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

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Haag, Fridolin ORCID: https://orcid.org/0000-0002-3492-8793, Miñarro, Sara ORCID: https://orcid.org/0000-0001-8243-8652 and Chennu, Arjun ORCID: https://orcid.org/0000-0002-0389-5589 (2022) Which predictive uncertainty to resolve? Value of information sensitivity analysis for environmental decision models. Environmental Modelling & Software, 158 . p. 105552. DOI https://doi.org/10.1016/j.envsoft.2022.105552.

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Abstract

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.

Document Type: Article
Programme Area: PA4
Research affiliation: Integrated Modelling > Data Science and Technology
Refereed: Yes
Open Access Journal?: No
DOI: https://doi.org/10.1016/j.envsoft.2022.105552
ISSN: 13648152
Date Deposited: 01 Nov 2022 15:10
Last Modified: 26 Mar 2024 13:31
URI: http://cris.leibniz-zmt.de/id/eprint/5045

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