dc.contributor.author | FENTON, NE | |
dc.contributor.author | NEIL, M | |
dc.contributor.author | Gill, R | |
dc.contributor.author | Lagnado, D | |
dc.date.accessioned | 2019-03-18T15:07:01Z | |
dc.date.available | 2019-03-03 | |
dc.date.available | 2019-03-18T15:07:01Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Neil, M., Fenton, N., Lagnado, D. and Gill, R. (2019). Modelling Competing Legal Arguments using Bayesian Model Comparison and Averaging. [online] arXiv.org. Available at: https://arxiv.org/abs/1903.04891 [Accessed 18 Mar. 2019]. | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/56313 | |
dc.description.abstract | Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational. | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Artificial Intelligence and Law | |
dc.rights | This is a pre-copyedited, author-produced version of an article accepted for publication in Artificial Intelligence and Law following peer review. | |
dc.title | Modelling Competing Legal Arguments using Bayesian Model Comparison and Averaging | en_US |
dc.type | Article | en_US |
dc.rights.holder | © Springer Nature B.V. 2019 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
dcterms.dateAccepted | 2019-03-03 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | Effective Bayesian Modelling with Knowledge before Data::European Research Council | en_US |
qmul.funder | Effective Bayesian Modelling with Knowledge before Data::European Research Council | en_US |
qmul.funder | Effective Bayesian Modelling with Knowledge before Data::European Research Council | en_US |