A Comparison of Two Hybrid Methods for Analyzing Evidential Reasoning.

Reasoning with evidence is error prone, especially when qualitative and quantitative evidence is combined, as shown by infamous miscarriages of justice, such as the Lucia de Berk case in the Netherlands. Methods for the rational analysis of evidential reasoning come in different kinds, often with arguments, scenarios and probabilities as primitives. Recently various combinations of argumentative, narrative and probabilistic methods have been investigated. By the complexity and subtlety of the subject matter, it has proven hard to assess the specific strengths and points of attention of different methods. Comparative case studies have only recently started, and never by one team. In this paper, we provide an analysis of a single case in order to compare the relative merits of two methods recently proposed in AI and Law: a method using Bayesian networks with embedded scenarios, and a method using case models that provide a formal analysis of argument validity. To optimise the transparency of the two analyses, we have selected a case about which the final decision is undisputed. The two analyses allow us to provide a comparative evaluation showing strengths and weaknesses of the two methods. We find a core of evidential reasoning that is shared between the methods.