Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management

Leonelli, M. and Smith, J. Q. (2013) Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management. In: 2013 IEEE 29th International Conference on Data Engineering workshops (ICDEW 2013). IEEE: Piscataway, NJ, pp. 181-192. ISBN 9781467353038 (doi: 10.1109/ICDEW.2013.6547448)

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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 to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper 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. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Leonelli, Dr Manuele
Authors: Leonelli, M., and Smith, J. Q.
College/School:College of Science and Engineering > School of Mathematics and Statistics
Publisher:IEEE
ISBN:9781467353038
Copyright Holders:Copyright © 2013 IEEE
First Published:First published in 2013 IEEE 29th International Conference on Data Engineering workshops (ICDEW 2013): 181-192
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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