The Simonshaven murder case modelled as a Bayesian network

A paper published today in Topics in Cognitive Science is one in a series of analyses of a Dutch murder case, each using a different modelling approach. In this case a woman was murdered while out walking with her husband in a quiet recreational area near the village of Simonshaven, close to Rotterdam, in 2011. The trial court of Rotterdam convicted the victim’s husband of murder by intentionally hitting and/or kicking her in the head and strangling her. For the appeal the defence provided new evidence about other ‘similar’ murders in the area committed by a different person.

The idea to use this case to evaluate a number of different methods for modelling complex legal cases was originally proposed by Floris Bex (Utrecht), Anne Ruth Mackor (Groningen) and Henry Prakken (Utrecht). In September 2016 -as part of our Programme Probability and Statistics in Forensic Science at the Isaac Newton Institute Cambridge - a special two-day workshop was arranged in which different teams were presented with the Simonshaven evidence and had to produce a model analysis. At the time the Appeal was still to be heard. In a follow-up workshop to review the various solutions (held in London in June 2017 as part of the BAYES-KNOWLEDGE project) the participants agreed to publish their results in a special issue of a journal.

This paper describes the Bayesian Network (BN) team's solution. One of the key aims was to  determine if a useful BN could be quickly constructed using the previously established idioms-based approach (this provides a generic method for translating legal cases into BNs). The BN model described was built by the authors during the course of the workshop. The total effort involved was approximately 26 hours (i.e. an average of 6 hours 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 defence and prosecution perspectives. The results show that the model is reasonably robust - pointing generally to a reasonably high posterior probability of guilt, but also generally below the 95% threshold expected in criminal law.

The authors acknowledge the insights of  the following workshop participants: Floris Bex, Christian Dahlman, Richard Gill, Anne Ruth Mackor, Ronald Meester, Henry Prakken, Leila Schneps, Marjan Sjerps, Nadine Smit, Bart Verheij, and Jacob de Zoete.


Full reference:

Fenton, N. E., Neil, M., Yet, B., & Lagnado, D. A. (2019). "Analyzing the Simonshaven Case using Bayesian Networks". Topics in Cognitive Science, 10.1111/tops.12417.  For those without a subscription to the journal, the published version can be read here: https://rdcu.be/bqYxp)  

See also:

• Bayesian networks and argumentation 
• Challenging claims that probability theory is incompatible with legal reasoning
• Probability and statistics in forensics