Approximations for Decision Making in the Dempster-Shafer Theory of Evidence
This work addresses computational bottlenecks for practitioners using Dempster-Shafer theory in decision-making, but it is incremental as it builds on existing approximation methods.
The paper tackles the high computational complexity of reasoning in Dempster-Shafer theory by evaluating approximation algorithms that reduce focal elements in belief functions, presenting empirical findings on their appropriateness and trade-offs in decision-making scenarios.
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.