The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling

The objective of this paper is to describe the potential offered by the Dempster-Shafer theory (DST) of evidence as a promising improvement on "traditional" approaches to decision analysis. Dempster-Shafer techniques originated in the work of Dempster on the use of probabilities with upper and lower bounds. They have subsequently been popularised in the literature on Artificial Intelligence (AI) and Expert Systems, with particular emphasis placed on combining evidence from different sources. In the paper we introduce the basic concepts of the DST of evidence, briefly mentioning its origins and comparisons with the more traditional Bayesian theory. Following this we discuss recent developments of this theory including analytical and application areas of interest. Finally we discuss developments via the use of an example incorporating DST with the Analytic Hierarchy Process (AHP).