Representing and Evaluating Legal Narratives with Subscenarios in a Bayesian Network

Abstract

In legal cases, stories or scenarios can serve as the context for a
crime when reasoning with evidence. In order to develop a
scientifically founded technique for evidential reasoning, a method is
required for the representation and evaluation of various scenarios in
a case. In this paper the probabilistic technique of Bayesian networks
is proposed as a method for modeling narrative, and it is shown how
this can be used to capture a number of narrative properties.

Bayesian networks quantify how the variables in a case interact.
Recent research on Bayesian networks applied to legal cases includes
the development of a list of legal idioms: recurring substructures in
legal Bayesian networks. Scenarios are coherent presentations of a
collection of states and events, and qualitative in nature. A method
combining the quantitative, probabilistic approach with the narrative
approach would strengthen the tools to represent and evaluate
scenarios.

In a previous paper, the development of a design method for modeling
multiple scenarios in a Bayesian network was initiated. The design
method includes two narrative idioms: the scenario idiom and the
merged scenarios idiom. In this current paper, the method of Vlek, et
al. (2013) is extended with a subscenario idiom and it is shown how
the method can be used to represent characteristic features of
narrative.