Semantic De-biased Associations (SDA) model to improve ill-structured decision support

Memon, T; Lu, J; Hussain, FK

Semantic De-biased Associations (SDA) model to improve ill-structured decision support

Memon, T Lu, J

Hussain, FK

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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), 2012, 7664 LNCS (PART 2), pp. 483 - 490
Issue Date:: 2012-11-19

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Field	Value	Language
dc.contributor.author	Memon, T	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.date.issued	2012-11-19	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7664 LNCS (PART 2), pp. 483 - 490	en_US
dc.identifier.isbn	9783642344800	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31672
dc.description.abstract	Decision makers are subject to rely upon their biased mental models to solve ill-structured decision problems. While mental models prove to be very helpful in understanding and solving ill-structured problems, the inherent biases often lead to poor decision making. This study deals with the issue of biases by proposing Semantic De-biased Associations (SDA) model. SDA model assists user to make more informed decisions by providing de-biased, and validated domain knowledge. It employs techniques to mitigate biases from mental models; and incorporates semantics to automate the integration of mental models. The effectiveness of SDA model in solving ill-structured decision problems is illustrated in this paper through a case study. © 2012 Springer-Verlag.	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	10.1007/978-3-642-34481-7_59	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Semantic De-biased Associations (SDA) model to improve ill-structured decision support	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	7664 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Doha, Qatar
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.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	7664 LNCS	en_US

Abstract:

Decision makers are subject to rely upon their biased mental models to solve ill-structured decision problems. While mental models prove to be very helpful in understanding and solving ill-structured problems, the inherent biases often lead to poor decision making. This study deals with the issue of biases by proposing Semantic De-biased Associations (SDA) model. SDA model assists user to make more informed decisions by providing de-biased, and validated domain knowledge. It employs techniques to mitigate biases from mental models; and incorporates semantics to automate the integration of mental models. The effectiveness of SDA model in solving ill-structured decision problems is illustrated in this paper through a case study. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/31672