Modelling competing legal arguments using Bayesian model comparison and averaging

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.

Argument Quality in Real World Argumentation

“…And though it accords well with fundamental intuitions, it also corrects where intuitions go badly wrong. This, in principle, allows the Bayesian framework to provide normative guidance for any area concerned with argument quality and evidence evaluation whether this be education, the courts [76,77,78,79], or intelligence analysts [80,81]. Consequently, the Bayesian framework has enabled new descriptive projects assessing lay understanding of argument quality.…”

Leveraging a Bayesian network approach to model and analyze supplier vulnerability to severe weather risk: A case study of the U.S. pharmaceutical supply chain following Hurricane Maria

“…To further explore the versatility of Bayesian networks in multiple areas, readers should consider the works of Perez-Minaña [70], (natural resource management); Zhou et al [71] (safety risk analysis); Hossain et al [72] and Hossain et al [73] (waterway port); Neil et al [74] (legal arguments); Kabir et al [75]; Hossain et al [76] (supply chain); Ghosh et al [77] (project management),;Hossain et al [78] (electrical infrastructure); Hosseini and Sardar [79] (electric vehicle); Shin et al [80] (cyber risk); Saldao et al [81] (information dependencies); Alipio et al [82] (vehicle traffic and flood monitoring); Goyal and Chanda [83] (financial institution); Hossain et al [84]; and several others.…”

Improving the production and evaluation of structural models using a Delphi process

“…(Wintle & Nicholson, 2014;Chee, 2016), and the law (e.g. Fenton & Neil, 2000;Fenton, Neil & Lagnado, 2013;Neil et al, 2019). The US Intelligence Advanced Research Projects Activity (IARPA) have also shown interest in having groups of intelligence analysts build relevant causal BNs to support their analysis, and hence subsequent decision and policy making, by funding our current research under the CREATE program (Crowdsourcing Evidence, Argumentation, Thinking and Evaluation, see www.iarpa.gov/index.php/research-programs/create).…”