A Formal Account for Reasoning About Uncertain Probabilities in Intelligence Analysis

In the sixth assessment of the international panel on climate change (IPCC) one can read sentences such as '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 are very common in the intelligence community, but a formal account for dealing with the degree of belief and of confidence is still missing. 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. 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.