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（登普斯特-沙弗理论、信念函数理论）在数据融合中的应用日益广泛，但其在社会与生命科学领域的潜力常因人们对这一理论独特特征缺乏认识而未能彰显。本文强调，证据理论能够表达源于对可能未预见事件发生之担忧的不确定性；相比之下，概率论必须局限于决策者当前设想的可能性。随后，我们阐释登普斯特-沙弗组合规则如何与概率论不同版本下的贝叶斯定理相关联，并探讨证据理论能够增强信息论的哪些应用。最后，我们通过一个实例来验证我们的论点：在该实例中，证据理论被用于解释审计工作中出现的部分重叠、部分矛盾的解决方案。