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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.

@article{ title = {Analyzing the Simonshaven Case Using Bayesian Networks}, type = {article}, year = {2019}, identifiers = {[object Object]}, keywords = {Bayesian networks,Evidence,Idioms,Legal reasoning,Probability,Uncertainty}, pages = {1-32}, websites = {http://doi.wiley.com/10.1111/tops.12417}, month = {3}, day = {12}, id = {3cde81b0-04e9-3e33-b4c2-9b387439f6e3}, created = {2019-12-23T02:15:41.990Z}, accessed = {2019-11-23}, file_attached = {false}, profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0}, group_id = {dd0b66b1-f60b-324e-aaa2-d1b8e5ba9e0c}, last_modified = {2019-12-23T02:15:41.990Z}, read = {false}, starred = {false}, authored = {false}, confirmed = {true}, hidden = {false}, private_publication = {false}, 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.}, bibtype = {article}, author = {Fenton, Norman and Neil, Martin and Yet, Barbaros and Lagnado, David}, journal = {Topics in Cognitive Science}, number = {November} }

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