This Note is about assessing the uncertainty that arises in the modeling step of statistical analyses and propagating that uncertainty through to the final inferences drawn or decisions made. It contains the project description section of a proposal to the Decision, Risk, and Management Sciences Program at the National Science Foundation. The authors advocate a Bayesian methodology for assessment and propagation of model uncertainty, and also discuss frequentist alternatives. Successful research of the type proposed will provide new general-purpose tools for decisionmaking that will improve the assessment of how much hedging against uncertainty should be built in.
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