  In this paper we analyze the use of subjective logic as a framework for
performing approximate transformations over probability distribution functions.
As for any approximation, we evaluate subjective logic in terms of
computational efficiency and bias. However, while the computational cost may be
easily estimated, the bias of subjective logic operators have not yet been
investigated. In order to evaluate this bias, we propose an experimental
protocol that exploits Monte Carlo simulations and their properties to assess
the distance between the result produced by subjective logic operators and the
true result of the corresponding transformation over probability distributions.
This protocol allows a modeler to get an estimate of the degree of
approximation she must be ready to accept as a trade-off for the computational
efficiency and the interpretability of the subjective logic framework.
Concretely, we apply our method to the relevant case study of the subjective
logic operator for binomial multiplication and fusion, and we study empirically
their degree of approximation.
