Filling in pattern designs for incomplete pairwise comparison matrices: (quasi-)regular graphs with minimal diameter

TL;DR

This paper investigates optimal patterns for incomplete pairwise comparison matrices using (quasi-)regular graphs with minimal diameter to reduce bias and errors in decision-making processes.

Contribution

It introduces a method for selecting comparison patterns based on graph theory, specifically focusing on (quasi-)regular graphs with minimal diameter to improve decision accuracy.

Findings

Identifies (quasi-)regular graphs with minimal diameter as optimal comparison patterns.

Provides practical recommendations and formats for implementing these graph-based patterns.

Demonstrates that low-diameter graphs reduce comparison bias and error accumulation.

Abstract

Multicriteria Decision Making problems are important both for individuals and groups. Pairwise comparisons have become popular in the theory and practice of preference modelling and quantification. We focus on decision problems where the set of pairwise comparisons can be chosen, i.e., it is not given a priori. The objective of this paper is to provide recommendations for filling patterns of incomplete pairwise comparison matrices (PCMs) based on their graph representation. Regularity means that each item is compared to others for the same number of times, resulting in a kind of symmetry. A graph on an odd number of vertices is called quasi-regular, if the degree of every vertex is the same odd number, except for one vertex whose degree is larger by one. If there is a pair of items such that their shortest connecting path is very long, the comparison between these two items relies on many intermediate comparisons, and is possibly biased by all of their errors. Such an example was previously found, where the graph generated from the table tennis players' matches included a long shortest path between two vertices (players), and the calculated result appeared to be misleading. If the diameter of the graph of comparisons is low as possible (among the graphs of the same number of edges), we can avoid, or, at least decrease, such cumulated errors. The aim of our research is to find graphs, among regular and quasi-regular ones, with minimal diameter. Both theorists and practitioners can use the results, given in several formats in the appendix: graph, adjacency matrix, list of edges.