Stochastic Sensitivity Analysis Using Fuzzy Influence Diagrams
This work addresses the need for sensitivity analysis in decision-making processes, particularly in domains like diagnostic decision-making, but it appears incremental as it builds on existing influence diagram methods.
The paper tackles the problem of performing stochastic sensitivity analysis in probabilistic inference and decision problems by proposing a method using Bayesian fuzzy probabilities within influence diagrams, which avoids the need to resolve problems for varying probability estimates and provides additional interval information beyond classical point estimates.
The practice of stochastic sensitivity analysis described in the decision analysis literature is a testimonial to the need for considering deviations from precise point estimates of uncertainty. We propose the use of Bayesian fuzzy probabilities within an influence diagram computational scheme for performing sensitivity analysis during the solution of probabilistic inference and decision problems. Unlike other parametric approaches, the proposed scheme does not require resolving the problem for the varying probability point estimates. We claim that the solution to fuzzy influence diagrams provides as much information as the classical point estimate approach plus additional information concerning stochastic sensitivity. An example based on diagnostic decision making in microcomputer assembly is used to illustrate this idea. We claim that the solution to fuzzy influence diagrams provides as much information as the classical point estimate approach plus additional interval information that is useful for stochastic sensitivity analysis.