Artificial Intelligence
Decision analysis using belief functions
Advances in the Dempster-Shafer theory of evidence
On decision making using belief functions
Advances in the Dempster-Shafer theory of evidence
The principle of minimum specificity as a basis for evidential reasoning
IPMU '86 Proceedings of the nternational Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems-Selected and Extended Contributions
An evidential theory approach to judgment-based decision-making (forest management)
An evidential theory approach to judgment-based decision-making (forest management)
Representing partial ignorance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.