Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment and in a way that makes sense with respect to the competing argument narratives. In contrast to this integrated approach, Non-Bayesian approaches to legal argumentation have tended to be narrative based and have focused on comparisons between competing stories and explanations. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization. The approach is consistent with subjectivist Bayesian philosophy. Practically, competing models of legal arguments are assessed by the extent to which the credibility of the sources of evidence are confirmed or disconfirmed in court. Those models that are more heavily disconfirmed are assigned lower weights, as model confidence measures, in the Bayesian model comparison and averaging approach adopted. In this way plurality of arguments are allowed yet a single judgement based on all arguments is possible and rational.
Authors: Prof. Martin Neil (Queen Mary, University of London), Prof. Norman Fenton (Queen Mary, University of London), Prof David Lagnado (UCL), and Prof Richard Gill (Leiden University)