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

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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.
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