Exploiting the Capabilities of Bayesian Networks for Engineering Risk Assessment: Causal Reasoning through Interventions

AbstractIn the last decade, Bayesian networks (BNs) have been widely used in engineering risk assessment due to the benefits that they provide over other methods. Among these, the most significant is the ability to model systems, causal factors, and their dependencies in a probabilistic manner. This capability has enabled the community to do causal reasoning throughassociations, which answers questions such as: “How does new evidence x′ about the occurrence of event X change my belief about the occurrence of event Y?” Associative reasoning has helped risk analysts to identify relevant risk‐contributing factors and perform scenario analysis by evidence propagation. However, engineering risk assessme nt has yet to explore other features of BNs, such as the ability to reason throughinterventions, which enables the BN model to support answering questions of the form “How doesdoing X=x ′ change my belief about the occurrence of event Y?” In this article, we propose to expand the scope of use of BN models in engineering risk assessment to support intervention reasoning. This will provide more robust risk‐informed decision support by enabling the modeling of policies and actio ns before being implemented. To do this, we provide the formal mathematical background and tools to model interventions in BNs and propose a framework that enables its use in engineering risk assessment. This is demonstrated in an illustrative case study on third‐party damage of natural gas p...
Source: Risk Analysis - Category: International Medicine & Public Health Authors: Tags: Original Research Article Source Type: research