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M Leonelli and JQ Smith

Using graphical modeling and multi-attribute utility theory for uncertainty handling in large systems

Abstract: Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help reducing these uncertainties, but it is critical to do this in an efficient and defensible way. The paper, after briefly introducing the structure of a typical DSS for nuclear emergencies, sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. But unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit in this paper. The technology we describe here provides a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, but nevertheless provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy.