Bayesian decision support for complex systems with many distributed experts

Leonelli, M. and Smith, J. Q. (2015) Bayesian decision support for complex systems with many distributed experts. Annals of Operations Research, 235(1), pp. 517-542. (doi: 10.1007/s10479-015-1957-7)

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Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems.

Item Type:Articles
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 > Statistics
Journal Name:Annals of Operations Research
ISSN (Online):1572-9338
Published Online:12 August 2015
Copyright Holders:Copyright © 2015 Springer Science+Business Media New York
First Published:First published in Annals of Operations Research 235(1):517-542
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
Data DOI:10.1007/s10479-015-1957-7

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