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• W2969897221 endingPage "134026" @default.
• W2969897221 startingPage "134026" @default.
• W2969897221 abstract "Participatory modelling increases the transparency of environmental planning and management processes and enhances the mutual understanding among different parties. We present a sequential probabilistic approach to involve stakeholders' views in the formal decision support process. A continuous Bayesian Belief Network (BBN) model is used to estimate population parameters for stakeholder groups, based on samples of individual value judgements. The approach allows quantification and visualization of the variability in views among and within stakeholder groups. Discrete BBN is populated with these parameters, to summarize and visualize the information and to link it to a larger decision analytic influence diagram (ID). As part of ID, the resulting discrete BBN element serves as a distribution-form decision criteria in probabilistic evaluation of alternative management strategies, to help find a solution that represents the optimal compromise in the presence of potentially conflicting objectives. We demonstrate our idea using example data from the field of marine spatial planning. However, this approach is applicable to many types of management cases. We suggest that by advancing the mutual understanding and concrete participation this approach can further facilitate the stakeholder involvement also during the various stages of the environmental management process." @default.
• W2969897221 created "2019-08-29" @default.
• W2969897221 creator A5000344259 @default.
• W2969897221 creator A5011565096 @default.
• W2969897221 creator A5057163117 @default.
• W2969897221 creator A5065168320 @default.
• W2969897221 date "2019-12-01" @default.
• W2969897221 modified "2023-10-03" @default.
• W2969897221 title "Incorporating stakeholders' values into environmental decision support: A Bayesian Belief Network approach" @default.
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• W2969897221 doi "https://doi.org/10.1016/j.scitotenv.2019.134026" @default.
• W2969897221 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31476493" @default.
• W2969897221 hasPublicationYear "2019" @default.
• W2969897221 type Work @default.

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