Combining stochastic uncertainty and linguistic inexactness: theory and experimental evaluation of four fuzzy probability models
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Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review
2020, Artificial Intelligence in MedicineCitation 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, FuturesCitation 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 MedicineCitation 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 ProcessesCitation 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.
Testing the descriptive validity of possibility theory in human judgments of uncertainty
2003, Artificial IntelligenceApplication of fuzzy expert systems in assessing operational risk of software
2003, Information and Software Technology