| Abstract: | Sensitivity analysis provides a way to mitigate traditional criticisms of Bayesian statistical decision theory, concerning dependence on subjective inputs. We suggest a general framework for sensitivity analysis allowing for perturbations in both the utility function and the prior distribution. Perturbations are constrained to classes modelling imprecision in judgements The framework discards first definitely bad alternatives; then, identifies alternatives that may share optimality with a current one; and, finally, detects least changes in the inputs leading to changes in ranking. The associated computational problems and their implementation are discussed. |
