UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Analyzing the Simonshaven Case Using Bayesian Networks

Fenton, N; Neil, M; Yet, B; Lagnado, D; (2019) Analyzing the Simonshaven Case Using Bayesian Networks. Topics in Cognitive Science 10.1111/tops.12417. (In press).

[img] Text
simonshaven_final_resubmission.pdf - ["content_typename_Accepted version" not defined]
Access restricted to UCL open access staff until 13 March 2020.

Download (2MB)

Abstract

This paper is one in a series of analyses of the Dutch Simonshaven murder case, each using a different modeling approach. We adopted a Bayesian network (BN)–based approach which requires us to determine the relevant hypotheses and evidence in the case and their relationships (captured as a directed acyclic graph) along with explicit prior conditional probabilities. This means that both the graph structure and probabilities had to be defined using subjective judgments about the causal, and other, connections between variables and the strength and nature of the evidence. Determining if a useful BN could be quickly constructed by a small group using the previously established idioms-based approach which provides a generic method for translating legal cases into BNs, was a key aim. The model described was built by the authors during a workshop dedicated to the case at the Isaac Newton Institute, Cambridge, in September 2016. The total effort involved was approximately 26 h (i.e., an average of 6 h per author). With the basic assumptions described in the paper, the posterior probability of guilt once all the evidence is entered is 74%. The paper describes a formal evaluation of the model, using sensitivity analysis, to determine how robust the model conclusions are to key subjective prior probabilities over a full range of what may be deemed “reasonable” from both defense and prosecution perspectives. The results show that the model is reasonably robust—pointing not only generally to a reasonably high posterior probability of guilt but also generally below the 95% threshold expected in criminal law. Given the constraints on building a complex model so quickly, there are inevitably weaknesses; hence, the paper describes these and how they might be addressed, including how to take account of supplementary case information not known at the time of the workshop.

Type: Article
Title: Analyzing the Simonshaven Case Using Bayesian Networks
DOI: 10.1111/tops.12417
Publisher version: https://doi.org/10.1111/tops.12417
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bayesian networks, Legal reasoning, Probability, Uncertainty, Evidence, Idioms
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology
URI: http://discovery.ucl.ac.uk/id/eprint/10071700
Downloads since deposit
1Download
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item