Vagueness in Human Judgment and Decision Making

  • Kazuhisa Takemura
Part of the Computer Science Workbench book series (WORKBENCH)


In many situations we use vague or fuzzy judgment in forming our own opinions or making decisions. The vagueness or fuzziness is inherent in people’s perception and judgment. Traditionally, psychological and philosophical theories implicitly had assumed the vagueness of thought processes [25, 26]. For example, Wittgenstein [43] pointed out that lay categories were better characterized by a “family resemblance” model which assumed vague boundaries of concepts rather than a classical set-theoretic model. Rosch [18] and Rosch and Mervice [19] also suggested vagueness of lay categories in her prototype model and reinterpreted the family resemblance model. Moreover, the social judgment theory [24] and the information integration theory [2] for describing judgment and decision making assumed that people evaluate the objects using natural languages which were inherently vague.


Fuzzy Number Prospect Theory Triangular Fuzzy Number Fuzzy Measure Evaluative Judgment 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    M. Allais, “Le comportement de l’homme rationnel devant le risque, critique des postulates et axiomes de I’école américaine,” Econometrica, Vol.21, pp.503–546, 1953.MathSciNetMATHCrossRefGoogle Scholar
  2. [2]
    N.H. Anderson, “A functional approach to person cognition,” In T.K. Srull and R.S. Wyer (Eds.), Advances in social cognition, vol.1, Hiisdale, New Jersey: Lawrence Erlbaum Associates, pp.37–51, 1988.Google Scholar
  3. [3]
    R. Beyth-Marom, “How probable is probable?: Numerical translation of verbal probability expressions,” Journal of Forecasting, Vol.1, pp.267–269, 1982.CrossRefGoogle Scholar
  4. [4]
    G. Choquet, “Theory of capacities,” Annales de L’Institut Fourier, Vol.5, pp.131–295, 1953.MathSciNetCrossRefGoogle Scholar
  5. [5]
    R.M. Dawes, “The robust beauty of improper linear models in decision making,” Kahneman, P. Slovic, and A. Tversky (Eds.), Judgment under uncertainty: Heuristics and biases, Cambridge: Cambridge University Press, pp.391–407, 1982.Google Scholar
  6. [6]
    P. Diamond, “Fuzzy least squares,” Information Sciences, Vol.46, pp.141–157, 1988.MathSciNetMATHCrossRefGoogle Scholar
  7. [7]
    D. Dubois and H. Prade, Fuzzy sets and systems: Theory and applications, New York: Academic Press, 1980.MATHGoogle Scholar
  8. [8]
    D. Ellsberg, “Risk, ambiguity, and the Savage axiom,” Quaterly Journal of Economics, Vol.75, pp.643–669, 1961.CrossRefGoogle Scholar
  9. [9]
    P.C. Fishburn, Nonlinear preference and utility theory, Baltimore, MD: The Johns Hopkins University Press, 1988.MATHGoogle Scholar
  10. [10]
    B. Hesketh, R. Pryor, M. Gleitman, and T. Hesketh, “Practical applications and psychometric evaluation of a computerised fuzzy graphic rating scale,” in T. Zetenyi, (Ed.), Fuzzy sets in psychology, New York: North-Holland, pp.425–454, 1988.Google Scholar
  11. [11]
    D. Kahneman and A. Tversky, “Prospect theory: An analysis of decision under risk,” Econometrica, 47, pp.263–291, 1979.MATHCrossRefGoogle Scholar
  12. [12]
    A. Kaufman and M.M. Gupta, Introduction to fuzzy arithmetic: Theory and applications, New York: Van Nostrand Reinhold, 1985.Google Scholar
  13. [13]
    L.R. Keller, “Properties of utility theories and related empirical phenomena,” in W. Edwards (Ed.), Utility theories: Measurements and applications, Boston: Kluwer Academic Publishers, pp.3–23, 1992.CrossRefGoogle Scholar
  14. [14]
    M. Mizumoto, “Fuzzy number,” in Japan Society for Fuzzy Theory and Systems, (Ed.), Fuzzy sets, Nikkan Kohgyo Shinbunsha, (in Japanese) pp.157–185, 1992.Google Scholar
  15. [15]
    T. Murofushi and M. Sugeno, “Fuzzy t-conorm integral with respect to fuzzy measures: Generalization of Sugeno integral and the Choquet integral,” Fuzzy set and Systems, Vol.42, pp.57–71, 1991.MathSciNetMATHCrossRefGoogle Scholar
  16. [16]
    T. Murofushi, M. Sugeno, and M. Machida, “Nonmonotonic fuzzy measures and Choquet integral,” Fuzzy Set and Systems, Vol.64, pp.73–86, 1994.MathSciNetMATHCrossRefGoogle Scholar
  17. [17]
    K. Nakamura, “On the nature of intransitivity in human preferential judgments,” in V. Novak, J. Ramik, M. Mares, M. Cherny, and J. Nekola (Eds.), Fuzzy Approach to Reasoning and Decision-Making, Dordrecht: Kluwer Academic Publishers, pp.147–162, 1992.CrossRefGoogle Scholar
  18. [18]
    E. Rosch, “Cognitive representation of semantic categories,” Journal of Experimental Psychology: General, Vol.104, pp.192–233, 1975.CrossRefGoogle Scholar
  19. [19]
    E. Rosch, and C.B. Mervis, “Family resemblances: Studies in the internal structure of categories,” Cognitive Psychology, Vol. 7, pp.573–603, 1975.CrossRefGoogle Scholar
  20. [20]
    M. Sakawa and H. Yano, “Multiobjective fuzzy linear regression analysis for fuzzy input-output data,” Fuzzy Sets and Systems, Vol.47, pp.173–181,1992.MATHCrossRefGoogle Scholar
  21. [21]
    I.R. Savage, The foundations of statistics, New York: Wiley, 1954.MATHGoogle Scholar
  22. [22]
    D. Schmeidler, “Subjective probability and expected utility without additivity,” Econometrica, Vol. 57, pp.571–587, 1989.MathSciNetMATHCrossRefGoogle Scholar
  23. [23]
    F. Seo and I. Nishizaki, “Fuzzy multiattribute utility functions,” Discussion Paper, No. 357, Kyoto Institute of Economic Research, Kyoto University, 1992.Google Scholar
  24. [24]
    M. Sherif and C.L. Hovland, Social judgment: Assimilation and contrast effects in communication and attitude change, New Haven: Yale University Press, 1961.Google Scholar
  25. [25]
    M. Smithson, Fuzzy set analysis for behavioral sciences, New York: Springer-Verlag, 1987.Google Scholar
  26. [26]
    M. Smithson, Ignorance and uncertainty, New York: Springer-Verlag, 1989.CrossRefGoogle Scholar
  27. [27]
    M. Sugeno, “Theory of fuzzy integrals and its applications,” Unpublished Ph.D. Thesis, Tokyo: Tokyo Institute of Technology, 1974.Google Scholar
  28. [28]
    M. Sugeno, Fuzzy control, (in Japanese) Nikkan Kogyo, 1988.Google Scholar
  29. [29]
    K. Takemura, “Reconsideration on concept of the attitude,” Bulletin of Koka Woman’s Junior College, (in Japanese) Vol.28, pp.119–132, 1990.Google Scholar
  30. [30]
    K. Takemura, “A vague information integration model: An application of fuzzy set theory to social psychology,” Bulletin of Koka Woman’s Junior College, Vol.29, pp.91–107, 1991 (in Japanese).Google Scholar
  31. [31]
    K. Takemura, “An analysis of shopping choice behavior using fuzzy multiattribute attitude model: A proposal of a new psychological method for the area marketing,” Japanese Studies in Regional Science, (in Japanese with English abstracts) Vol. 22, pp.119–131, 1992.Google Scholar
  32. [32]
    K. Takemura, “Prediction of behavioral intention by fuzzy multiattribute attitude model: An application of possibilistic linear regression analysis to fuzzy rating data,” Procedings of the 22th Annual Conference of the Behaviormetric Society of Japan, (in Japanese) Tsukuba, Japan, pp.122–125, 1994.Google Scholar
  33. [33]
    K. Takemura, Psychology of Decision Making, (in Japansese) Tokyo: Fukumura syuppan, 1996.Google Scholar
  34. [34]
    K. Takemura, T. Hou, and J. Wo, “Study on consumer’s multiattribute attitude in Taiwan: using possibilistic linear regression analysis for fuzzy rating data,” Proceedings of the 11 th Fuzzy System Symposium (in Japanese), 1995, Okinawa, Japan, pp.413–416.Google Scholar
  35. [35]
    H. Tanaka, “Fuzzy data analysis by possibilistic linear model,” Fuzzy Sets and Systems, Vol.24, pp.363–375, 1987.MathSciNetMATHCrossRefGoogle Scholar
  36. [36]
    H. Tanaka and J. Watada, “Possibilistic linear systems and their application to the linear regression model,” Fuzzy Sets and Systems, Vol.27, pp.275–289, 198MathSciNetCrossRefGoogle Scholar
  37. [37]
    H. Tanaka, S. Uejima, and K. Asai, “Linear regression analysis with fuzzy model,” IEEE Transactions on Systems Man Cybernetics, Vol.12, pp.903–907, 1982.MATHCrossRefGoogle Scholar
  38. [38]
    H. Tanaka, I. Hayashi, and K. Nagasaka, “Interval regression analysis by possibilistic measures,” The Japanese Journal of Behaviormetrics, (in Japanese) Vol.16, No.1, pp.1–7, 1988Google Scholar
  39. [39]
    A. Tversky and D. Kahneman, “Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment,” Psychological Review, Vol.90, pp.293–315, 1983.CrossRefGoogle Scholar
  40. [40]
    A. Tversky and D. Kahneman, “Advances in prospect theory: Cumulative representation of uncertainty,” Journal of Risk and Uncertainty, Vol. 5, pp.297–323, 1992.MATHCrossRefGoogle Scholar
  41. [41]
    A. Tversky, S. Sattath, and P. Slovic, “Contingent weighting in judgment and choice,” Psychological Review, Vol.95, pp.371–384, 1988.CrossRefGoogle Scholar
  42. [42]
    J. von Neumann and O. Morgenstern, Theory of Games and Economic Behavior, Princeton, NJ: Princeton University Press, 2nd ed, 1947.Google Scholar
  43. [43]
    L. Wittgenstein, Philosophical Investigations, New York: MacMillan, 1953.Google Scholar
  44. [44]
    J.F. Yates, Judgment and Decision Making,Englewood Cliffs, New Jersey: Prentice-Hall, 1989.Google Scholar
  45. [45]
    A. Zadeh, “Fuzzy sets,” Information and Control, Vol. 8, pp.338–353, 1965.MathSciNetMATHCrossRefGoogle Scholar
  46. [46]
    A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Transactions on Systems, Man and Cybernetics, Vol.3, No.1, pp.28–44, 1973.MathSciNetMATHCrossRefGoogle Scholar
  47. [47]
    A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning,” Information Sciences, Vol.8, pp.199–249, 1975.MathSciNetCrossRefGoogle Scholar
  48. [48]
    R. Zwick, D.V. Budescu, and T.S. Wallsten, “An empirical study of the integration of linguistic probabilities,” in T. Zetenyi (Ed.), Fuzzy sets in psychology, Amsterdam: North-Holland, pp.91–125, 1988.Google Scholar

Copyright information

© Springer-Verlag Tokyo 2000

Authors and Affiliations

  • Kazuhisa Takemura

There are no affiliations available

Personalised recommendations