Research Note on Uncertain Probabilities and Abstract Argumentation
This work addresses the need for nuanced representation of uncertainty in argumentation systems, particularly for applications like climate discourse, but it is incremental as it builds on existing probabilistic and argumentation frameworks.
The paper tackles the problem of incorporating degrees of belief and confidence into abstract argumentation, proposing a formal method based on Sato's distribution semantics to enable probabilistic inference over queries in such settings.
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.