Advertisement

Using Belief Degree Distributed Fuzzy Cognitive Maps for Energy Policy Evaluation

  • Lusine Mkrtchyan
  • Da Ruan
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 33)

Abstract

Cognitive maps (CMs) were initially for graphical representation of uncertain causal reasoning. Later Kosko suggested Fuzzy Cognitive Maps (FCMs) in which users freely express their opinions in linguistic terms instead of crisp numbers. However, it is not always easy to assign some linguistic term to a causal link. In this paper we suggest a new type of CMs namely, Belief Degree-Distributed FCMs (BDD-FCMs) in which causal links are expressed by belief structures which enable getting the links’ evaluations with distributions over the linguistic terms. We propose a general framework to construct BDD-FCMs by directly using belief structures or other types of structures such as interval values, linguistic terms, or crisp numbers. The proposed framework provides a more flexible tool for causal reasoning as it handles any kind of structures to evaluate causal links. We propose an algorithm to find a similarity between experts judgments by BDD-FCMs for a case study in Energy Policy evaluation.

Keywords

Adjacency Matrix Causal Link Energy Policy Linguistic Term Adjacency Matrice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Axelrod, R.: Structure of Decision: the Cognitive Maps of Political Elites, Princeton (1976)Google Scholar
  2. 2.
    Pelaez, C.E., Bowles, J.B.: Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Information Sciences 88, 177–199 (1996)CrossRefGoogle Scholar
  3. 3.
    Siraj, A., et al.: Fuzzy cognitive maps for decision support in an iintelligent intrusion detection system. In: Proceedings of IFSA/NAFIPS Conference on Soft Computing, pp. 173–189. MIT Press (2001)Google Scholar
  4. 4.
    Styblinski, M.A., Meyer, B.D.: Fuzzy cognitive maps, signal flow graphs, and qualitative circuit analysis. In: Proceedings of the 2nd IEEE Int. Conf. on Neural Networks, pp. 549–556 (1988)Google Scholar
  5. 5.
    Ozesmi, U., Ozesmi, S.L.: Ecological model based on people’s knowledge: a multi-step cognitive mapping approach. Ecological Modelling 176, 43–64 (2004)CrossRefGoogle Scholar
  6. 6.
    Hobbsand, B.F., et al.: Fuzzy cognitive mapping as a tool to define mnagement objectives for complex ecosystems. Ecological Applications 12, 1548–1565 (2002)CrossRefGoogle Scholar
  7. 7.
    Radomski, P.J., Goeman, P.J.: Decision making and modeling in freshwater sport-fisheries management. Fisheries 21, 14–21 (1996)CrossRefGoogle Scholar
  8. 8.
    Kardaras, D., Karakostas, B.: The use of fuzzy cognitive maps to simulate the information systems strategic planning process. Information and Software Technology 41, 197–210 (1999)CrossRefGoogle Scholar
  9. 9.
    Kardaras, D., Mentzas, G.: Using fuzzy cognitive maps to model and analyse business performance assessment. Advances in Industrial Engineering Applications and Practice 2, 63–68 (1997)Google Scholar
  10. 10.
    Lee, S., Han, I.: Fuzzy cognitive map for the design of edi controls. Information and Management 37, 37–50 (2000)CrossRefGoogle Scholar
  11. 11.
    Hong, T., Han, I.: Knowledge-based data mining of news information on the internet using cognitive maps and neural networks. Expert Systems with Applications 23, 1–8 (2002)CrossRefGoogle Scholar
  12. 12.
    Lazzerini, B., Mkrtchyan, L.: Risk analysis using extended fuzzy cognitive maps. In: 2010 International Conference on Intelligent Computing and Cognitive Informatics (ICICCI), Kuala Lumpur, June 22-23, pp. 179–182 (2010), http://dx.doi.org/10.1109/ICICCI.2010.105, doi:10.1109/ICICCI.2010.105, ISBN: 978-1-4244-6640-5
  13. 13.
    Smith, E., Eloff, J.: Cognitive fuzzy modeling for enhanced risk assessment in a health care institution. IEEE Intelligent Systems, 69–75 (2002)Google Scholar
  14. 14.
    Jasinevicius, R., Petrauskas, V.: Fuzzy expert maps for risk management systems. In: IEEE/OES US/EU-Baltic International Symposium, pp. 1–4 (2008)Google Scholar
  15. 15.
    Dickerson, J.A., Kosko, B.: Virtual worlds as fuzzy cognitive maps. Presence, 173–189 (1994)Google Scholar
  16. 16.
    Papageorgiou, E., Groumpos, P.: A Weight Adaptation Method for Fuzzy Cognitive Maps to a Process Control Problem. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3037, pp. 515–522. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Markoczy, L., Goldberg, J.: A method for eliciting and comparing causal maps. Journal of Management (2), 305–333 (1995)CrossRefGoogle Scholar
  18. 18.
    Zhang, W., Chen, S.: A logical architecture for cognitive maps. In: Proceedings of the 2nd Int. Conf. on Neural Networks, pp. 231–238 (1988)Google Scholar
  19. 19.
    Kosko, B.: Fuzzy cognitive maps. International Journal on Man-Machine 24(1), 65–75 (1996)CrossRefGoogle Scholar
  20. 20.
    Ozesmi, U., Ozesmi, S.L.: Automatic construction of fcms. Ecological Modelling 176, 43–64 (2004)CrossRefGoogle Scholar
  21. 21.
    Schneider, M., et al.: Automatic construction of fcms. Fuzzy Sets Syst. 93, 161–172 (1998)CrossRefGoogle Scholar
  22. 22.
    Kabak, O., Ruan, D.: A cumulative belief-degree approach for nuclear safeguards evaluation. IEEE Transactions on Knowledge and Data Management (in Press) (2010)Google Scholar
  23. 23.
    Kandasamy, W.B.V., Smarandache, F.: Fuzzy cognitive maps and neutrosophic cognitive maps. Phoenix (2003)Google Scholar
  24. 24.
    Langfield-Smith, K., Wirth, A.: Measuring differences between cognitive maps. The Journal of the Operational Research Society 43(12), 1135–1150 (1992)Google Scholar
  25. 25.
    Munda, G.: A conflict analysis approach for ulluminating distributional issues in sustainability policy. European Journal of Operational Research (1), 307–322 (2009)CrossRefGoogle Scholar
  26. 26.
    Ruan, D., et al.: Multi-criteria group decision support with linguistic variables in long-term scenarios for belgian energy policy. Journal of Universal Computer Science 15(1), 103–120 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lusine Mkrtchyan
    • 1
  • Da Ruan
    • 2
  1. 1.IMT Lucca Institute for Advanced StudiesLuccaItaly
  2. 2.Belgian Nuclear Research Centre (SCK∙CEN)MolBelgium

Personalised recommendations