Approximations for Decision Making in the Dempster-Shafer Theory of Evidence

TL;DR

This paper evaluates approximation algorithms for the Dempster-Shafer theory of evidence, focusing on their effectiveness and tradeoffs in decision-making contexts, and introduces a new approximation method.

Contribution

It introduces a new approximation algorithm and provides an empirical analysis of existing methods' suitability for decision making.

Findings

Approximation algorithms can significantly reduce computational complexity.

Tradeoffs exist between accuracy and efficiency in approximation methods.

Empirical results guide the choice of approximation techniques in practice.

Abstract

The computational complexity of reasoning within the Dempster-Shafer theory of evidence is one of the main points of criticism this formalism has to face. To overcome this difficulty various approximation algorithms have been suggested that aim at reducing the number of focal elements in the belief functions involved. Besides introducing a new algorithm using this method, this paper describes an empirical study that examines the appropriateness of these approximation procedures in decision making situations. It presents the empirical findings and discusses the various tradeoffs that have to be taken into account when actually applying one of these methods.