A multi-criteria perception-based strict-ordering algorithm for identifying the most-preferred choice among equally-evaluated alternatives

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Indecision constitutes a frequent and significant problem among decision makers (DMs). The substantial amount of potential choices available, the subjectivity and imprecision inherent in the information received, and the limited capacity of DMs to assimilate the information available are generally blamed for the resulting indecisiveness. We consider the problem of a DM who must rank a set of alternatives in a way that there are no two alternatives equally ranked. Each alternative is quantitatively and qualitatively described by a different information sender (IS). That is, the DM cannot observe the alternatives directly, he has to subjectively infer and evaluate them on the basis of the information provided by the ISs. The current paper introduces a novel algorithm that generates a strict order over the alternatives when DMs cannot decide among them. The proposed algorithm defines a lexicographic choice rule that is implementable over different evaluation loops. This algorithm complements and extends previous research on subjective evaluations and exchange reliability where the ISs–DMs interaction does not guarantee that DMs are able to make a unique most-preferred choice. We illustrate how this algorithm can also be adapted to account for the strategic manipulation of the information provided by the ISs and for different attitudes towards uncertainty on the side of the DMs.




Tavana, M., Di Caprio, D. and Santos-Arteaga, F.J. (2017) ‘A Multi-criteria Perception-Based Strict-Ordering Algorithm for Identifying the Most-Preferred Choice among Equally-Evaluated Alternatives,’ Information Sciences, Vol. 381, pp. 322-340.