A distance-based method for preference information retrieval in paired comparisons

Abstract

The pairwise comparison method is an interesting technique for assessing priority weights for a finite set of objects. In fact, some web search engines use this inference tool to quantify the importance of a set of web sites. In this paper we deal with the problem of incomplete paired comparisons. Specifically, we focus on the problem of retrieving preference information (as priority weights) from incomplete pairwise comparison matrices generated during a group decision-making process. The proposed methodology solves two problems simultaneously: the problem of deriving preference weights when not all data are available and the implicit consensus problem. We consider an approximation methodology within a flexible and general distance framework for this purpose. © Springer-Verlag Berlin Heidelberg 2005.