Ordinal relative belief entropy
This work addresses a foundational issue in uncertainty measurement for AI and decision-making, offering a new approach to dynamic processes, though it appears incremental in extending entropy concepts.
The paper tackles the problem of measuring uncertainties in a frame of discernment by proposing a novel ordinal entropy that accounts for the order of proposition confirmation, addressing a gap where traditional entropies treat the frame as static. It provides numerical examples to verify the correctness and validity of the proposed entropy.
Specially customised Entropies are widely applied in measuring the degree of uncertainties existing in the frame of discernment. However, all of these entropies regard the frame as a whole that has already been determined which dose not conform to actual situations. In real life, everything comes in an order, so how to measure uncertainties of the dynamic process of determining sequence of propositions contained in a frame of discernment is still an open issue and no related research has been proceeded. Therefore, a novel ordinal entropy to measure uncertainties of the frame of discernment considering the order of confirmation of propositions is proposed in this paper. Compared with traditional entropies, it manifests effects on degree of uncertainty brought by orders of propositions existing in a frame of discernment. Besides, some numerical examples are provided to verify the correctness and validity of the proposed entropy in this paper.