Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning

Verheij, Bart; Bex, Floris; Timmer, Sjoerd T.; Vlek, Charles; Meyer, John‐Jules Ch.; Renooij, Silja; Prakken, Henry

doi:10.1093/lpr/mgv013

Cited by 44 publications

(37 citation statements)

References 45 publications

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“…Combinations of these approaches have been studied by Bex (2011), who combined arguments and scenarios, by Timmer et al (2015), who considered Bayesian networks and arguments, and by Verheij (2014), who combined arguments, scenarios and probabilities. An overview of these combined approaches can be found in Verheij et al (2015). Our contribution with this paper is to combine Bayesian networks and scenarios such that scenario-based reasoning can support the explanation of Bayesian networks.…”

Section: Discussionmentioning

confidence: 99%

A method for explaining Bayesian networks for legal evidence with scenarios

et al. 2016

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In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a popular tool for analysing parts of a case, constructing and understanding a network for an entire case is not straightforward. We propose an explanation method for understanding a Bayesian network in terms of scenarios. This method builds on a previously proposed construction method, which we slightly adapt with the use of scenario schemes for the purpose of explaining. The resulting structure is explained in terms of scenarios, scenario quality and evidential support. A probabilistic interpretation of scenario quality is provided using the concept of scenario schemes. Finally, the method is evaluated by means of a case study.

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Section: Discussionmentioning

confidence: 99%

A method for explaining Bayesian networks for legal evidence with scenarios

et al. 2016

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“…The theory has a clear normative aim (cf. Bex 2011, Verheij et al 2016, in that it tells us how we should reason with evidence. The hybrid theory provides a by providing a "logic of evidence and proof" based on stories and arguments.…”

Section: The Hybrid Theory As a Methods For Legal Argumentationmentioning

confidence: 99%

Evidence Assessment in Refugee Law with Stories and Arguments

Bex¹,

Bex-Reimert²

2016

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Abstract:In this article, we aim to analyse whether a systematic method for reasoning with evidence in legal cases -the hybrid theory of stories and arguments -can be applied to a novel legal domain, namely European asylum law. This analysis serves as a case study for testing the applicability of the hybrid theory outside of the context of criminal law. Furthermore, the analysis will provide insights on how the hybrid theory can be used to improve the normative framework for evidence assessment in asylum cases.Résumé: Dans cet article, nous visons à analyser si une méthode systématique de raisonner avec des preuves dans les affaires juridiquesla théorie hybride des histoires et des arguments -peut s'appliquer à un nouveau domaine juridique, à savoir le droit d'asile politique en Europe. Cette analyse est une étude de cas qui cherche à tester l'applicabilité de la théorie hybride hors du contexte du droit pénal. En outre, l'analyse permettra de mieux comprendre comment la théorie hybride peut être utilisée pour améliorer le cadre normatif employé pour évaluer des preuves dans les cas d'asile politique.

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“…In the literature, scenario schemes have been used to represent a scenario's completeness (Bex 2011;Bex and Verheij 2013;Verheij et al 2016;Vlek et al 2014Vlek et al , 2016, taking inspiration from the use of scripts in artificial intelligence and cognitive science (Schank and Abelson 1977). Here the cases in a case model are used to represent completeness.…”

Section: Scenariosmentioning

confidence: 99%

“…Research has started on addressing these issues by developing an argumentation theory that connects presumptive arguments, coherent hypotheses and degrees of uncertainty (Verheij 2014a, b;Verheij et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Proof with and without probabilities

Verheij

2017

Artif Intell Law

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Evidential reasoning is hard, and errors can lead to miscarriages of justice with serious consequences. Analytic methods for the correct handling of evidence come in different styles, typically focusing on one of three tools: arguments, scenarios or probabilities. Recent research used Bayesian networks for connecting arguments, scenarios, and probabilities. Well-known issues with Bayesian networks were encountered: More numbers are needed than are available, and there is a risk of misinterpretation of the graph underlying the Bayesian network, for instance as a causal model. The formalism presented here models presumptive arguments about coherent hypotheses that are compared in terms of their strength. No choice is needed between qualitative or quantitative analytic styles, since the formalism can be interpreted with and without numbers. The formalism is applied to key concepts in argumentative, scenario and probabilistic analyses of evidential reasoning, and is illustrated with a fictional crime investigation example based on Alfred Hitchcock's film 'To Catch A Thief'.

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Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning

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Cited by 44 publications

References 45 publications

A method for explaining Bayesian networks for legal evidence with scenarios

A method for explaining Bayesian networks for legal evidence with scenarios

Evidence Assessment in Refugee Law with Stories and Arguments

Proof with and without probabilities

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