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A Linguistic Approach to Structural Analysis in Prospective Studies

  • Pablo J. Villacorta
  • Antonio D. Masegosa
  • Dagoberto Castellanos
  • Maria T. Lamata
Part of the Communications in Computer and Information Science book series (CCIS, volume 297)

Abstract

One of the methodologies more used to accomplish prospective analysis is the scenario method. The first stage of this method is the so called structural analysis and aims to determine the most important variables of a system. Despite being widely used, structural analysis still presents some shortcomings, mainly due to the vagueness of the information used in this process. In this sense, the application of Soft Computing to structural analysis can contribute to reduce the impact of these problems by providing more interpretable and robust models. With this in mind, we present a methodology for structural analysis based on computing with words techniques to properly address vagueness and increase the interpretability. The method has been applied to a real problem with encouraging results.

Keywords

intelligent systems soft computing computing with words scenario method structural analysis 

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References

  1. 1.
    Arya, D.S., Abbasi, S.A.: Identification and classification of key variables and their role in environmental impact assessment: Methodology and software package intra. Environmental Monitoring and Assessment 72, 277–296 (2001)CrossRefGoogle Scholar
  2. 2.
    Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. Management Science Series B-Application 17(4), B141–B164 (1970)MathSciNetGoogle Scholar
  3. 3.
    Castellanos, D., Masegosa, A.D., Villacorta, P.J., Novoa, P., Pelta, D.: Improving scenario method for technology foresight by soft computing techniques. In: Proc. 4th Int. Seville Conference on Future-Oriented Technology Analysis (2011)Google Scholar
  4. 4.
    Duperrin, J.C., Godet, M.: Methode de hierarchisation des elements d un sisteme. Rapport Economique du CEA, R. pp. 45–41 (1973)Google Scholar
  5. 5.
    Garcia-Cascales, M.S., Lamata, M.T.: A modification to the index of Liou and Wang for ranking fuzzy number. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, 411–424 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Godet, M.: The art of scenarios and strategic planning: Tools and pitfalls. Technological Forecasting and Social Change 65(1), 3–22 (2000)CrossRefGoogle Scholar
  7. 7.
    Herrera, F., Alonso, S., Chiclana, F., Herrera-Viedma, E.: Computing with words in decision making: foundations, trends and prospects. Fuzzy Optimization and Decision Making 8(4), 337–364 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kacprzyk, J., Yager, R.R.: Linguistic summaries of data using fuzzy logic. International Journal of General Systems 30(2), 133–154 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Kanungo, S., Duda, S., Srinivas, Y.: A structured model for evaluating information systems effectiveness. Systems Research and Behavioral Science 16(6), 495–518 (1999)CrossRefGoogle Scholar
  10. 10.
    Kim, J.H., Barnett, G.A.: A structural analysis of international conflict: From a communication perspective. International Interactions 33(2), 135–165 (2007)CrossRefGoogle Scholar
  11. 11.
    Klir, G., Yuan, B.: Fuzzy sets and Fuzzy Logic. Prentice Hall (1995)Google Scholar
  12. 12.
    Qureshi, M.N., Dinesh Kumar, P.K.: An integrated model to identify and classify the key criteria and their role in the assessment of 3pl services providers. Asia Pacific Journal of Marketing and Logistics 20(2), 227–249 (2008)CrossRefGoogle Scholar
  13. 13.
    Sharma, H., Gupta, A.: Sushil: The objectives of waste management in india: A futures inquiry. Technological Forecasting and Social Change 48(3), 285–309 (1995)CrossRefGoogle Scholar
  14. 14.
    Villacorta, P.J., Masegosa, A.D., Castellanos, D., Novoa, P., Pelta, D.A.: Sensitivity analysis in the scenario method: a multi-objective approach. In: Proc. 11th Int. Conf. on Intelligent Systems Design and Applications, pp. 867–872 (2011)Google Scholar
  15. 15.
    Yager, R.: On the retranslation process in Zadeh’s paradigm of computing with words. IEEE Trans. on Systems, Man and Cybernetics, Part B 34(2), 1184–1195 (2004)CrossRefGoogle Scholar
  16. 16.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning - I. Information Sciences 8(3), 199–249 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Zadeh, L.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pablo J. Villacorta
    • 1
  • Antonio D. Masegosa
    • 1
  • Dagoberto Castellanos
    • 1
  • Maria T. Lamata
    • 1
  1. 1.Models of Decision and Optimization Research Group, Dept. of Computer Science and AI, CITICUniversity of GranadaGranadaSpain

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