Optimal partial questioning in large dimensional AHP

  • Authors:

  • Michele Fedrizzi;Silvio Giove

  • Affiliations:

  • University of Trento, Italy;University of Venice, Italy

  • Venue:
  • Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
  • Year:
  • 2009

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Abstract

In this paper we propose a flexible method for optimally choosing and sequencing in time a subset of pairwise comparisons between the alternatives in large--dimensional AHP problems. Two criteria are taken into account in defining the choice rule: the fair involvement of all the alternatives in the pairwise comparisons and the consistency of the elicited judgements. The combination of the two criteria guarantees the best reliability of the already collected information. The method indicates at each step the two alternatives to be next compared and stops the process taking into account both the reliability of the already elicited judgements and the necessity of bounding the potentially large number of judgements to be submitted to the decision maker.