Conservative, Proportional and Optimistic Contextual Discounting in the Belief Functions Theory
This work addresses a specific problem in information fusion for researchers in belief functions theory, offering incremental improvements to modeling use cases like temporal discounting.
The paper tackled the limitations of existing discounting schemes in belief functions theory by proposing new contextual discounting schemes—conservative, proportional, and optimistic—and demonstrated their applicability to source reliability and temporal discounting, showing that classical discounting is a special case.
Information discounting plays an important role in the theory of belief functions and, generally, in information fusion. Nevertheless, neither classical uniform discounting nor contextual cannot model certain use cases, notably temporal discounting. In this article, new contextual discounting schemes, conservative, proportional and optimistic, are proposed. Some properties of these discounting operations are examined. Classical discounting is shown to be a special case of these schemes. Two motivating cases are discussed: modelling of source reliability and application to temporal discounting.