Skip navigation

Belief revision as applied within a descriptive model of jury deliberations

Belief revision as applied within a descriptive model of jury deliberations

Dragoni, A.F., Giorgini, P. and Nissan, E. (2001) Belief revision as applied within a descriptive model of jury deliberations. Information & Communications Technology Law, 10 (1). pp. 53-65. ISSN 1360-0834 (Print), 1469-8404 (Online)

Full text not available from this repository.


Belief revision is a well-researched topic within Artificial Intelligence (AI). We argue that the new model of belief revision as discussed here is suitable for general modelling of judicial decision making, along with the extant approach as known from jury research. The new approach to belief revision is of general interest, whenever attitudes to information are to be simulated within a multi-agent environment with agents holding local beliefs yet by interacting with, and influencing, other agents who are deliberating collectively. The principle of 'priority to the incoming information', as known from AI models of belief revision, is problematic when applied to factfinding by a jury. The present approach incorporates a computable model for local belief revision, such that a principle of recoverability is adopted. By this principle, any previously held belief must belong to the current cognitive state if consistent with it. For the purposes of jury simulation such a model calls for refinement. Yet, we claim, it constitutes a valid basis for an open system where other AI functionalities (or outer stimuli) could attempt to handle other aspects of the deliberation which are more specific to legal narratives, to argumentation in court, and then to the debate among the jurors.

Item Type: Article
Uncontrolled Keywords: belief revision, jury deliberations, communications technology
Subjects: K Law > K Law (General)
Q Science > QA Mathematics > QA76 Computer software
Pre-2014 Departments: School of Computing & Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:00
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

Actions (login required)

View Item View Item