# Conflict management in information fusion with belief functions

arXiv: https://arxiv.org/abs/1709.04182 · HTML: https://mlsanity.com/paper/1709.04182/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Multimodal Learning with Uncertainty Quantification based on Discounted Belief Fusion](https://mlsanity.com/paper/2412.18024/) (2024)
- [Estimation of the qualification and behavior of a contributor and aggregation of his answers in a crowdsourcing context](https://mlsanity.com/paper/2303.04548/) (2023)
- [Evidential uncertainty sampling for active learning](https://mlsanity.com/paper/2309.12494/) (2023)
- [Belief functions on ordered frames of discernment](https://mlsanity.com/paper/2210.04535/) (2022)
- [Resolving Zadehs Paradox Axiomatic Possibility Theory as a Foundation for Reliable Artificial Intelligence](https://mlsanity.com/paper/2512.05257/) (2025)

## Abstract (arXiv)

In Information fusion, the conflict is an important concept. Indeed, combining several imperfect experts or sources allows conflict. In the theory of belief functions, this notion has been discussed a lot. The mass appearing on the empty set during the conjunctive combination rule is generally considered as conflict, but that is not really a conflict. Some measures of conflict have been proposed and some approaches have been proposed in order to manage this conflict or to decide with conflicting mass functions. We recall in this chapter some of them and we propose a discussion to consider the conflict in information fusion with the theory of belief functions.