Empty set biasing issues in the Transferable Belief Model for fusing and decision making

Abstract

This paper is a tutorial on how the empty set can be used within set theory. We look at common misconceptions and the effect that these can have when applied to those set based models. The role of the empty set when fusing data and information modelled in sets can be confusing and incorrectly implemented. We show how the empty set has implications when decisions are made in the Transferable Belief Model (TBM), even having the ability to completely negate the open world concept of the TBM. We aim to highlight the common problems the empty set can cause so that they can be avoided by correct implementation and also suggest appropriate improvements so that the empty set does not impair results when making decisions, specifically within the TBM.