Environmental models are important decision support tools to understand the implications of policy actions. Because of their power to shape preferred policy directions, it's useful to consider how the modeling process can be improved by increasing transparency and formalizing the role of professional judgments in models. Bayesian reasoning - asking what model output we ought to expect as a function of the possible realities - is rarely applied to the interpretation of the output from mechanistic models. Specifically, our framework treats the model as an evidence-generating process from which the analyst considers the joint relationship between reality and model output. This process yields a likelihood function that (i) serves to guide what questions should be asked in the model building and validation process and (ii) how insights should be updated given prior information and model outputs. To illustrate the approach, we discuss physical climate models and integrated assessment models. We argue that such improvements could accelerate learning about model structure and learning from model outputs.
Dr. Melissa A. Kenney is an Associate Research Professor in Environmental Decision Analysis and Indicators at the University of Maryland (indicators.umd.edu). Her research broadly addresses how to integrate both scientific knowledge and societal values into policy decision-making under uncertainty. Her research expertise includes conceptual modeling and decision structuring, indicators, systems analysis, multi-attribute methods, and evaluation of decision support to address environmental policy decisions. These methods have been applied to a range of topics including participatory global change indicators, setting environmental policy criteria, economic analyses for restoration alternatives assessment, expert elicitation, and value of information of indicators. Dr. Kenney was an AAAS Leshner Leadership Institute Public Engagement Fellow, focusing on stakeholder engaged research to create climate-resilient solutions in the U.S. and Chesapeake Bay region. She earned a Ph.D. from Duke University, focusing on water quality modeling and decision analysis.