Plausibility Measures: A General Approach for Representing Uncertainty

Abstract

Introduction The standard approach to modeling uncertainty is probability theory. In recent years, researchers, motivated by varying concerns including a dissatisfaction with some of the axioms of probability and a desire to represent information more qualitatively, have introduced a number of generalizations and alternatives to probability, including Dempster-Shafer belief functions [Shafer, 1976] , possibility measures [Dubois and Prade, 1990] , lexicographic probability [Blume et al., 1991] , and many others. Rather than investigating each of these approaches piecemeal, I consider here an approach to representing uncertainty that generalizes them all, and lets us understand their commonalities and differences. A plausibility measure [Friedman and Halpern, 1995] associates with a set a plausibility, which is just an element in a partially ordered space. The only real requirement is that if U is a subset of V , the