Which predictive uncertainty to resolve? Value of information sensitivity analysis for environmental decision models
Haag, Fridolin (Leibniz Center for Tropical Marine Research)
Miñarro, Sara (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
Chennu, Arjun (Leibniz Center for Tropical Marine Research)

Fecha:	2022
Resumen:	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 management 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.
Nota:	Unidad de excelencia María de Maeztu CEX2019-000940-M
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Agent-based modeling ; Coral reef management ; Multi-criteria decision analysis ; Sensitivity analysis ; Uncertainty
Publicado en:	Environmental Modelling and Software, Vol. 158 (December 2022) , art. 105552, ISSN 1364-8152

DOI: 10.1016/j.envsoft.2022.105552

15 p, 1.8 MB

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