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IMPLICATIONS OF USING ROBUST BAYESIAN ANALYSIS TO REPRESENT DIVERSE SOURCES OF UNCERTAINTY IN INTEGRATED ASSESSMENT
Impact/Purpose:
We have the following objectives for the proposed project:
- To develop practical methods for using robust Bayesian analysis to separate the various sources of uncertainty in environmental modeling.
- To clarify the ramifications of environmental model uncertainty for linked economic analyses, particularly the problem of discounting under uncertainty.
- To establish how Bayesian networks can be used to integrate environmental and economic models in the robust Bayesian setting.
Description:
In our previous research, we showed that robust Bayesian methods can be used in environmental modeling to define a set of probability distributions for key parameters that captures the effects of expert disagreement, ambiguity, or ignorance. This entire set can then be updated against data using Bayes’ theorem to investigate the degree to which aleatory and/or epistemic uncertainty are reduced through additional observations. Further work is required to clarify the methods of selecting the appropriate set definitions in real-world applications. Such work addresses the first objective of the proposed project. In parallel research, we have demonstrated that economic analyses performed under conditions of uncertainty require specific, and previously unrecognized, methods and rates for discounting future benefits. We plan for our second objective to lead to a delineation of the exact consequences of this result for integrated assessment modeling. Finally, we hypothesize that the above two outcomes will have significant implications for decision support. This will be tested using the integrated robust Bayesian network to evaluate policies according to both conventional and alternative decision criteria.