Co‐designing and building an expert‐elicited non‐parametric Bayesian network model: demonstrating a methodology using a Bonamia Ostreae spread risk case study. Issue 6 (20th February 2022)

• Record Type: Journal Article
• Authors: Hanea, Anca M.; Hilton, Zoë; Knight, Ben; P. Robinson, Andrew
• Is Part Of: Risk analysis. Volume 42:Issue 6(2022)
• Page Start: 1235
• Page End: 1254
• Publication Date: 2022-02-20
• Subjects: Bayesian networks -- expert elicitation -- pathogens risk -- uncertainty quantification; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.13904
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: The development and use of probabilistic models, particularly Bayesian networks (BN), to support risk‐based decision making is well established. Striking an efficient balance between satisfying model complexity and ease of development requires continuous compromise. Codesign, wherein the structural content of the model is developed hand‐in‐hand with the experts who will be accountable for the parameter estimates, shows promise, as do so‐called nonparametric Bayesian networks (NPBNs), which provide a light‐touch approach to capturing complex relationships among nodes. We describe and demonstrate the process of codesigning, building, quantifying, and validating an NPBN model for emerging risks and the consequences of potential management decisions using structured expert judgment (SEJ). We develop a case study of the local spread of a marine pathogen, namely, Bonamia ostreae . The BN was developed through a series of semistructured workshops that incorporated extensive feedback from many experts. The model was then quantified with a combination of field and expert‐elicited data. The IDEA protocol for SEJ was used in its hybrid (remote and face‐to‐face) form to elicit information about more than 100 parameters. This article focuses on the modeling and quantification process, the methodological challenges, and the way these were addressed.