Bayesian decision support for complex systems with many distributed experts

doi:10.1007/s10479-015-1957-7

2015-08-12

Manuele Leonelli *

James Q. Smith

DOI：10.1007/s10479-015-1957-7

原文链接

原文求助

摘要

En 中文

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.

Keyword:

Bayesian decision theory

Combination of expert judgement

Decision support systems

Graphical models

Uncertainty handling

期刊

Annals of Operations Research

IF:

4.5

论文数:

8.1K

被引数:

2.1W

机构

University of Warwick

学者数:

2.2W

论文数: 2.2W

被引数: 85

引用论文

The chemosensitivity to therapy of childhood early B acute lymphoblastic leukemia could be determined by the combined expression of CD34, SPI-B and BCR genes

Leukemia Research

IF0

2006-06-01

PREAI

Leila Talby; Hervé Chambost; Marie-Christine Roubaud; Catherine N’Guyen; Michèle Milili; Béatrice Loriod; Chantal Fossat; Christophe Picard; Jean Gabert; Pierre Chiappetta; Gérard Michel; Claudine Schiff

INFLUENCE DIAGRAMS FOR BAYESIAN DECISION-ANALYSIS

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

IF6

1989-06-01

PREAI

SMITH, JQ

Clear cell renal cell carcinoma with a syncytial-type multinucleated giant tumor cell component: implications for differential diagnosis

Human Pathology

IF0

2014-04-01

PREAI

Sean R. Williamson; Jennifer B. Kum; Michael P. Goheen; Liang Cheng; David J. Grignon; Muhammad T. Idrees