Combining stochastic uncertainty and linguistic inexactness: theory and experimental evaluation of four fuzzy probability models

https://doi.org/10.1016/S0020-7373(89)80021-5Get rights and content

Two major sources of imprecision in human knowledge, linguistic inexactness and stochastic uncertainty, are identified in this study. It is argued that since in most realistic situations these two types exist simultaneously, it is necessary to combine them in a formal framework to yield realistic solutions. This study presents such a framework by combining concepts from probability and fuzzy set theories. In this framework four models (Kwakernaak, 1978; Yager, 1979, Yager, 1984; Zadeh, 1968, Zadeh, 1975) that attempt to account for the numeric or linguistic responses in various probability elicitation tasks were tested. The linguistic models were relatively effective in predicting subjects' responses compared to a random choice model. The numeric model (Zadeh, 1968) proved to be insufficient. These results and others suggest that subjects are unable to represent the full complexity of a problem. Instead they adopt a simplified view of the problem by representing vague linguistic concepts by multiple-crisp representations (the α-level sets). All of the mental computation is done at these surrogate levels.

References (86)

  • M. Kochen

    Applications of fuzzy sets in psychology.

  • M. Kochen

    Enhancement of coping through blurring

    Fuzzy Sets and Systems

    (1979)
  • R. Kruse

    The strong law of large numbers of fuzzy random variables

    Information Sciences

    (1982)
  • R. Kruse

    Statistical estimation with linguistic data

    Information Sciences

    (1984)
  • H. Kwakernaak

    Fuzzy random variables, I: Definitions

    Information Sciences

    (1978)
  • H. Kwakernaak

    Fuzzy random variables, I1: Algorithms and examples for the discrete case

    Information Sciences

    (1979)
  • M. Miyakoshi et al.

    A strong law of large numbers for fuzzy random variables

    Fuzzy Sets and Systems

    (1984)
  • S. Nahmias

    Fuzzy variables

    Fuzzy Sets and Systems

    (1978)
  • M.A. Nakao et al.

    Numbers are better than words

    American Journal of Medicine

    (1983)
  • H.T. Nguyen

    On fuzziness and linguistic probabilities

    Journal of Mathematical Analysis and Applications

    (1977)
  • A.M. Norwich et al.

    A model for the measurement of membership and the consequences of its empirical implication

    Fuzzy Sets and Systems

    (1984)
  • A.L. Ralescu et al.

    Probability and fuzziness

    Information Science

    (1984)
  • A. Rapoport et al.

    Direct and indirect scaling of membership functions of probability phrases

    Mathematical Modelling

    (1987)
  • G. Shafer et al.

    Languages and designs for probability judgment

    Cognitive Science

    (1985)
  • P. Smets

    Probability of a fuzzy event: an axiomatic approach

    Fuzzy Sets and Systems

    (1982)
  • W.E. Stein

    Fuzzy probability vectors

    Fuzzy Sets and Systems

    (1985)
  • W.E. Stein et al.

    Convex fuzzy variables

    Fuzzy Sets and Systems

    (1981)
  • P. Szolovits et al.

    Categorial and probabilisitc reasoning in medical diagnosis

    Artificial Intelligence

    (1978)
  • T.S. Wallsten et al.

    Base rate effects on the interpretation of probability and frequency expressions

    Journal of Memory and Language

    (1986)
  • R.R. Yager

    A note on probabilities of fuzzy events

    Information Sciences

    (1979)
  • R.R. Yager

    Generalized probabilities of fuzzy events from belief structures

    Information Sciences

    (1982)
  • R.R. Yager

    Probabilities from fuzzy observations

    Information Sciences

    (1984)
  • R.R. Yager

    A representation of the probability of a fuzzy subset

    Fuzzy Sets and Systems

    (1984)
  • L.A. Zadeh

    Fuzzy sets

    Information and Control

    (1965)
  • L.A. Zadeh

    Probability measures of fuzzy events

    Journal of Mathematical Analysis and Applications

    (1968)
  • L.A. Zadeh

    The concept of linguistic variable and its application to approximate reasoning.

    Information Science

    (1975)

    Information Science

    (1975)

    Information Science

    (1975)
  • L.A. Zadeh

    Fuzzy probabilities

    Information Processing and Management

    (1984)
  • A.C. Zimmer

    Verbal versus numerical processing of subjective probabilities.

  • R. Zwick

    A note on random sets and the Thurstonian scaling methods

    Fuzzy Sets and Systems

    (1987)
  • R. Zwick

    The Evaluation of Verbal Models

    International Journal of Man-Machine Studies

    (1988)
  • R. Zwick et al.

    An empirical study of the integration of linguistic probabilities

  • R. Zwick et al.

    Measures of similarity among fuzzy concepts: A comparative analysis

    International Journal of Approximate Reasoning

    (1987)
  • S.W. Becker et al.

    What price ambiguity? Or the role of ambiguity in decision making

    Journal of Political Economy

    (1964)
  • Cited by (44)

    • Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review

      2020, Artificial Intelligence in Medicine
      Citation Excerpt :

      Fuzzy logic represents a possibility logic model that uses reasoning to explain whether an event is about to happen [81]. This model was introduced by [82,83] and facilitates the process of vagueness treatment in a DSS by generating fuzzy rules using vague linguistic terms [84,85] instead of conventional rules to model decision boundaries in a more flexible way. However, it is difficult to estimate the membership functions [86].

    • Getting the picture: A visual metaphor increases the effectiveness of retirement communication

      2019, Futures
      Citation Excerpt :

      The more detailed text, with information about the probability, was: "there is a small chance that …" ('Detail Probability'). The reason for using a verbal probability is that people translate numerical probabilities (e.g., 5% chance) immediately into verbal probabilities (Bottorff et al., 1998; Palmer & Sainfort, 1993) and people prefer to use them (Zwick & Wallsten, 1989). The combination of the two textual factors Level of Detail Outcome and Level of Detail Probability resulted in four groups of phrases, see Appendix A.

    • Knowledge discovery in clinical decision support systems for pain management: A systematic review

      2014, Artificial Intelligence in Medicine
      Citation Excerpt :

      Fuzzy logic [74] represents a possibility logic model that uses reasoning to explain whether an event is about to happen. This model was introduced by [18,40] with the advantage that it allows for the use of vague linguistic terms in the rules [75,76]. However, it is difficult to estimate the membership functions [77] (see Fig. 3).

    • The appeal of vague financial forecasts

      2011, Organizational Behavior and Human Decision Processes
      Citation Excerpt :

      This curvilinear preference can be explained by the investors’ desire to achieve an appropriate balance between congruence and precision for informative decision making. The desire for congruity can be explained in the context of the three-way taxonomy of the sources of imprecision that affect the way people process and communicate uncertainty discussed by Budescu and Wallsten (1995) (see also Wallsten, 1990; Zwick & Wallsten, 1989). The three sources are the definition of the target event, the nature of uncertainty about that event, and the representation of this uncertainty.

    View all citing articles on Scopus
    View full text