4.6 Article

Causal models versus reason models in Bayesian networks for legal evidence

Journal

SYNTHESE
Volume 200, Issue 6, Pages -

Publisher

SPRINGER
DOI: 10.1007/s11229-022-03953-y

Keywords

Evidence; Bayesian network; Law; Causality; Reason; Prior probability

Funding

  1. Lund University
  2. Torsten Soderbergs Stiftelse
  3. Ragnar Soderbergs stiftelse

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This paper compares causal models and reason models in the construction of Bayesian networks for legal evidence. It explores the differences between the two models and highlights the advantages of reason models, which are better aligned with the philosophy of Bayesian inference and more suited for measuring the combined support of evidence.
In this paper we compare causal models with reason models in the construction of Bayesian networks for legal evidence. In causal models, arrows in the network are drawn from causes to effects. In a reason model, the arrows are instead drawn towards the evidence, from factum probandum to factum probans. We explore the differences between causal models and reason models and observe several distinct advantages with reason models. Reason models are better aligned with the philosophy of Bayesian inference, as they model reasons for up-dating beliefs. Reason models are better suited for measuring the combined support of the evidence, and a prior probability of guilt that reflects the number of possible perpetrators is accommodated more easily with reason models.

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