Object-oriented graphical representations of complex patterns of evidence

Hepler, Amanda B.; Dawid, A. Philip; Leucari, Valentina

doi:10.1093/lpr/mgm005

Cited by 54 publications

(62 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Building on earlier work in this direction (Fenton et al 2013;Hepler et al 2007), these results show that Bayesian networks can be used to model arguments and structured hypotheses. Also two well-known issues encountered when using Bayesian networks come to light:…”

Section: Introductionsupporting

confidence: 61%

“…Bex has provided a hybrid model connecting arguments and scenarios (Bex 2011;Bex et al 2010), and has worked on the further integration of the two tools (Bex 2015;Bex and Verheij 2013). Connections between arguments and probabilities have been studied by Hepler et al (2007) combining object-oriented modeling and Bayesian networks. Fenton et al (2013) continued this work by developing representational idioms for the modeling of evidential reasoning in Bayesian networks.…”

Section: Discussionmentioning

confidence: 99%

“…For the Bayesian network modeling of scenarios, Vlek et al (2014) provides such a method, and Timmer et al (2015a) discusses the inclusion of argumentation schemes and their critical questions in a Bayesian network model. These works continue from the use of building blocks and idioms for building a Bayesian network model of the evidence in a criminal case, pioneered by Hepler et al (2007) and Fenton et al (2013). Perhaps ideas from these systematic modeling approaches can be adapted to the present setting.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Proof with and without probabilities

Verheij

2017

Artif Intell Law

View full text Add to dashboard Cite

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'.

show abstract

Section: Introductionsupporting

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Proof with and without probabilities

Verheij

2017

Artif Intell Law

View full text Add to dashboard Cite

show abstract

“…Nonetheless, some work has been done to simplify the construction of a complex network for a legal case (i.e., a network that is larger than a few nodes for analysing a technical result) by using basic structures that recur throughout various cases as building blocks. This was proposed by Hepler et al (2004), who used object-oriented Bayesian networks. Fenton et al (2013) proposed a list of legal idioms which served as basic building blocks for legal cases.…”

Section: Discussionmentioning

confidence: 99%

“…Nonetheless, some basic substructures recur throughout various cases, as pointed out by Hepler et al (2004). By using such substructures as basic building blocks, the task of constructing a Bayesian network graph is simplified.…”

Section: Constructing a Bayesian Network With Idiomsmentioning

confidence: 99%

A method for explaining Bayesian networks for legal evidence with scenarios

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

References

2014

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

View full text Add to dashboard Cite

Object-oriented graphical representations of complex patterns of evidence

Cited by 54 publications

References 17 publications

Proof with and without probabilities

Proof with and without probabilities

A method for explaining Bayesian networks for legal evidence with scenarios

References

Contact Info

Product

Resources

About