Uncertain decision information processing in warning systems under group decision making framework

Ma, J

Uncertain decision information processing in warning systems under group decision making framework

Ma, J

Permalink

Publication Type:: Thesis
Issue Date:: 2011

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (154.92 kB)

Adobe PDF

Download thesisAdobe PDF (1.21 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ma, J
dc.date.accessioned	2012-03-06T00:34:38Z
dc.date.accessioned	2012-12-15T03:53:32Z
dc.date.available	2012-03-06T00:34:38Z
dc.date.available	2012-12-15T03:53:32Z
dc.date.issued	2011
dc.identifier.uri	http://hdl.handle.net/10453/20361
dc.description	University of Technology, Sydney. Faculty of Engineering and Information Technology.
dc.description.abstract	Human factors affect the development and deployment of an effective peoplecentred warning system. The study of decision information processing in a complex and dynamic decision environment can be used to handle human factors efficiently. Taking note that group decision-making is an effective processing strategy when people make decisions in a complex and dynamic decision environment, this thesis studies four aspects of decision information processing within a group decisionmaking framework. The four processing aspects include 1) detecting decision information inconsistency; 2) integrating decision information; 3) predicting risk using decision information; and 4) measuring decision information similarity. Focusing on the above four processing aspects, the thesis: (1) Presents a rule-map technique and establishes a rule-map-based information inconsistency detection method for data inconsistency; presents a state-based domain knowledge representation technique and establishes a detection method for logical inconsistency based on this; (2) Presents an extended physical model as an information integration framework and establishes an information integration method based on this; (3) Presents a vector aggregation operator based on a complex fuzzy set framework and establishes an information prediction method for decision information with multiple periodic features; (4) Presents a graduate aggregation operator and establishes a measuring method for similarities among decision information. PHD Thesis, UTS v The thesis illustrates a decision support system prototype of decision information processing in group decision-making. Experiments indicate that the presented techniques and methods can effectively support dynamic decision information processing in a complex decision environment.	en
dc.format	Thesis (PhD)	en
dc.language.iso	en	en
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/20361/2/02 Whole.pdf
dc.relation.replaces	http://hdl.handle.net/2100/1294
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Group decision making.	en
dc.subject	Risk.	en
dc.subject	Uncertainty.	en
dc.title	Uncertain decision information processing in warning systems under group decision making framework	en
dc.type	Thesis
utslib.copyright.status	open_access

Abstract:

Human factors affect the development and deployment of an effective peoplecentred warning system. The study of decision information processing in a complex and dynamic decision environment can be used to handle human factors efficiently. Taking note that group decision-making is an effective processing strategy when people make decisions in a complex and dynamic decision environment, this thesis studies four aspects of decision information processing within a group decisionmaking framework. The four processing aspects include 1) detecting decision information inconsistency; 2) integrating decision information; 3) predicting risk using decision information; and 4) measuring decision information similarity. Focusing on the above four processing aspects, the thesis: (1) Presents a rule-map technique and establishes a rule-map-based information inconsistency detection method for data inconsistency; presents a state-based domain knowledge representation technique and establishes a detection method for logical inconsistency based on this; (2) Presents an extended physical model as an information integration framework and establishes an information integration method based on this; (3) Presents a vector aggregation operator based on a complex fuzzy set framework and establishes an information prediction method for decision information with multiple periodic features; (4) Presents a graduate aggregation operator and establishes a measuring method for similarities among decision information. PHD Thesis, UTS v The thesis illustrates a decision support system prototype of decision information processing in group decision-making. Experiments indicate that the presented techniques and methods can effectively support dynamic decision information processing in a complex decision environment.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/20361