Plausibility Measures: A User's Guide

We examine a new approach to modeling uncer­ tainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially or­ dered set. This approach is easily seen to gener­ alize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. The lack of structure in a plausibility measure makes it easy for us to add structure on an "as needed" basis, letting us examine what is required to ensure that a plausi­ bility measure has certain properties of interest. This gives us insight into the essential features of the properties in question, while allowing us to prove general results that apply to many ap­ proaches to reasoning about uncertainty. Plau­ sibility measures have already proved useful in analyzing default reasoning. In this paper, we examine their "algebraic properties", analogues to the use of+ and x in probability theory. An understanding of such properties will be essential if plausibility measures are to be used in practice as a representation tool.