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Inferring consensus weights from pairwise comparison matrices without suitable properties

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  • Jacinto González-Pachón
  • Carlos Romero

Abstract

Pairwise comparison is a popular method for establishing the relative importance of n objects. Its main purpose is to get a set of weights (priority vector) associated with the objects. When the information gathered from the decision maker does not verify some rational properties, it is not easy to search the priority vector. Goal programming is a flexible tool for addressing this type of problem. In this paper, we focus on a group decision-making scenario. Thus, we analyze different methodologies for getting a collective priority vector. The first method is to aggregate general pairwise comparison matrices (i.e., matrices without suitable properties) and then get the priority vector from the consensus matrix. The second method proposes to get the collective priority vector by formulating an optimization problem without determining the consensus pairwise comparison matrix beforehand. Copyright Springer Science+Business Media, LLC 2007

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  • Jacinto González-Pachón & Carlos Romero, 2007. "Inferring consensus weights from pairwise comparison matrices without suitable properties," Annals of Operations Research, Springer, vol. 154(1), pages 123-132, October.
    • Handle: RePEc:spr:annopr:v:154:y:2007:i:1:p:123-132:10.1007/s10479-007-0182-4
    DOI: 10.1007/s10479-007-0182-4
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    References listed on IDEAS

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    3. Brandt, Patric & Kvakić, Marko & Butterbach-Bahl, Klaus & Rufino, Mariana C., 2017. "How to target climate-smart agriculture? Concept and application of the consensus-driven decision support framework “targetCSA”," Agricultural Systems, Elsevier, vol. 151(C), pages 234-245.
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    5. Kun Chen & Gang Kou & J. Michael Tarn & Yan Song, 2015. "Bridging the gap between missing and inconsistent values in eliciting preference from pairwise comparison matrices," Annals of Operations Research, Springer, vol. 235(1), pages 155-175, December.
    6. Thierry Denœux & Marie-Hélène Masson, 2012. "Evidential reasoning in large partially ordered sets," Annals of Operations Research, Springer, vol. 195(1), pages 135-161, May.
    7. Amelia Bilbao-Terol & Mariano Jiménez & Mar Arenas-Parra, 2016. "A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planning," Annals of Operations Research, Springer, vol. 245(1), pages 137-162, October.
    8. Fu-Ling Cai & Xiuwu Liao & Kan-Liang Wang, 2012. "An interactive sorting approach based on the assignment examples of multiple decision makers with different priorities," Annals of Operations Research, Springer, vol. 197(1), pages 87-108, August.
    9. González-Pachón, Jacinto & Romero, Carlos, 2011. "The design of socially optimal decisions in a consensus scenario," Omega, Elsevier, vol. 39(2), pages 179-185, April.
    10. J. Fülöp & W. Koczkodaj & S. Szarek, 2012. "On some convexity properties of the Least Squares Method for pairwise comparisons matrices without the reciprocity condition," Journal of Global Optimization, Springer, vol. 54(4), pages 689-706, December.
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