Bayesian networks (BNs) provide decision support in complex investigative domains where uncertainty plays a role, such as medicine, forensics and risk assessment. Yet, BNs are only sparsely used in practice. In data-poor domains, they have to be manually constructed, which is too time-consuming to support pressing decisions. Furthermore, few domain experts have the mathematical background to build a BN, a graph representing dependencies among variables with probability distributions over these variables. So despite the increased analytical power a BN could bring with respect to, for example, evidence aggregation or sensitivity analysis, many experts still use more qualitative concepts such as scenarios (stories, cases, timelines) and arguments (evidence graphs, ordered lists), which convey verbally expressed uncertainty ("strong evidence", "plausible scenarios").

If BNs are to be used in actual investigations, we need software tools and interfaces for BN construction that are engineered into the heart of the decision-making process. These tools should be based on familiar, more linguistically-oriented concepts such as arguments and stories, and complemented by algorithms intended to speed up and facilitate the BN-building process.

• dr. Remi Wieten
• prof. dr. F.J. (Floris) Bex
• dr. S. (Silja) Renooij

Lucia de Berk found out first-hand: evidence based on statistics can easily lead to errors. This project aims to help prevent this sort of error from occurring. The project's new approach is to link the successful statistical modelling technique of Bayesian networks to models that effectively dovetail legal argumentation and scenario construction in the legal world.

• prof. dr. J.J.C. (John-Jules) Meyer
• dr. S. (Silja) Renooij

• S.T. Timmer
• C.S. Vlek MSc
• dr. B. Verheij
• prof. dr. L.C. Verbrugge (Department of Artificial Intelligence; University of Groningen)