Which predictive uncertainty to resolve? Value of information sensitivity analysis for environmental decision models

Uncertainties in environmental decisions are large, but resolving them is costly. We provide a framework for value of information (VoI) analysis to identify key predictive uncertainties in a decision model. The approach addresses characteristics that complicate this analysis in environmental management: dependencies in the probability distributions of predictions, trade-offs between multiple objectives, and divergent stakeholder perspectives. For a coral reef fisheries case, we predict ecosystem and fisheries trajectories given different man-agement alternatives with an agent-based model. We evaluate the uncertain predictions with preference models based on utility theory to find optimal alternatives for stakeholders. Using the expected value of partially perfect information (EVPPI), we measure how relevant resolving uncertainty for various decision attributes is. The VoI depends on the stakeholder preferences, but not directly on the width of an attribute's probability distribution. Our approach helps reduce costs in structured decision-making processes by prioritizing data collection efforts.

Automated summary

The study provides a framework for value of information (VoI) analysis to identify key predictive uncertainties in environmental decision models, resolving complexities such as dependencies, trade-offs, and stakeholder perspectives. The approach evaluates uncertain predictions through preference models based on utility theory to find optimal alternatives for stakeholders.