Monte Carlo simulationResumen
Applications of Multicriteria Decision Aiding are normally dependent on the preferences concerning alternatives for each criterion. They also depend on the measurements of the importance of criteria. Over the last decade, as a response to the fact that such preferences or measurements are often either not available or highly uncertain, Finnish researchers have developed a family of analytical methods called SMAA. Methods belonging to this family include SMAA-1, SMAA-D, SMAA-O, SMAA-2, SMAA-3, SMAA-A, SMAA-TRI, Ref-SMAA and SMAA-P. They consist, in essence, of formulating inverse problems in the weight space. These problems allow for the solving of multidimensional integrals and can be approached by the Monte Carlo simulation. In this article, the principal concepts of SMAA methods are presented. An example application to real data of one of the most important among these methods, the SMAA-2 method, is developed to demonstrate the main features of this approach. The article closes by addressing the appropriateness of using SMAA methods when the above-mentioned limitations prevail.Descargas
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