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)


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  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