A lexicographically optimal completion for pairwise comparison matrices with missing entries

Estimating missing judgements is a key component in many multi-criteria decision making techniques, especially in the Analytic Hierarchy Process. Inspired by the Koczkodaj inconsistency index and a widely used solution concept of cooperative game theory called the nucleolus, the current study proposes a new algorithm for this purpose. In particular, the missing values are substituted by variables, and the inconsistency of the most inconsistent triad is reduced first, followed by the inconsistency of the second most inconsistent triad, and so on. The necessary and sufficient condition for the uniqueness of the suggested lexicographically optimal completion is proved to be a simple graph-theoretic notion: the undirected graph associated with the pairwise comparisons, where the edges represent the known elements, should be connected. Crucially, our method does not depend on an arbitrarily chosen measure of inconsistency as there exists essentially one reasonable triad inconsistency index.Comment: 17 pages, 2 figure

A lexicographically optimal completion for pairwise comparison matrices with missing entries

Estimating missing judgements is a key component in many multi-criteria decision making techniques, especially in the Analytic Hierarchy Process. Inspired by the Koczkodaj inconsistency index and a widely used solution concept of cooperative game theory called the nucleolus, the current study proposes a new algorithm for this purpose. In particular, the missing values are substituted by variables, and the inconsistency of the most inconsistent triad is reduced first, followed by the inconsistency of the second most inconsistent triad, and so on. The necessary and sufficient condition for the uniqueness of the suggested lexicographically optimal completion is proved to be a simple graph-theoretic notion: the undirected graph associated with the pairwise comparisons, where the edges represent the known elements, should be connected. Crucially, our method does not depend on an arbitrarily chosen measure of inconsistency as there exists essentially one reasonable triad inconsistency index

Are incomplete and self-confident preference relations better in multicriteria decision making? A simulation-based investigation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Incomplete preference relations and self-confident preference relations have been widely used in multicriteria decision-making problems. However, there is no strong evidence, in the current literature, to validate their use in decision-making. This paper reports on the design of two bounded rationality principle based simulation methods, and detailed experimental results, that aim at providing evidence to answer the following two questions: (1) what are the conditions under which incomplete preference relations are better than complete preference relations?; and (2) can self-confident preference relations improve the quality of decisions? The experimental results show that when the decision-maker is of medium rational degree, incomplete preference relations with a degree of incompleteness between 20% and 40% outperform complete preference relations; otherwise, the opposite happens. Furthermore, in most cases the quality of the decision making improves when using self-confident preference relations instead of incomplete preference relations. The paper ends with the presentation of a sensitivity analysis that contributes to the robustness of the experimental conclusions

A new parsimonious AHP methodology: assigning priorities to many objects by comparing pairwise few reference objects

We propose a development of the Analytic Hierarchy Process (AHP) permitting to use the methodology also for decision problems with a very large number of alternatives and several criteria. While the ap- plication of the original AHP method involves many pairwise comparisons between considered objects, that can be alternatives with respect to considered criteria or criteria between them, our parsimonious proposal is composed of five steps: (i) direct evaluation of the objects at hand; (ii) selection of some reference objects; (iii) application of the original AHP method to the reference objects; (iv) check of the consistency of the pairwise comparisons of AHP and the compatibility between the rating and the prior- itization with a subsequent discussion with the decision maker who can modify the rating or pairwise comparisons of reference objects; (v) revision of the direct evaluation on the basis of the prioritization supplied by AHP on reference objects. Our approach permits to avoid the distortion of comparing more relevant objects (reference points) with less relevant objects. Moreover, our AHP approach avoids rank reversal problems, that is, changes of the order in the prioritizations due to adding or removing one or more objects from the set of considered objects. The new proposal has been tested and experimentally validated

Incomplete pairwise comparative judgments: Recent developments and a proposed method

The current paper deals with incomplete Pairwise Comparisons (‘PWs’) when a large number of alternatives is evaluated. PWs are used to quantify decision maker's preferences, both ordinal and cardinal, in multi-criteria decision-making settings for eliciting the priorities of alternative options or weights of criteria. We use additive PWs with a different scale and show how 2-diagonal samples are used to deduce the implied weights thus prioritizing the alternatives. As a consequence, the number of PWs in incomplete judgment decision matrices is greatly reduced while preserving consistency and quality of the results. Computational results are provided and an example from the literature is applied to demonstrate the effectiveness of this method

An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The reciprocal preference relation (RPR) is a powerful tool to represent decision makers’ preferences in decision making problems. In recent years, various types of RPRs have been reported and investigated, some of them being the ‘classical’ RPRs, interval-valued RPRs and hesitant RPRs. Additive consistency is one of the most commonly used property to measure the consistency of RPRs, with many methods developed to manage additive consistency of RPRs. To provide a clear perspective on additive consistency issues of RPRs, this paper reviews the consistency measurements of the different types of RPRs. Then, consistency-driven decision making and information fusion methods are also reviewed and classified into four main types: consistency improving methods; consistency-based methods to manage incomplete RPRs; consistency control in consensus decision making methods; and consistency-driven linguistic decision making methods. Finally, with respect to insights gained from prior researches, further directions for the research are proposed