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The best of all worlds: Integrating decision analysis and causal modeling with ecological risk assessments
Citation:
Carriger, J. The best of all worlds: Integrating decision analysis and causal modeling with ecological risk assessments. SETAC North America 40th Annual Meeting, Toronto, n/a, CANADA, November 03 - 07, 2019.
Impact/Purpose:
Requested talk that will discuss recent quantitative applications of ecological risk assessment and a potential path forward for risk assessment. This will be presented at the 40th annual SETAC North America Conference at the 40 Years of SETAC session.
Description:
For the past several decades, the ecological risk assessment (ERA) process has been an instrumental tool for government agencies to protect and evaluate risks to environmental resources. Predicting the future of ERA is difficult because of the many methodological improvements currently under development. One direction that has the potential to be especially useful for managing risks is the integration of ERA with decision analysis frameworks and tools. The primary goal of an ERA process is to provide information for decision making; consequently, better correspondence between risk assessment and decision making has been a topic of ongoing research and discussion. For example, integrating risk with decision making was a focus of a National Academy of Sciences charge by the United States Environmental Protection Agency in 2009. This was echoed in a 2011 journal special issue article and rejoinder discussions on moving towards a “solution-focused risk assessment” that integrates the decisions under consideration with the design and output of the risk assessment. Decision analysis frameworks and analytic structures can be especially useful for solution-focused ERAs by augmenting the problem formulation stage, the characterization of risks, and the identification of the value of ERA information for risk management decisions. Decision analytical tools that would be useful for an integrative approach for decision modeling in ERAs are graphical modeling approaches as exemplified by Bayesian networks and influence diagrams. Recent advances in causal modeling and inferences with graphical modeling approaches have opened a pathway towards better integration of decision making, risk management, and risk assessment. Graphical models, such as results chains and Bayesian decision networks, are powerful tools for capturing and evaluating the information needed for managing risks. Moreover, incorporating ERAs into the steps of a formal decision analysis approach, one that examines the consequences and quality of candidate decisions, will help ensure that the technical data and analyses are focused on what is needed for examining trade-offs and making more informed decisions. Behind each ERA is a decision problem. Better correspondence between decision analysis and ERA will lead to improved risk assessments and information for managing environmental stressors.