Toward a Characterization of Uncertainty Measure for the Dempster-Shafer Theory
This work addresses a foundational issue in uncertainty quantification for AI and decision-making, but it appears incremental as it builds on existing measures like AU.
The paper tackles the problem of characterizing uncertainty measures in Dempster-Shafer Theory by proposing a set of axiomatic requirements and proves that the recently proposed measure AU is minimal among all measures satisfying these requirements.
This is a working paper summarizing results of an ongoing research project whose aim is to uniquely characterize the uncertainty measure for the Dempster-Shafer Theory. A set of intuitive axiomatic requirements is presented, some of their implications are shown, and the proof is given of the minimality of recently proposed measure AU among all measures satisfying the proposed requirements.