A General Structure for Legal Arguments About Evidence Using Bayesian Networks
Article first published online: 30 OCT 2012
Copyright © 2012 Cognitive Science Society, Inc.
Volume 37, Issue 1, pages 61–102, January/February 2013
How to Cite
Fenton, N., Neil, M. and Lagnado, D. A. (2013), A General Structure for Legal Arguments About Evidence Using Bayesian Networks. Cognitive Science, 37: 61–102. doi: 10.1111/cogs.12004
- Issue published online: 17 JAN 2013
- Article first published online: 30 OCT 2012
- Received 22 November 2010; received in revised form 3 May 2012; accepted 4 May 2012
- Legal arguments;
- Bayesian networks
A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where BNs have been used in the legal context, they are presented as completed pieces of work, with no insights into the reasoning and working that must have gone into their construction. This means the process of building BNs for legal arguments is ad hoc, with little possibility for learning and process improvement. This article directly addresses this problem by describing a method for building useful legal arguments in a consistent and repeatable way. The method complements and extends recent work by Hepler, Dawid, and Leucari (2007) on object-oriented BNs for complex legal arguments and is based on the recognition that such arguments can be built up from a small number of basic causal structures (referred to as idioms). We present a number of examples that demonstrate the practicality and usefulness of the method.