AIOct 14, 2025

A Non-overlap-based Conflict Measure for Random Permutation Sets

arXiv:2510.16001v1h-index: 4
Originality Incremental advance
AI Analysis

This work addresses conflict measurement for uncertain information fusion involving order structures, which is an incremental advancement in the domain of evidence theory.

The paper tackled the problem of measuring conflict between uncertain evidence represented by permutation mass functions in random permutation sets, proposing a non-overlap-based conflict measure method that incorporates top-weightedness and flexibility in parameters.

Random permutation set (RPS) is a new formalism for reasoning with uncertainty involving order information. Measuring the conflict between two pieces of evidence represented by permutation mass functions remains an urgent research topic in order-structured uncertain information fusion. In this paper, a detailed analysis of conflicts in RPS is carried out from two different perspectives: random finite set (RFS) and Dempster-Shafer theory (DST). Starting from the observation of permutations, we first define an inconsistency measure between permutations inspired by the rank-biased overlap(RBO) measure and further propose a non-overlap-based conflict measure method for RPSs. This paper regards RPS theory (RPST) as an extension of DST. The order information newly added in focal sets indicates qualitative propensity, characterized by top-ranked elements occupying a more critical position. Some numerical examples are used to demonstrate the behavior and properties of the proposed conflict measure. The proposed method not only has the natural top-weightedness property and can effectively measure the conflict between RPSs from the DST view but also provides decision-makers with a flexible selection of weights, parameters, and truncated depths.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes