Forecasting with jury-based probabilistic argumentation

Probabilistic Argumentation supports a form of hybrid reasoning by integrating quantitative (probabilistic) reasoning and qualitative argumentation in a natural way. Jury-based Probabilistic Argumentation supports the combination of opinions by different reasoners. In this paper we show how Jury-based Probabilistic Abstract Argumentation (JPAA) and a form of Jury-based Probabilistic Assumptionbased Argumentation (JPABA) can naturally support forecasting, whereby subjective probability estimates are combined to make predictions about future occurrences of events. The form of JPABA we consider is an instance of JPAA and results from integrating Assumption-Based Argumentation (ABA) and probability spaces expressed by Bayesian networks, under the so-called constellation approach. It keeps the underlying structured argumentation and probabilistic reasoning modules separate while integrating them. We show how JPAA and (the considered form of) JPABA can be used to support forecasting by 1) supporting different forecasters (jurors) to determine the probability of arguments (and, in the JPABA case, sentences) with respect to their own probability spaces, while sharing arguments (and their components); and 2) supporting the aggregation of individual forecasts to produce group forecasts