Incomplete Information within Relative Pairwise Comparisons as Utilized by the AHP

Motivated by a rising demand for feasible and practical decision aids this article relaxes the need for complete pairwise comparisons in Analytic Hierarchy Process (AHP) based approaches regularly applied in multiple criteria decision making practice. We propose a method based on the k-walk procedure from Graph Theory to extract a weighted ranking profile from an incomplete pairwise comparison matrix offering increased flexibility and practicability to the user. Our simulation tests show relative advantages over Harker’s method. The connection into Graph Theory offers an understandable interpretation of weight vectors derived from either complete, incomplete, consistent or inconsistent pairwise comparison data.