Advertisement

Semantic De-biased Associations (SDA) Model to Improve Ill-Structured Decision Support

  • Tasneem Memon
  • Jie Lu
  • Farookh Khadeer Hussain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)

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.

Keywords

Cognitive DSS cognitive biases ill-structured decision support semantic mental model representation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Khatri, N., Ng, H.A.: The role of intuition in strategic decision making. Human Relations 53, 57–86 (2000)CrossRefGoogle Scholar
  2. 2.
    Nemati, H.R., Steiger, D.M., Iyer, L.S., Herschel, R.T.: Knowledge warehouse: An architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems 33, 143–161 (2002)CrossRefGoogle Scholar
  3. 3.
    Schwenk, C.R.: The cognitive perspective on strategic decision making. Journal of Management Studies 25, 41–55 (1988)CrossRefGoogle Scholar
  4. 4.
    Chen, J.Q., Lee, S.M.: An exploratory cognitive dss for strategic decision making. Decision Support Systems 36, 147–160 (2003)CrossRefGoogle Scholar
  5. 5.
    Niu, L., Lu, J., Zhang, G.: Cognition-Driven Decision Support for Business Intelligence. SCI, vol. 238. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Johnson-Laird, P.N.: Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Harvard University Press, Cambridge (1983)Google Scholar
  7. 7.
    Kahneman, D., Tversky, A.: Subjective probability: A judgment of representativeness. Cognitive Psychology 3, 430–454 (1972)CrossRefGoogle Scholar
  8. 8.
    Mintzberg, H.: The Nature of Managerial Work. Harpercollins College Div., New York (1973)Google Scholar
  9. 9.
    Korte, R.F.: Biases in decision making and implications for human resource development. Advances in Developing Human Resources 5, 440–457 (2003)CrossRefGoogle Scholar
  10. 10.
    Bhandari, G., Hassanein, K., Deaves, R.: Debiasing investors with decision support systems: An experimental investigation. Decis. Support Syst. 46, 399–410 (2008)CrossRefGoogle Scholar
  11. 11.
    Lucchiari, C., Pravettoni, G.: Cognitive balanced model: A conceptual scheme of diagnostic decision making. Journal of Evaluation in Clinical Practice 18, 82–88 (2012)CrossRefGoogle Scholar
  12. 12.
    Ram, S., Carlson, D.A.: Hyperintelligence: The next frontier. Commun. ACM 33, 311–321 (1990)CrossRefGoogle Scholar
  13. 13.
    Yadav, S.B., Khazanchi, D.: Subjective understanding in strategic decision making: An information systems perspective. Decision Support Systems 8, 55–71 (1992)CrossRefGoogle Scholar
  14. 14.
    Nutt, P.C.: Why Decisions Fail: Avoiding the Blunders and Traps that Lead to Debacles, 1st edn. Berrett-Koehler Publishers (2002)Google Scholar
  15. 15.
    Maqsood, T., Finegan, A.D., Walker, D.H.T.: Biases and heuristics in judgment and decision making: The dark side of tacit knowledge. Issues in Informing Science and Information Technology 1, 295–301 (2004)Google Scholar
  16. 16.
    Ubel, P.A., Smith, D.M., Zikmund-Fisher, B.J., Derry, H.A., McClure, J., Stark, A., Wiese, C., Greene, S., Jankovic, A., Fagerlin, A.: Testing whether decision aids introduce cognitive biases: Results of a randomized trial. Patient Education and Counseling 80, 158–163 (2010)CrossRefGoogle Scholar
  17. 17.
    Tversky, A., Kahneman, D.: The framing of decisions and the psychology of choice. Science 211, 453–458 (1981)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Hicks, E.P., Kluemper, G.T.: Heuristic reasoning and cognitive biases. American Journal of Orthodontics and Dentofacial Orthopedics 139, 297–304 (2011)CrossRefGoogle Scholar
  19. 19.
    Skitmore, R.M., Stradling, S.G., Tuohy, A.P.: Project management under uncertainty. Construction Management and Economics 7, 103–113 (1989)CrossRefGoogle Scholar
  20. 20.
    Rentsch, J.R., Klimoski, R.J.: Why do great minds think alike?: Antecedents of team member schema agreement. Journal of Organizational Behavior 22, 107–120 (2001)CrossRefGoogle Scholar
  21. 21.
    Kahneman, D., Lovallo, D., Sibony, O.: The big idea: Before you make that big decision. Harvard Business Review, 50–60 (2011)Google Scholar
  22. 22.
    Hodgkinson, G.P., Bown, N.J., Maule, A.J., Glaister, K.W., Pearman, A.D.: Breaking the frame: an analysis of strategic cognition and decision making under uncertainty. Strategic Management Journal 20, 977–985 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tasneem Memon
    • 1
  • Jie Lu
    • 1
  • Farookh Khadeer Hussain
    • 1
  1. 1.Decision Systems and e-Service Intelligence Laboratory(DeSI), Centre for Quantum Computation and Intelligent Systems(QCIS)University of Technology SydneyAustralia

Personalised recommendations