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Choice rules with fuzzy preferences: Some characterizations

Author

Listed:
  • Kunal Sengupta

    (CESP, Jawaharlal Nehru University, New Delhi, India)

Abstract

Consider an agent with fuzzy preferences. This agent, however, has to make exact choices when faced with different feasible sets of alternatives. What rule does he follow in making such choices? This paper provides an axiomatic characterization of a class of binary choice rules called the satisfying rule. When =1, this rule is the Orlovsky choice rule. On the other hand, for \leq1/2, the rule coincides with the M rule that has been extensively analyzed in the literature on fuzzy preferences.

Suggested Citation

  • Kunal Sengupta, 1999. "Choice rules with fuzzy preferences: Some characterizations," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 16(2), pages 259-272.
    • Handle: RePEc:spr:sochwe:v:16:y:1999:i:2:p:259-272
    Note: Received: 3 August 1995/Accepted: 19 November 1997
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    Citations

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    Cited by:

    1. Sara Lelli, 2001. "Factor Analysis vs. Fuzzy Sets Theory: Assessing the Influence of Different Techniques on Sen's Functioning Approach," Public Economics Working Paper Series ces0121, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Working Group Public Economics.
    2. Hannu Nurmi, 2001. "Resolving Group Choice Paradoxes Using Probabilistic and Fuzzy Concepts," Group Decision and Negotiation, Springer, vol. 10(2), pages 177-199, March.
    3. Draeseke, Robert & Giles, David E.A., 2002. "A fuzzy logic approach to modelling the New Zealand underground economy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(1), pages 115-123.
    4. Davide Martinetti & Susana Montes & Susana Díaz & Bernard Baets, 2018. "On a correspondence between probabilistic and fuzzy choice functions," Fuzzy Optimization and Decision Making, Springer, vol. 17(3), pages 247-264, September.
    5. David E. A. Giles & Robert Draeseke, 2001. "Econometric Modelling based on Pattern recognition via the Fuzzy c-Means Clustering Algorithm," Econometrics Working Papers 0101, Department of Economics, University of Victoria.
    6. Yu, Tiffany Hui-Kuang & Wang, David Han-Min & Chen, Su-Jane, 2006. "A fuzzy logic approach to modeling the underground economy in Taiwan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 471-479.

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