Representing uncertainty in silvicultural decisions: An application of the Dempster-Shafer theory of evidence.



Forest management decisions often must be made using sparse data and expert judgment. The representation of this knowledge in traditional approaches to decision analysis implies a precise value for probabilities or, in the case of Bayesian analysis, a precisely specified joint distribution for unknown parameters. The precision of this specification does not depend on the strength or weakness of the evidence on which it is based. This often leads to exaggerated precision in the results of decision analyses, and obscures the importance of imperfect information. Here, I suggest an alternative based on the Dempster-Shafer theory of evidence, which differs from conventional approaches in allowing the allocation of belief to subsets of the possible outcomes, or, in the case of a continuous set of possibilities, to intervals. The Dempster-Shafer theory incorporates Bayesian analysis as a special case; a critical difference lies in the representation of ignorance or uncertainty. I present examples of silvicultural decision-making using belief functions for the case of no data, sparse data, and adaptive management under increasing data availability. An approach based on the Dempster-Shafer principles can yield not only indications of optimal policies, but also valuable information about the level of certainty in decision-making.


Natural Resources and the Environment

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Forest Ecology and Management



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