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Simulation techniques for Stochastic Multicriteria Acceptability Analysis

Risto Lahdelma, Pekka Salminen, Simulation techniques for Stochastic Multicriteria Acceptability Analysis. In: K. Miettinen P. Salminen A. Salo R. Lahdelma (Ed.), Proceedings of the 67th meeting of the European Working Group “Multiple Criteria Decision Aiding”, Reports of the Department of Mathematical Information Technology, Series A, 163-177, University of Jyväskylä, 2008.

Abstract:

Stochastic multicriteria acceptability analysis (SMAA) is a family of methods for aiding multicriteria group decision making in problems with uncertain, imprecise, or partially missing information. Various kinds of uncertain information is represented using suitable probability distributions. Analysis is based on simulating different possible values for the uncertain parameters and collecting statistics of the performance of different alternatives. SMAA can be used with different decision models and in different problem formulations. SMAA is also suitable for the different phases of a decision process. A general approach for applying SMAA in real-life decision problems is to use it repetitively with more and more accurate information until the information is sufficient for making a decision. Between the analyses, information can be added by making more accurate criteria measurements, or assessing the DMs’ preferences more accurately in terms of various preference parameters.

BibTeX entry:

@INPROCEEDINGS{inpLaSa08a,
  title = {Simulation techniques for Stochastic Multicriteria Acceptability Analysis},
  booktitle = {Proceedings of the 67th meeting of the European Working Group “Multiple Criteria Decision Aiding”},
  author = {Lahdelma, Risto and Salminen, Pekka},
  series = {Reports of the Department of Mathematical Information Technology, Series A},
  editor = {R. Lahdelma, K. Miettinen P. Salminen A. Salo},
  publisher = {University of Jyväskylä},
  pages = {163-177},
  year = {2008},
  keywords = {Multicriteria decision aiding; SMAA; Simulation; Incomplete information},
}

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

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