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A New Approach on ρ to Decision Making Using Belief Functions Under Incomplete Information

  • Yuliang Fan
  • Peter Deer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

This paper discusses an expected utility approach on ρ to decision making under incomplete information using the belief function framework. In order to make rational decisions under incomplete information, some subjective assumptions often need to be made because of the interval representations of the belief functions. We assume that a decision maker may have some evidence from different sources about the value of ρ, and this evidence can also be represented by a belief function or can result in a unique consonant belief function that is constrained by the evidence over the same frame of discernment. We thus propose a novel approach based on the two-level reasoning Transferable Belief Model and calculate the expected utility value of ρ using pignistic probabilities transformed from the interval-based belief functions. The result can then be used to make a choice between overlapped expected value intervals. Our assumption is between the strongest assumption of a warranted point value of ρ and the weakest assumption of a uniform probability distribution for an unwarranted ρ.

Keywords

Hide Sector Belief Function Credal Level Focal Element Basic Probability Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yuliang Fan
    • 1
  • Peter Deer
    • 1
  1. 1.School of Information and Communication TechnologyGriffith University, Gold Coast CampusAustralia

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