Abstract
A key task while trying to reach consensus within a flexible attribute-set group decision-making (FAST-GDM) problem is the quantification of the level of concordance between the evaluations given by each participant and the collective evaluations computed for the group. To cope with that task, several theoretical concordance indices based on similarity measures designed to compare intuitionistic fuzzy sets (IFSs) have been introduced. Also, a visual tool, called IFS contrasting chart (IFSCC), has been proposed to facilitate an estimation of the level of concordance between individual and collective evaluations characterized respectively by two IFSs. Aiming to determine the level to which those theoretical indices reflect the perceived levels of concordance, in this paper we introduce a novel variant of an IFSCC, which includes an explicit ranking that provides additional information regarding a potential decision on the evaluated options. We use this variant in a test where individuals were asked to estimate the level of concordance between collective and individual evaluations obtained while solving a FAST-GDM problem. The results of this test and some suggestions about the use of such theoretical indices are presented.
This paper is an extended version of the work published in: Loor, M., Tapia-Rosero, A., De Tré, G.: Usability of Concordance Indices in FAST-GDM Problems. In: Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018). pp. 67–78. INSTICC, SciTePress (2018). DOI: 10.5220/0006956500670078.
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Appendix
Appendix
1.1 Evaluations About the ‘Best Smooth Dip’
This appendix presents the IFS contrasting charts (IFSCCs) of the evaluations given by 11 persons who tried to reach a consensus about the best smooth dip(s), among 3 potential dips, to pair with banana chips. In contrast to the IFSCCs used in [16], these IFSCCs have been augmented with the ranking of the options.
Figures 13 and 14 show the IFSCCs corresponding to the first round and the second round respectively. Figures 13a and 14a represent the collective evaluations computed for the group during the first and the second round respectively.
IFSCCs characterizing the evaluations obtained during Round 1
IFSCCs characterizing the evaluations obtained during Round 2
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Loor, M., Tapia-Rosero, A., De Tré, G. (2021). Concordance in FAST-GDM Problems: Comparing Theoretical Indices with Perceived Levels. In: Sabourin, C., Merelo, J.J., Barranco, A.L., Madani, K., Warwick, K. (eds) Computational Intelligence. IJCCI 2018. Studies in Computational Intelligence, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-030-64731-5_3
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