%0 Journal Article
%T Epistemic Uncertainty Analysis: An Approach Using Expert Judgment and Evidential Credibility
%A Patrick Hester
%J Journal of Quality and Reliability Engineering
%D 2012
%R 10.1155/2012/617481
%X When dealing with complex systems, all decision making occurs under some level of uncertainty. This is due to the physical attributes of the system being analyzed, the environment in which the system operates, and the individuals which operate the system. Techniques for decision making that rely on traditional probability theory have been extensively pursued to incorporate these inherent aleatory uncertainties. However, complex problems also typically include epistemic uncertainties that result from lack of knowledge. These problems are fundamentally different and cannot be addressed in the same fashion. In these instances, decision makers typically use subject matter expert judgment to assist in the analysis of uncertainty. The difficulty with expert analysis, however, is in assessing the accuracy of the expert's input. The credibility of different information can vary widely depending on the expert¡¯s familiarity with the subject matter and their intentional (i.e., a preference for one alternative over another) and unintentional biases (heuristics, anchoring, etc.). This paper proposes the metric of evidential credibility to deal with this issue. The proposed approach is ultimately demonstrated on an example problem concerned with the estimation of aircraft maintenance times for the Turkish Air Force. 1. Introduction Real-world decision making is always performed under uncertainty. This uncertainty is present in the physical attributes of the system being analyzed, the environment in which it operates, and the individuals which operate the system. Decision makers must make decisions which best incorporate these uncertainties. With some problems, such as determining the probability of a terrorist attack on a given target, assigning probabilistic estimations to uncertain parameters is impossible due to the lack of statistical evidence upon which to base probabilistic estimates. Given these complex problems, decision makers often solicit subject matter expert opinion to provide estimates on uncertain parameters within a model. While this is a valid approach, soliciting expert opinions introduces additional uncertainty due to the varying degree of knowledge of the expert about the subject matter (i.e., one individual may truly be the world renowned expert in a field whereas others are merely seasoned practitioners). Additionally, as human beings, they have the potential for intentional and unintentional biases. The challenge when performing this type of analysis, in which expert judgment is essential to address uncertainty, is in assigning ¡°weights¡± to the
%U http://www.hindawi.com/journals/jqre/2012/617481/