Forecasting with jury-based probabilistic argumentation

Probabilistic Argumentation naturally supports the integration of quantitative (probabilistic) reasoning and qualitative argumentation. Meanwhile, Jury-based Probabilistic Argumentation supports the combination of opinions by different reasoners. We show how Jury-based Probabilistic Abstract Argumentation (JPAA) and a form of Jury-based Probabilistic Assumption-based Argumentation (JPABA) can naturally support forecasting, whereby subjective probability estimates are combined to make predictions about future 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. We show how JPAA and (the considered form of) JPABA can support forecasting by allowing different forecasters to determine the probability of arguments (and, in JPABA, sentences) with respect to their own probability spaces, while sharing arguments (and, in JPABA, their components). We show in turn how this supports the aggregation of individual forecasts into group forecasts.