An Empirical Evaluation of the Approximation of Subjective Logic Operators Using Monte Carlo Simulations

TL;DR

This paper evaluates the accuracy of subjective logic operators by comparing their approximate results to true probability transformations using Monte Carlo simulations, highlighting trade-offs between efficiency and bias.

Contribution

It introduces an experimental protocol leveraging Monte Carlo methods to quantify the bias of subjective logic operators in probability transformations.

Findings

Subjective logic operators exhibit measurable bias compared to true probability transformations.

The protocol provides estimates of approximation errors for subjective logic operators.

Empirical analysis of binomial multiplication and fusion operators demonstrates their approximation levels.

Abstract

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.