Two approaches to reasoning about uncertainty are discussed. A parallel certainty inference model superimposes reasoning about the credibility of inferences on a deductive framework. A second approach identifies and implements representativeness as the general determinant of credibility in classification tasks. These approaches are steps in the evolution of endorsement-based reasoning, a view of uncertainty in terms of structured objects that represent characteristics of evidence
