Object-oriented graphical representations of complex patterns of evidence
Authors: Hepler, Amanda B.; Dawid, A. Philip; Leucari, Valentina
Source: Law, Probability and Risk, Volume 6, Numbers 1-4, 10 October 2007 , pp. 275-293(19)
Publisher: Oxford University Press
Abstract:We reconsider two graphical aids to handling complex mixed masses of evidence in a legal case: Wigmore charts and Bayesian networks. Our aim is to forge a synthesis of their best features and to develop this further to overcome remaining limitations. One important consideration is the multilayered nature of a complex case, which can involve direct evidence, ancillary evidence, evidence about ancillary evidence, etc. all of a number of different kinds. If all these features are represented in one diagram, the result can be messy and hard to interpret. In addition, there are often recurrent features and patterns of evidence and evidential relations, e.g. credibility processes or match identification (DNA, eyewitness evidence, etc.), that may appear, in identical or similar form, at many different places within the same network, or within several different networks, and it is wasteful to model all these individually. The recently introduced technology of object-oriented BNs suggests a way of dealing with these problems. Any network can itself contain instances of other networks, the details of which can be hidden from view until information on their detailed structure is desired. Moreover, generic networks to represent recurrent patterns of evidence can be constructed once and for all and copied or edited for reuse as needed. We describe the potential of this mode of description to simplify the construction and display of complex legal cases. To facilitate our narrative, the celebrated Sacco and Vanzetti murder case is used to illustrate the various methods discussed.
Document Type: Research Article
Publication date: 2007-10-10
- The journal publishes papers that deal with topics on the interface of law and probabilistic reasoning. These are interpreted broadly to include aspects relevant to the interpretation of scientific evidence, the assessment of uncertainty and the assessment of risk. The readership is primarily academic lawyers, mathematicians, statisticians and social scientists with interests in quantitative reasoning.