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Generalising Social Structure Using Interval Type-2 Fuzzy Sets

  • Christopher K. FrantzEmail author
  • Bastin Tony Roy Savarimuthu
  • Martin K. Purvis
  • Mariusz Nowostawski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9862)

Abstract

To understand the operation of the informal social sphere in human or artificial societies, we need to be able to identify their existing behavioural conventions (institutions). This includes the contextualisation of seemingly objective facts with subjective assessments, especially when attempting to capture their meaning in the context of the analysed society. An example for this is numeric information that abstractly expresses attributes such as wealth, but only gains meaning in its societal context. In this work we present a conceptual approach that combines clustering techniques and Interval Type-2 Fuzzy Sets to extract structural information from aggregated subjective micro-level observations. A central objective, beyond the aggregation of information, is to facilitate the analysis on multiple levels of social organisation. We introduce the proposed mechanism and discuss its application potential.

Keywords

Membership Function Interval Centre Lower Membership Function Input Interval Social Cluster 
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.

References

  1. 1.
    Acharya, T., Ray, A.K.: Fuzzy set theory in image processing. In: Image Processing: Principles and Applications, pp. 209–226. Wiley, Hoboken (2005)Google Scholar
  2. 2.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U. (eds.) Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press, Portland (1996)Google Scholar
  3. 3.
    Festinger, L.: A theory of social comparison processes. Hum. Relat. 7(2), 117–140 (1954)CrossRefGoogle Scholar
  4. 4.
    Frantz, C., Purvis, M.K., Savarimuthu, B.T.R., Nowostawski, M.: Analysing the dynamics of norm evolution using interval type-2 fuzzy sets. In: WI-IAT 2014 Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 230–237 (2014)Google Scholar
  5. 5.
    Frantz, C.K., Purvis, M.K., Savarimuthu, B.T.R., Nowostawski, M.: Modelling dynamic normative understanding in agent societies. Scalable Comput.: Pract. Experience 16(4), 355–378 (2015)Google Scholar
  6. 6.
    Greenwald, A.G., Banaji, M.R., Rudman, L.A., Farnham, S.D., Nosek, B.A., Mellott, D.S.: A unified theory of implicit attitudes, stereotypes, self-esteem, and self-concept. Psychol. Rev. 109(1), 3–25 (2002)CrossRefGoogle Scholar
  7. 7.
    Hassan, S., Salgado, M., Pavón, J.: Friendship dynamics: modelling social relationships through a fuzzy agent-based simulation. Discrete Dyn. Nat. Soc. 2011, Article ID 765640, 19 p (2011)Google Scholar
  8. 8.
    Liu, F., Mendel, J.M.: Encoding words into interval Type-2 fuzzy sets using an interval approach. IEEE Trans. Fuzzy Syst. 16(6), 1503–1521 (2008)CrossRefGoogle Scholar
  9. 9.
    Long, Z., Yuanc, Y., Long, W.: Designing fuzzy controllers with variable universes of discourse using input-output data. Eng. Appl. Artif. Intell. 36, 215–221 (2014)CrossRefGoogle Scholar
  10. 10.
    Mendel, J., John, R., Liu, F.: Interval Type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Syst. 14(6), 808–821 (2006)CrossRefGoogle Scholar
  11. 11.
    Morales, J., López-Sánchez, M., Rodriguez-Aguilar, J.A., Vasconcelos, W., Wooldridge, M.: Online automated synthesis of compact normative systems. ACM Trans. Auton. Adapt. Syst. 10(1), 2:1–2:33 (2015)CrossRefGoogle Scholar
  12. 12.
    North, D.C.: Institutions, Institutional Change, and Economic Performance. Cambridge University Press, New York (1990)CrossRefGoogle Scholar
  13. 13.
    Ören, T., Ghasem-Aghaee, N.: Personality representation processable in fuzzy logic for human behavior simulation. In: Proceedings of the 2003 Summer Computer Simulation Conference, Montreal, Canada, July 20–24, pp. 11–18. SCS, San Diego (2003)Google Scholar
  14. 14.
    Riveret, R., Artikis, A., Busquets, D., Pitt, J.: Self-governance by transfiguration: from learning to prescriptions. In: Cariani, F., Grossi, D., Meheus, J., Parent, X. (eds.) DEON 2014. LNCS, vol. 8554, pp. 177–191. Springer, Heidelberg (2014)Google Scholar
  15. 15.
    Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69(1), 99–118 (1955)CrossRefGoogle Scholar
  16. 16.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning - I. Inf. Sci. 8(3), 199–249 (1975)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christopher K. Frantz
    • 1
    Email author
  • Bastin Tony Roy Savarimuthu
    • 2
  • Martin K. Purvis
    • 2
  • Mariusz Nowostawski
    • 3
  1. 1.College of Enterprise and DevelopmentOtago PolytechnicDunedinNew Zealand
  2. 2.Department of Information ScienceUniversity of OtagoDunedinNew Zealand
  3. 3.Faculty of Computer Science and Media TechnologyNorwegian University of Science and TechnologyGjøvikNorway

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