Team situation awareness measure using semantic utility functions for supporting dynamic decision-making
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Team decision-making is a remarkable feature in a complex dynamic decision environment, which can be supported by team situation awareness. In this paper, a team situation awareness measure (TSAM) method using a semantic utility function is proposed. The semantic utility function is used to clarify the semantics of qualitative information expressed in linguistic terms. The individual and team situation awareness are treated as linguistic possibility distributions on the potential decisions in a dynamic decision environment. In the TSAM method, team situation awareness is generated through reasoning and aggregating individual situation awareness based on a multi-level hierarchy mental model of the team. Individual and team mental models are composed of key drivers and significant variables. An illustrative example in telecoms customer churn prediction is given to explain the effectiveness and the main steps of the TSAM method.
KeywordsDynamic decision-making Situation awareness measurement Utility function Qualitative information
The work presented in this paper was supported by Australian Research Council (ARC) under Discovery Project DP0559213 and DP0880739. The authors sincerely appreciate the advice and suggestions of Professor Da Ruan from the Belgian Nuclear Research Center (SCK CEN).
- Berson A, Smith S, Thearling K (2000) Building data mining applications for CRM. McGraw-Hill, New YorkGoogle Scholar
- Endsley MR, Garland DJ (2000) Situation awareness analysis and measurement. Lawrence Erlbaum, MahwahGoogle Scholar
- Gopal RK, Meher SK (2008) Customer churn time prediction in mobile telecommunication industry using ordinal regression. In: PAKDD2008, Lecture Notes in Artificial Intelligence, vol 5012, Springer, Berlin, pp 884–889Google Scholar
- Grabisch M, Labreuche C (2005) Fuzzy measures and integrals in MCDA. In: Figueira J, Greco S, Ehrgott M (eds) Multiple criteria decision analysis: state of the art surveys, international series in operations research and management science, chap 14, vol 78. Springer, New York, pp 563–604Google Scholar
- Kon M (2004) Stop customer churn before it starts. Harv Manag Update 9(7):3–5Google Scholar
- Lawry J (2008) An overview of computing with words using label semantics. In: Bustince H, Herrera F, Montero J (eds) Fuzzy sets and their extensions: representation, aggregation and models. Springer, Berlin, pp 65–87Google Scholar
- Lu J, Zhang G, Ruan D, Wu F (2007) Multi-objective group decision making—methods, software and applications with fuzzy set technology. Imperial College Press, LondonGoogle Scholar
- McMarley JS, Wickens CD, Goh J, Horrey WJ (2002) A computational model of attention/situation awareness. In: Proceedings of the 46th annual meeting of the human factors and ergonomic society. Human Factors and Ergonomics Society, Santa MonicaGoogle Scholar
- Uhlarik J, Comerford DA (2002) A review of situation awareness literature relevant to pilot surveillance functions. Technical Report DOT/FAA/AM-02/3, Department of Psychology, Kansas State University, Manhattan, KS, USAGoogle Scholar