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Inferring consensus weights from pairwise comparison matrices without suitable properties

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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.

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Authors and Affiliations

  1. Department of Artificial Intelligence, Computer Science School, Technical University of Madrid, Campus de Montegancedo s/n, 28660, Boadilla del Monte, Madrid, Spain

    Jacinto González-Pachón

  2. Department of Forest Economics and Management, Forestry School, Technical University of Madrid, Ciudad Universitaria s/n, 28040, Madrid, Spain

    Carlos Romero

Authors
  1. Jacinto González-Pachón
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  2. Carlos Romero
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Correspondence to Jacinto González-Pachón.

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González-Pachón, J., Romero, C. Inferring consensus weights from pairwise comparison matrices without suitable properties. Ann Oper Res 154, 123–132 (2007). https://doi.org/10.1007/s10479-007-0182-4

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  • DOI: https://doi.org/10.1007/s10479-007-0182-4

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