License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.CMN.2013.315
URN: urn:nbn:de:0030-drops-41373
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Vlek, Charlotte S. ; Prakken, Henry ; Renooij, Silja ; Verheij, Bart

Representing and Evaluating Legal Narratives with Subscenarios in a Bayesian Network

p315-vlek.pdf (3 MB)


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

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

BibTeX - Entry

  author =	{Charlotte S. Vlek and Henry Prakken and Silja Renooij and Bart Verheij},
  title =	{{Representing and Evaluating Legal Narratives with Subscenarios in a Bayesian Network}},
  booktitle =	{2013 Workshop on Computational Models of Narrative},
  pages =	{315--332},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-57-6},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{32},
  editor =	{Mark A. Finlayson and Bernhard Fisseni and Benedikt L{\"o}we and Jan Christoph Meister},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-41373},
  doi =		{10.4230/OASIcs.CMN.2013.315},
  annote =	{Keywords: Narrative, Scenarios, Bayesian networks, Legal evidence}

Keywords: Narrative, Scenarios, Bayesian networks, Legal evidence
Collection: 2013 Workshop on Computational Models of Narrative
Issue Date: 2013
Date of publication: 02.08.2013

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