Representing and Evaluating Legal Narratives with Subscenarios in a Bayesian network

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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 (2013) is extended with a subscenario idiom and it is shown how the method can be used to represent characteristic features of narrative.
Originele taal-2English
TitelProceedings of the 2013 Workshop on Computational Models of Narrative (CMN 2013)
RedacteurenM. A. Finlayson, B. Fisseni, B. Löwe, J. C. Meister
Plaats van productieDagstuhl
Aantal pagina's18
StatusPublished - 2013
Evenement2014 Workshop on Computational Models of Narrative - Hamburg, Germany
Duur: 4-aug.-20136-aug.-2013


Workshop2014 Workshop on Computational Models of Narrative

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