Skip to main content

On Group Decision Making, Consensus Reaching, Voting and Voting Paradoxes under Fuzzy Preferences and a Fuzzy Majority: A Survey and some Perspectives

  • Chapter

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 220))

Abstract

Group decision making, as meant in this chapter, is the following choice problem which proceeds in a multiperson setting. There is a group of individuals (decisionmakers, experts, ...) who provide their testimonies concerning an issue in question. These testimonies are assumed here to be individual preference relations over some set of option (alternatives, variants, ...). The problem is to find a solution, i.e. an alternative or a set of alternatives, from among the feasible ones, which best reflects the preferences of the group of individuals as a whole. We will survey main developments in group decision making under fuzziness. First, we will briefly outline some basic inconsistencies and negative results of group decision making and social choice, and show how they can be alleviated by some plausible modifications of underlying assumptions, mainly by introducing fuzzy preference relations and, to a lesser extent, a fuzzy majority. Then, we will concentrate on how to derive solutions under individual fuzzy preference relations, and a fuzzy majority equated with a fuzzy linguistic quantifier (e.g., most, almost all, ...) and dealt with in terms of a fuzzy logic based calculus of linguistically quantified statements or via the ordered weighted averaging (OWA) operators. We will briefly mention that one of solution concepts proposed can be a prototype for a wide class of group decision making choice functions. Then, we will discuss a related issue of how to define a “soft” degree of consensus in the group under individual fuzzy preference relations and a fuzzy majority. Finally, we will show how fuzzy preferences can help alleviate some voting paradoxes.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aizerman, M.A. (1985). New problems in the general choice theory, Social Choice and Welfare, 2, 235–282.

    Google Scholar 

  2. Arrow, K.J. (1963). Social Choice and Individual Values. 2nd Edition. Wiley, New York.

    Google Scholar 

  3. Barrett, C.R., Pattanaik, P.K. and Salles, M. (1986). On the structure of fuzzy social welfare functions. Fuzzy Sets and Systems, 19, 1–10.

    Google Scholar 

  4. Barrett, C.R., Pattanaik, P.K. and Salles, M. (1990). On choosing rationally when preferences are fuzzy. Fuzzy Sets and Systems, 34, 197–212.

    Google Scholar 

  5. Barrett, C.R., Pattanaik, P.K. and Salles, M. (1992). Rationality and aggregation of preferences in an ordinally fuzzy framework. Fuzzy Sets and Systems 49, 9–13.

    Google Scholar 

  6. Basu, K, Deb, R. and Pattanaik, P.K. (1992) Soft sets: An ordinal formulation of vagueness with some applications to the theory of choice. Fuzzy Sets and Systems 45, 45–58.

    Article  MATH  MathSciNet  Google Scholar 

  7. Bezdek, J.C., Spillman, B. and Spillman, R. (1978). A fuzzy relation space for group decision theory, Fuzzy Sets and Systems, 1, 255–268.

    Google Scholar 

  8. Bezdek, J.C., Spillman, B. and Spillman, R. (1979). Fuzzy relation space for group decision theory: An application, Fuzzy Sets and Systems, 2, 5–14.

    Google Scholar 

  9. Blin, J.M. (1974). Fuzzy relations in group decision theory, J. of Cybernetics, 4, 17–22.

    Google Scholar 

  10. Blin, J.M. and Whinston, A.P. (1973). Fuzzy sets and social choice, J. of Cybernetics, 4, 17–22.

    Google Scholar 

  11. Bordogna, G., Fedrizzi, M. and Pasi, G. (1997) A linguistic modelling of consensus in group decision making based on OWA operators, IEEE Trans. on Systems, Man and Cybernetics, SMC-27, 126–132.

    Google Scholar 

  12. Chiclana, F, Herrera, F. and Herrera-Viedma, E. (2001) Integrating multiplicative preference relations in a multipurpose decision making model based on fuzzy preference relations. Fuzzy Sets and Systems, 122, 277–291.

    Article  MATH  MathSciNet  Google Scholar 

  13. Chiclana, F, Herrera, F. and Herrera-Viedma, E. (2001a) Multiperson decision making based on multiplicative preference relations. European Journal of Operational Research, 129, 372–385.

    Article  MATH  MathSciNet  Google Scholar 

  14. Cutello, V. and Montero, J. (1993) A characterization of rational amalgamation operations, International J. of Approximate Reasoning, 8, 325–344.

    Article  MATH  MathSciNet  Google Scholar 

  15. Dasgupta, M. and Deb, R. (1996), Transitivity and fuzzy preferences, Social Choice and Welfare, 13, 305–318.

    Article  MATH  MathSciNet  Google Scholar 

  16. DeGrazia, A. (1953), Mathematical Derivation of an Election System, Isis, 44, 42–51.

    Article  Google Scholar 

  17. Delgado, M., Verdegay, J.L. and Vila, M.A. (1993). On aggregation operations of linguistic labels, Int. J. of Intelligent Systems, 8, 351–370.

    Google Scholar 

  18. Delgado,M., Herrera, F., Herrera-Viedma, E. and Martinez, L. (1998) Combining numerical and linguistic information in group decision making. Information Sciences, 107, 177–194.

    Article  MathSciNet  Google Scholar 

  19. Fedrizzi, M., Kacprzyk, J. and Nurmi, H. (1993). Consensus degrees under fuzzy majorities and fuzzy preferences using OWA (ordered weighted average) operators, Control and Cybernetics, 22, 71–80.

    Google Scholar 

  20. Fedrizzi, M., Kacprzyk, J. and Nurmi, H. (1996). How different are social choice functions: a rough sets approach, Quality and Quantity, 30, 87–99.

    Google Scholar 

  21. Fedrizzi, M., Kacprzyk, J. and Zadrożny, S. (1988). An interactive multi-user decision support system for consensus reaching processes using fuzzy logic with linguistic quantifiers, Decision Support Systems, 4, 313–327.

    Article  Google Scholar 

  22. Fishburn, P.C. (1990). Multiperson decision making: a selective review. In J. Kacprzyk and M. Fedrizzi (Eds.): Multiperson Decision Making Models using Fuzzy Sets and Possibility Theory, Kluwer, Dordrecht, pp. 3–27.

    Google Scholar 

  23. Fodor, J. and Roubens, M. (1994) Fuzzy Preference Modelling and Multicriteria Decision Support. Kluwer, Dordrecht.

    MATH  Google Scholar 

  24. García-Lapresta, J.L. and Llamazares, B. (2000). Aggregation of fuzzy preferences: Some rules of the mean. Social Choice and Welfare, 17, 673–690.

    Google Scholar 

  25. González-Pachòn, J., Gómez, D., Montero, J. and Yáñez, J. (2003) Searching for the dimension of valued preference relations, International Journal of Approximate Reasoning, 33, 133–157.

    Article  MATH  MathSciNet  Google Scholar 

  26. Gonzáalez-Pachón, J., Gómez, D., Montero, J. and Yáñez, J. (2003a) Soft dimension theory, Fuzzy Sets and Systems, 137, 137–149.

    Article  Google Scholar 

  27. Herrera, F., and Herrera-Viedma, E. (2000) Choice functions and mechanisms for linguistic preference relations. European Journal of Operational Research, 120, 144–161.

    Article  MATH  MathSciNet  Google Scholar 

  28. Herrera, F., Herrera-Viedma, E. and Verdegay, J.L. (1996). A model of consensus in group decision making under linguistic assessments, Fuzzy Sets and Systems, 78, 73–88.

    Google Scholar 

  29. Herrera, F., Herrera-Viedma, E. and Verdegay, J.L. (1998) Choice processes for non-homogeneous group decision making in linguistic setting. Fuzzy Sets and Systems, 94, 297–308.

    Article  MathSciNet  Google Scholar 

  30. Herrera, F., Herrera-Viedma, E. and Verdegay, J.L. (1997) Linguistic measures based on fuzzy coincidence for reaching consensus in group decision making. International Journal of Approximate Reasoning, 16, 309–334.

    Article  MATH  Google Scholar 

  31. Herrera, F., Herrera-Viedma, E. and Verdegay, J.L. (1997a) A rational consensus model in group decision making using linguistic assessments. Fuzzy Sets and Systems, 88, 31–49.

    Article  Google Scholar 

  32. Herrera, F., Martínez, L. (2000) An approach for combining numerical and linguistic information based on the 2-tuple fuzzy linguistic representation model in decision making. International J. of Uncertainty , Fuzziness and Knowledge-Based Systems 8, 539–562.

    Article  MATH  Google Scholar 

  33. Herrera, F. and Verdegay, J.L. (1995). On group decision making under linguistic preferences and fuzzy linguistic quantifiers. In B. Bouchon-Meunier, R.R. Yager and L.A. Zadeh (Eds.): Fuzzy Logic and Soft Computing, World Scientific, Singapore, pp. 173–180.

    Google Scholar 

  34. Intrilligator, M.D. (1973). A probabilistic model of social choice, Review of Economic Studies, 40, 553–560.

    Google Scholar 

  35. Intrilligator, M.D. (1982). Probabilistic models of choice, Mathematical Social Sciences, 2, 157–166.

    Google Scholar 

  36. Kacprzyk, J. (1984). Collective decision making with a fuzzy majority rule, Proc. of WOGSC Congress, AFCET, Paris, pp. 153–159.

    Google Scholar 

  37. Kacprzyk, J. (1985). Zadeh’s commonsense knowledge and its use in multicriteria, multistage and multiperson decision making. In M.M. Gupta et al. (Eds.): Approximate Reasoning in Expert Systems, North–Holland, Amsterdam, pp. 105–121.

    Google Scholar 

  38. Kacprzyk, J. (1985). Group decision-making with a fuzzy majority via linguistic quantifiers. Part I: A consensory-like pooling; Part II: A competitive-like pooling, Cybernetics and Systems: an International J., 16, 119–129 (Part I), 131–144 (Part II).

    Google Scholar 

  39. Kacprzyk, J. (1986). Group decision making with a fuzzy linguistic majority, Fuzzy Sets and Systems, 18, 105–118.

    Google Scholar 

  40. Kacprzyk, J. (1987). On some fuzzy cores and “soft” consensus measures in group decision making. In J.C. Bezdek (Ed.): The Analysis of Fuzzy Information, Vol. 2, CRC Press, Boca Raton, pp. 119–130.

    Google Scholar 

  41. Kacprzyk, J. (1987). Towards ‘human consistent’ decision support systems through commonsense-knowledge-based decision making and control models: a fuzzy logic approach, Computers and Artificial Intelligence, 6, 97–122.

    Google Scholar 

  42. Kacprzyk, J. and Fedrizzi, M. (1986). “Soft” consensus measures for monitoring real consensus reaching processes under fuzzy preferences, Control and Cybernetics, 15, 309–323.

    MathSciNet  Google Scholar 

  43. Kacprzyk, J. and Fedrizzi, M. (1988). A “soft” measure of consensus in the setting of partial (fuzzy) preferences, Europ. J. of Operational Research, 34, 315–325.

    Google Scholar 

  44. Kacprzyk, J. and Fedrizzi, M. (1989). A ‘human-consistent’ degree of consensus based on fuzzy logic with linguistic quantifiers, Mathematical Social Sciences, 18, 275–290.

    Google Scholar 

  45. Kacprzyk, J. and Fedrizzi, M., Eds. (1990). Multiperson Decision Making Models Using Fuzzy Sets and Possibility Theory, Kluwer, Dordrecht.

    MATH  Google Scholar 

  46. Kacprzyk, J., Fedrizzi, M. and Nurmi, H. (1992). Group decision making and consensus under fuzzy preferences and fuzzy majority, Fuzzy Sets and Systems, 49, 21–31.

    Google Scholar 

  47. Kacprzyk, J., Fedrizzi, M. and Nurmi, H. (1997). OWA operators in group decision making and consensus reaching under fuzzy preferences and fuzzy majority. In R.R. Yager and J. Kacprzyk (Eds.): The Ordered Weighted Averaging Operators: Theory and Applications, Kluwer, Boston, pp. 193–206.

    Google Scholar 

  48. Kacprzyk, J. and Nurmi, H. (1998) Group decision making under fuzziness, in R. Słowiński (Ed.): Fuzzy Sets in Decision Analysis, Operations Research and Statistics, Kluwer, Boston, pp. 103–136.

    Google Scholar 

  49. Kacprzyk, J., Nurmi, H. and Fedrizzi, M., Eds. (1996). Consensus under Fuzziness, Kluwer, Boston.

    Google Scholar 

  50. Kacprzyk, J., Nurmi H. and Fedrizzi, M. (1999) Group decision making and a measure of consensus under fuzzy preferences and a fuzzy linguistic majority, In L.A. Zadeh and J. Kacprzyk (Eds.): Computing with Words in Information/Intelligent Systems. Part 2. Foundations, Physica–Verlag (Springer–Verlag), Heidelberg and New York, pp. 233-–243.

    Google Scholar 

  51. Kacprzyk, J. and Roubens, M., Eds. (1988). Non-Conventional Preference Relations in Decision Making, Springer–Verlag, Heidelberg.

    MATH  Google Scholar 

  52. Kacprzyk, J. and Zadrożny, S. (2002) Collective choice rules in group decision making under fuzzy preferences and fuzzy majority: a unified OWA operator based approach. Control and Cybernetics, 31, 937–948.

    Google Scholar 

  53. Kacprzyk, J. and Zadrożny (2003) An Internet-based group decision support system, Management, VII (28), 4–10.

    Google Scholar 

  54. Kacprzyk J. and Zadrożny S. (2003) Dealing with imprecise knowledge on preferences and majority in group decision making: towards a unified characterization of individual and collective choice functions, Bull. of the Polish Academy of Sciences. Tech. Sci., 3, 286–302.

    Google Scholar 

  55. Kacprzyk, J., Zadrożny, S. and Fedrizzi, M. (1997). An interactive GDSS for consensus reaching using fuzzy logic with linguistic quantifiers. In D. Dubois, H. Prade and R.R. Yager (Eds.): Fuzzy Information Engineering–A Guided Tour of Applications, Wiley, New York, pp. 567–574.

    Google Scholar 

  56. Kelly, J.S. (1978) Arrow Impossibility Theorems. Academic Press, New York.

    MATH  Google Scholar 

  57. Kelly, J.S. (1978) Social Choice Theory: An Introduction, Academic Press, New York.

    Google Scholar 

  58. Kim, J.B. (1983). Fuzzy rational choice functions, Fuzzy Sets and Systems, 10, 37–43.

    Google Scholar 

  59. Kuzmin, V.B. and Ovchinnikov, S.V. (1980a). Group decisions I: In arbitrary spaces of fuzzy binary relations, Fuzzy Sets and Systems, 4, 53–62.

    Article  MathSciNet  Google Scholar 

  60. Kuzmin, V.B. and Ovchinnikov, S.V. (1980b). Design of group decisions II: In spaces of partial order fuzzy relations, Fuzzy Sets and Systems, 4, 153–165.

    Article  MathSciNet  Google Scholar 

  61. Lagerspetz, E. (1995), Paradoxes and representation. Electoral Studies, 15, 83–92.

    Article  Google Scholar 

  62. Loewer, B. and Laddaga, R. (1985). Destroying the consensus, in Loewer B., Guest Ed., Special Issue on Consensus, Synthese, 62 (1), pp. 79–96.

    Google Scholar 

  63. Montero, J. (1985) A note on Fung-Fu‘s theorem’’, Fuzzy Sets and Systems, 13, 259–269.

    Article  Google Scholar 

  64. Montero, J. (1987) Arrow’s theorem under fuzzy rationality, Behavioral Science, 32, 267–273.

    Article  MathSciNet  Google Scholar 

  65. Montero, J. (1988) Aggregation of fuzzy opinions in a non-homogeneous group, Fuzzy Sets and Systems, 25, 15–20.

    Article  MATH  MathSciNet  Google Scholar 

  66. Montero, J. (1990) Single-peakedness in weighted aggregation of fuzzy opinions in a fuzzy group, in: Kacprzyk J and Fedrizzi, M. Eds., Multiperson Decision Making Models, Kluwer, Dordrecht, pp. 163–171.

    Google Scholar 

  67. Montero, J., Tejada, J. and Cutello, V. (1997) A general model for deriving preference structures from data, European J. of Operational Research, 98, 98–110.

    Article  MATH  Google Scholar 

  68. Nurmi, H. (1981). Approaches to collective decision making with fuzzy preference relations, Fuzzy Sets and Systems, 6, 249–259.

    Google Scholar 

  69. Nurmi, H. (1982). Imprecise notions in individual and group decision theory: resolution of Allais paradox and related problems, Stochastica, VI, 283–303.

    Google Scholar 

  70. Nurmi, H. (1983). Voting procedures: a summary analysis, British J. of Political Science, 13, 181–208.

    Google Scholar 

  71. Nurmi, H. (1984). Probabilistic voting, Political Methodology, 10, 81–95.

    Google Scholar 

  72. Nurmi, H. (1987). Comparing Voting Systems, Reidel, Dordrecht.

    Google Scholar 

  73. Nurmi, H. (1997), Referendum design: an exercise in applied social choice theory, Scandinavian Political Studies, 20, 33–52.

    Article  Google Scholar 

  74. Nurmi, H. (1998), Voting paradoxes and referenda, Social Choice and Welfare, 15, 333–350.

    Article  MATH  MathSciNet  Google Scholar 

  75. Nurmi, H. (1999), Voting Paradoxes and How to Deal with Them. Springer–Verlag, Berlin-Heidelberg-New York.

    MATH  Google Scholar 

  76. Nurmi, H. and Kacprzyk, J. (1991). On fuzzy tournaments and their solution concepts in group decision making, Europ. J. of Operational Research, 51, 223–232.

    Google Scholar 

  77. Nurmi, H. and Kacprzyk, J. (2000) Social choice under fuzziness: a perspective. In: J. Fodor, B. De Baets and P. Perny (Eds.): Preferences and Decisions under Incomplete Knowledge. Physica–Verlag (Springer–Verlag), Heidelberg and New York, pp. 107–130.

    Google Scholar 

  78. Nurmi, H., Kacprzyk, J. and Fedrizzi, M. (1996). Probabilistic, fuzzy and rough concepts in social choice, Europ. J. of Operational Research, 95, 264–277.

    Google Scholar 

  79. Roubens, M. and Vincke, Ph. (1985). Preference Modelling, Springer–Verlag, Berlin.

    MATH  Google Scholar 

  80. Salles, M. (1996). Fuzzy utility. In S. Barberá, P.J. Hammond and C. Seidl (Eds.): Handbook of Utility Theory, Kluwer, Boston.

    Google Scholar 

  81. Sengupta, K. (1999), Choice rules with fuzzy preferences: some characterizations, Social Choice and Welfare, 16, 259–272.

    Article  MATH  MathSciNet  Google Scholar 

  82. Szmidt, E. and Kacprzyk, J. (1996). Intuitionistic fuzzy sets in group decision making, Notes on Intuitionistic Fuzzy Sets, 2, 15–32.

    Google Scholar 

  83. Tanino, T. (1984). Fuzzy preference orderings in group decision making, Fuzzy Sets and Systems, 12, 117–131.

    Google Scholar 

  84. Yager, R.R (1988). On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Trans. on Systems, Man and Cybernetics, SMC-18, 183–190.

    Google Scholar 

  85. Yager, R.R. and Kacprzyk, J. (Eds.) (1997). The Ordered Weighted Averaging Operators: Theory and Applications, Kluwer, Boston.

    Google Scholar 

  86. Zadeh, L.A. (1983). A computational approach to fuzzy quantifiers in natural languages, Computers and Maths. with Appls., 9, 149–184.

    Google Scholar 

  87. Zadrożny, S. (1997). An approach to the consensus reaching support in fuzzy environment. In: J. Kacprzyk, H. Nurmi and M. Fedrizzi (Eds.): Consensus under Fuzziness. Kluwer, Boston, pp. 83–109.

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Kacprzyk, J., Zadrożny, S., Fedrizzi, M., Nurmi, H. (2008). On Group Decision Making, Consensus Reaching, Voting and Voting Paradoxes under Fuzzy Preferences and a Fuzzy Majority: A Survey and some Perspectives. In: Bustince, H., Herrera, F., Montero, J. (eds) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73723-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73723-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73722-3

  • Online ISBN: 978-3-540-73723-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics