A graphical model for risk analysis and management

Wang, X; Williams, MA

A graphical model for risk analysis and management

Wang, X

Williams, MA

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6291 LNAI pp. 256 - 269
Issue Date:: 2010-11-08

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Field	Value	Language
dc.contributor.author	Wang, X https://orcid.org/0000-0002-7459-1670	en_US
dc.contributor.author	Williams, MA https://orcid.org/0000-0002-1047-0503	en_US
dc.date.issued	2010-11-08	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6291 LNAI pp. 256 - 269	en_US
dc.identifier.isbn	3642152791	en_US
dc.identifier.isbn	9783642152795	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16175
dc.description.abstract	Risk analysis and management are important capabilities in intelligent information and knowledge systems. We present a new approach using directed graph based models for risk analysis and management. Our modelling approach is inspired by and builds on the two level approach of the Transferable Belief Model. The credal level for risk analysis and model construction uses beliefs in causal inference relations among the variables within a domain and a pignistic(betting) level for the decision making. The risk model at the credal level can be transformed into a probabilistic model through a pignistic transformation function. This paper focuses on model construction at the credal level. Our modelling approach captures expert knowledge in a formal and iterative fashion based on the Open World Assumption(OWA) in contrast to Bayesian Network based approaches for managing uncertainty associated with risks which assume all the domain knowledge and data have been captured before hand. As a result, our approach does not require complete knowledges and is well suited for modelling risk in dynamic changing environments where information and knowledge is gathered over time as decisions need to be taken. Its performance is related to the quality of the knowledge at hand at any given time. © 2010 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-15280-1_25	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A graphical model for risk analysis and management	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6291 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Belfast, Northern Ireland	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	6291 LNAI	en_US

Abstract:

Risk analysis and management are important capabilities in intelligent information and knowledge systems. We present a new approach using directed graph based models for risk analysis and management. Our modelling approach is inspired by and builds on the two level approach of the Transferable Belief Model. The credal level for risk analysis and model construction uses beliefs in causal inference relations among the variables within a domain and a pignistic(betting) level for the decision making. The risk model at the credal level can be transformed into a probabilistic model through a pignistic transformation function. This paper focuses on model construction at the credal level. Our modelling approach captures expert knowledge in a formal and iterative fashion based on the Open World Assumption(OWA) in contrast to Bayesian Network based approaches for managing uncertainty associated with risks which assume all the domain knowledge and data have been captured before hand. As a result, our approach does not require complete knowledges and is well suited for modelling risk in dynamic changing environments where information and knowledge is gathered over time as decisions need to be taken. Its performance is related to the quality of the knowledge at hand at any given time. © 2010 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/16175