# Research Note on Uncertain Probabilities and Abstract Argumentation

arXiv: https://arxiv.org/abs/2208.10932 · HTML: https://mlsanity.com/paper/2208.10932/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [A Differential Approach to Inference in Bayesian Networks](https://mlsanity.com/paper/1301.3847/) (2013)
- [A Comparative Study of Ranking-based Semantics for Abstract Argumentation](https://mlsanity.com/paper/1602.01059/) (2016)
- [Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits](https://mlsanity.com/paper/2102.10865/) (2021)
- [AFRA: Argumentation framework with recursive attacks](https://mlsanity.com/paper/1810.04886/) (2018)
- [SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation](https://mlsanity.com/paper/2110.01990/) (2021)
- [Probabilistic Logic Programming with Beta-Distributed Random Variables](https://mlsanity.com/paper/1809.07888/) (2018)

## Abstract (arXiv)

The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1.5C (medium confidence)." Such reports directly feed the public discourse, but nuances such as the degree of belief and of confidence are often lost. In this paper, we propose a formal account for allowing such degrees of belief and the associated confidence to be used to label arguments in abstract argumentation settings. Differently from other proposals in probabilistic argumentation, we focus on the task of probabilistic inference over a chosen query building upon Sato's distribution semantics which has been already shown to encompass a variety of cases including the semantics of Bayesian networks. Borrowing from the vast literature on such semantics, we examine how such tasks can be dealt with in practice when considering uncertain probabilities, and discuss the connections with existing proposals for probabilistic argumentation.