  While Evidence Theory (Demster-Shafer Theory, Belief Functions Theory) is
being increasingly used in data fusion, its potentialities in the Social and
Life Sciences are often obscured by lack of awareness of its distinctive
features. With this paper we stress that Evidence Theory can express the
uncertainty deriving from the fear that events may materialize, that one has
not been able to figure out. By contrast, Probability Theory must limit itself
to the possibilities that a decision-maker is currently envisaging.
  Subsequently, we illustrate how Dempster-Shafer's combination rule relates to
Bayes' Theorem for various versions of Probability Theory and discuss which
applications of Information Theory can be enhanced by Evidence Theory. Finally,
we illustrate our claims with an example where Evidence Theory is used to make
sense of the partially overlapping, partially contradictory solutions that
appear in an auditing exercise.
