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Risk-Based Scenario Analysis and Construction Using Bayesian Networks
Citation:
Carriger, John F. Risk-Based Scenario Analysis and Construction Using Bayesian Networks. ORDA Science Webinar, NA, N/A, November 20, 2020.
Impact/Purpose:
Presentation to the Department of Interior Office of Restoration and Damage Assessment (DOI ORDA) Science Webinar Series
Description:
Bayesian networks provide powerful tools for representing causal knowledge and the uncertainties in that knowledge. Risk assessments involve evaluating causal understanding of the impact of stressors on the environment and many of the key uncertainties are found within this understanding. The Bayesian network relative risk model (BN-RRM) has been at the forefront of the application of Bayesian networks to complex environmental problems. Past applications of the BN-RRM to ecological risk assessment include unintended risks of gene drives to the environment, salmonid population risks from multiple stressors, eDNA application to risk assessment, ecosystem service restoration for Superfund, and integrated (human and ecological) risk assessment. An early step in a BN-RRM is developing a causal structure. This relies on the information from problem formulation as well as the analytical needs in the risk assessment. A useful structure provides a platform for evaluating uncertainties and capturing quantitative probabilistic information from knowledge, data, and models. The conceptual model can serve as a basis for this structure and provides a system representation for developing scenarios and management interventions. This presentation will provide an overview of these topics and a discussion of how Bayesian networks can serve as a useful and flexible platform for capturing and representing the information in a risk assessment for causal analysis.