Combining Good Neighbor Agreements and Multimodel Analyses to Improve Decision-Support Modeling
AuthorMorales, David Eduardo
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PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractHydrogeologists commonly inform decision makers by predicting the impacts of proposed activities on future water resources. In many cases, they are hired by one stakeholder to perform analyses that are most relevant to their decisions. This leaves other stakeholders to make decisions based on a model that is less well suited, or even inappropriate, to make the predictions that are needed in their decision context. One approach to address this is to build an ensemble of models that spans the range of plausible conditions and that are developed to provide different stakeholder-relevant predictions. This ensures that all stakeholders can make better-informed decisions; but, it raises a difficult practical question. How can all stakeholders come to consensus decisions based on multiple, possibly conflicting models? One path forward may be through Good Neighbor Agreements (GNAs). With these agreements, stakeholders can agree upon a course of action, possibly based on one model or a combination of models, with legally binding caveats that account for the predictions of other models in the ensemble. This allows all parties to make decisions based on the best available science with contingencies to address uncertainties. GNAs are relatively new, but they have been applied in many decision contexts. My work utilizes a simplified groundwater-flow model within well-defined parameter bounds to generate an ensemble of models and associated outcomes. With these predictions, we explore how the decision-support process can be recast as an ensemble of contingent actions matched with a subset of plausible decision-critical concerns structured within a GNA.
Degree ProgramGraduate College