Provenance Across Evidence Combination in Theory of Belief Functions
Paweł Kowalski, Trevor Martin · 2018
Theory of belief functions (Dempster-Shafer theory) is one of the most commonly used mathematical frameworks in the field of uncertain information representation. Two important areas of research in its context are that of evidence combination and decision making. Although they are often considered theoretically separate, the combination process itself drives the final decision. The information contained in each of the sources propagates through the belief aggregation process, and impacts the final assessment to some degree. If the decision made has an impact on the real-world; particularly in scenarios where a wrong decision may bring about significant risks it is prudent to be able to identify the key drivers of this assessment. In this paper we present a novel method of identifying the relative contribution of each source of evidence to the final belief, thus making it possible to track provenance across the information fusion process. This is likely to be useful in intelligent decision-support systems utilising belief functions, where lack of transparency may be hindering adoption. Unlike traditional methods, which focus on the content of the contributing source only, the approach proposed here is based on analysis of dissimilarity between the contributing sources; the result of the fusion process and the decision made. The behaviour of this metric is analysed through simulation. It is shown that the proposed measure performs well with regard to identifying the source having the most significant impact on the decision, often outperforming more traditional metrics.