Causal models versus reason models in Bayesian networks for legal evidence
Bibliographic Data
| ID | 10806410 |
|---|---|
| Authors | Christian Dahlman (0000-0003-3118-9486, Lund University, corresponding author), Eivind Kolflaath (University of Bergen) |
| Year | 2022 |
| Volume | 200 |
| Issue | 6 |
| Publication date | 2022-11-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Synthese (JOURNAL) |
| Journal identifiers | ISSN: 0039-7857 • E-ISSN: 1573-0964 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11229-022-03953-y |
| OpenAlex | W4309001063 |
| Language | EN |
| References cited | 14 |
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
Bayesian inference · Bayesian network · Bayesian probability · Causal inference · Causal model · Econometrics · Epistemology · Inference · Metaphysics · Philosophy of language · Philosophy of science · Statistics · Artificial Intelligence · Bayesian Modeling and Causal Inference · Computer Science · Jury Decision Making Processes · Law, Economics, and Judicial Systems · Mathematics · Philosophy
| Citation velocity | historical |
|---|---|
| Highly cited | No |