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.

翻译：尽管证据理论（Dempster-Shafer理论、置信函数理论）在数据融合领域的应用日益增多，但其在社会科学与生命科学中的潜力常因对其独特特征的认知不足而被忽视。本文强调，证据理论能够表达源于对事件可能发生、而决策者尚未能厘清之担忧所产生的不确定性。相比之下，概率论必须局限于决策者当前所能设想的可能性。随后，我们阐释了Dempster-Shafer合成规则如何与不同版本概率论中的贝叶斯定理相关联，并讨论了信息理论的哪些应用可通过证据理论得到增强。最后，我们通过一个审计案例中的示例说明我们的观点，其中证据理论被用于理解部分重叠、部分矛盾的解决方案。