Combining morphological analysis and Bayesian networks for strategic decision support

Morphological analysis (MA) and Bayesian networks (BN) are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of...

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Main Authors: A de Waal, T Ritchey
Format: Article
Language:English
Published: Operations Research Society of South Africa (ORSSA) 2007-12-01
Series:ORiON
Online Access:http://orion.journals.ac.za/pub/article/view/51
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author A de Waal
T Ritchey
author_facet A de Waal
T Ritchey
author_sort A de Waal
collection DOAJ
description Morphological analysis (MA) and Bayesian networks (BN) are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of a qualitative and quantitative part. The qualitative part is a cause-and-effect, or causal graph. The quantitative part depicts the strength of the causal relationships between variables. Combining MA and BN, as two phases in a modelling process, allows us to gain the benefits of both of these methods. The strength of MA lies in defining, linking and internally evaluating the parameters of problem spaces and BN modelling allows for the definition and quantification of causal relationships between variables. Short summaries of MA and BN are provided in this paper, followed by discussions how these two computer aided methods may be combined to better facilitate modelling procedures. A simple example is presented, concerning a recent application in the field of environmental decision support.
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spelling doaj.art-ac86e672843d4654ba81104d0790eb582022-12-21T22:22:47ZengOperations Research Society of South Africa (ORSSA)ORiON2224-00042007-12-0123210.5784/23-2-5151Combining morphological analysis and Bayesian networks for strategic decision supportA de Waal0T Ritchey1Meraka Institute, CSIRRitchey ConsultingMorphological analysis (MA) and Bayesian networks (BN) are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of a qualitative and quantitative part. The qualitative part is a cause-and-effect, or causal graph. The quantitative part depicts the strength of the causal relationships between variables. Combining MA and BN, as two phases in a modelling process, allows us to gain the benefits of both of these methods. The strength of MA lies in defining, linking and internally evaluating the parameters of problem spaces and BN modelling allows for the definition and quantification of causal relationships between variables. Short summaries of MA and BN are provided in this paper, followed by discussions how these two computer aided methods may be combined to better facilitate modelling procedures. A simple example is presented, concerning a recent application in the field of environmental decision support.http://orion.journals.ac.za/pub/article/view/51
spellingShingle A de Waal
T Ritchey
Combining morphological analysis and Bayesian networks for strategic decision support
ORiON
title Combining morphological analysis and Bayesian networks for strategic decision support
title_full Combining morphological analysis and Bayesian networks for strategic decision support
title_fullStr Combining morphological analysis and Bayesian networks for strategic decision support
title_full_unstemmed Combining morphological analysis and Bayesian networks for strategic decision support
title_short Combining morphological analysis and Bayesian networks for strategic decision support
title_sort combining morphological analysis and bayesian networks for strategic decision support
url http://orion.journals.ac.za/pub/article/view/51
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AT tritchey combiningmorphologicalanalysisandbayesiannetworksforstrategicdecisionsupport