WHY THE AUSTRALIAN GOVERNMENT DOES NOT USE DECISION SUPPORT SYSTEMS FOR TOBACCO CONTROL AMONG ADOLESCENTS

Authors

  • Sonja Petrovic-Lazarevic Monash University

Abstract

The paper points to the decision support systems' application in improving the Australian Government tobacco control decision processes relevant to adolescents. Most smokers are recruited as juveniles. Once started, there is a tendency for this age group to become addicted to it (Bachman et al., 1997; QUIT, 2006). The research project findings indicate that the Australian Government tobacco control decision process may be influenced by interested groups, is not dealing with tobacco control for adolescents and is not using decision support systems. If applied, however, the theoretical contributions of decision support systems can improve tobacco control decision processes to prevent juveniles becoming smokers and decrease the high death rate caused by smoking-related illnesses.

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References

Abraham, A., Petrovic-Lazarevic, S. and Coghill, K. (2006) EvoPol: A framework for optimising social regulation policies. Kybernetes, 35 (6) pp 814-824.

Bachman, J.G., Wadsworth, K. N., O'Malley, P.M., Lloyd, D. J. and Schulenberg, J.E. (1997) Smoking, Drinking and Drug Use in Young Adulthood. Lawrence Erlbaum Associates, Mahwah, NJ.

Bezdek, J.C., Dubois, D. and Prade, H. (1999) Fuzzy Sets in Approximate Reasoning and Information Systems. Kluwer Academic Publisher, Boston, IL.

Carlson, C., Fedrizzi, M. and Fuller, R. (2004) Fuzzy Logic in Management. Kluwer Academic Publishers, Boston, IL.

Coghill, K. and Petrovic-Lazarevic, S. (2002) Self-organisation of the community: Democratic republic of anarchic utopia. In Dimitrov, V. and Korotkich, V. (Eds) Fuzzy Logic: A Framework for the New Millennium. Springer-Verlag, New York, NY. 79-93.

Cordon, O. and Herrera, F. (1997) Evolutionary design of TSK fuzzy rule based systems using (μ , λ) evolution strategies. Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, Spain, pp 509-514.

Finlay, P.N. (1994) Introducing Decision Support Systems. Blackwell Publishers, Oxford.

Gugor, Z. and F. Arikan, F. (2000) A fuzzy outranking method in energy policy planning. Fuzzy Sets and Systems, 11 (1) pp 115-122.

Hosmer, H.H. (1992) Using fuzzy logic to represent security policies in the multi policy paradigm. ACM SIGSAC, 10 (44) pp 12-21.

Keen, P.G.W. (1980) Adaptive Design for Decision Support Systems (electronic resource). Association for Computing Machinery, Monash University Library.

Keen, P.G.W. and Morton, S.S. (1978) Decision Support Systems: An Organizational Perspective. Addison-Wesley, Reading, MA.

Kroneman, M.W. and Van der Zee. J. (1997) Health policy as a fuzzy concept: Methodological problems encountered when evaluating health policy reforms in an international perspective. Health Policy, 40 (2) pp 139-155.

Laudon, K.C. and Laudon, J. (2006) Management Information Systems. Prentice Hall, Upper Saddle River, NJ.

Nguyen, H.T. and Walker, E.A. (2000) A First Course in Fuzzy Logic. Chapman and Hall/CRC, London.

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Published

2007-06-30

How to Cite

Petrovic-Lazarevic, S. . (2007). WHY THE AUSTRALIAN GOVERNMENT DOES NOT USE DECISION SUPPORT SYSTEMS FOR TOBACCO CONTROL AMONG ADOLESCENTS. The Journal of Contemporary Issues in Business and Government, 13(1), 76–85. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/24