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Pseudo-Criteria Versus Linear Utility Function in Stochastic Multicriteria Acceptability Analysis

Risto Lahdelma, Pekka Salminen, Pseudo-Criteria Versus Linear Utility Function in Stochastic Multicriteria Acceptability Analysis. European Journal of Operational Research 141(2), 454–469, 2002.

Abstract:

Stochastic multicriteria acceptability analysis (SMAA) is a multicriteria decision support method for multiple decision makers (DMs) in discrete problems. SMAA does not require explicit or implicit preference information from the DMs. Instead, the method is based on exploring the weight space in order to describe the valuations that would make each alternative the preferred one. Partial preference information can be represented in the weight space analysis through weight distributions. In this paper we compare two variants of the SMAA method using randomly generated test problems with 2 to 12 criteria and 4 to 12 alternatives. In the original SMAA, a utility or value function models the DMs' preference structure, and the inaccuracy or uncertainty of the criteria is represented by probability distributions. In SMAA-3, ELECTRE III type pseudo-criteria are used instead. Both methods compute for each alternative an acceptability index measuring the variety of different valuations that support that alternative, and a central weight vector representing the typical valuations resulting in that decision. We seek answers to three questions: 1) how similar are the results provided by the decision models, 2) what kind of systematic differences exist between the models, and 3) how could one select indifference and preference thresholds of the pseudo-criteria model to match a utility model with given probability distributions?

BibTeX entry:

@ARTICLE{jLaSa02a,
  title = {Pseudo-Criteria Versus Linear Utility Function in Stochastic Multicriteria Acceptability Analysis},
  author = {Lahdelma, Risto and Salminen, Pekka},
  journal = {European Journal of Operational Research},
  volume = {141},
  number = {2},
  pages = {454–469},
  year = {2002},
  keywords = {Multiple criteria analysis, Decision support systems, Decision theory, Utility theory, Pseudo-criteria},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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