Modeling crime scenarios in a Bayesian Network

Legal cases involve reasoning with evidence and with the development of a software support tool in mind, a formal foundation for evidential reasoning is required. Three approaches to evidential reasoning have been prominent in the literature: argumentation, narrative and probabilistic reasoning. In this paper a combination of the latter two is proposed. In recent research on Bayesian networks applied to legal cases, a number of legal idioms have been developed as recurring structures in legal Bayesian networks. A Bayesian network quantifies how various variables in a case interact. In the narrative approach, scenarios provide a context for the evidence in a case. A method that integrates the quantitative, numerical techniques of Bayesian networks with the qualitative, holistic approach of scenarios is lacking. In this paper, a method is proposed for modeling several scenarios in a single Bayesian network. The method is tested by doing a case study. Two new idioms are introduced: the scenario idiom and the merged scenarios idiom. The resulting network is meant to assist a judge or jury, helping to maintain a good overview of the interactions between relevant variables in a case and preventing tunnel vision by comparing various scenarios

Unfolding crime scenarios with variations:a method for building a Bayesian network for legal narratives

Legal reasoning can be approached from various perspectives, traditionally argumentation, probability and narrative. The communication between forensic experts and a judge or jury would benefit from an integration of these approaches. In previous papers we worked on the connection between the narrative and the probabilistic approach. We developed techniques for representing crime scenarios in a Bayesian network. But for complex cases, the construction of a Bayesian network structure using these techniques remained a cumbersome task. In this paper we therefore propose a method called unfolding a scenario and a representation for small variations within a scenario. With these tools, a Bayesian network can be built up step by step, gradually adding more details. The method of unfolding a scenario is intended to support the process of building a Bayesian network, additionally resulting in a well-structured graphical structure

Representing and Evaluating Legal Narratives with Subscenarios in a Bayesian network

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