口語評估詞統計值估計之研究__臺灣人文及社會科學引文索引資料庫

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題名：	口語評估詞統計值估計之研究
作者：	蕭文峰
作者(外文)：	Wen-Feng Hsiao
校院名稱：	國立中山大學
系所名稱：	資訊管理學系研究所
指導教授：	林信惠
學位類別：	博士
出版日期：	2001
主題關鍵詞：	口語共識判斷；口語資訊；口語意見綜合；認知直覺運算；口語表達；verbal information；cognitive operation；and consensus judgment of verbal opinions；aggregation of verbal opinions；verbal representation
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(1) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:1 共同引用:0 點閱:38

口語形式的資訊在人類日常溝通中佔極重要角色。最近的研究指出人類認知在處理口語形式的資訊與數值形式的資訊並無顯著差異。然而目前尚無適當的模式可用以描述認知之口語資訊處理。因此，本論文探討人類認知與規範性方法(包括數值方法與模糊集合方法)在處理口語資訊上的差異、並分析人類認知之共通決策法則，以藉此對口語運算之描述性模式提出建議。所探討之口語運算包括口語統計值之位置、均值、及變異。
在口語位置研究中，本論文探討口語五等級評估尺度之數值表示法、模糊表示法、及認知表示法之差異。其中，認知表示法是以所提之區間估計方式獲取。研究結果發現認知之口語表達與數值表示法的等距(equal interval)，或模糊表示法之對稱、等區間(equal space)的假設不符。
在均值運算方面，本研究首先探討數值方法、模糊方法、及認知方法三者在綜合口語評估詞之差異。研究結果顯示數值運算表現最差，與人類認知運算有顯著差異。模糊集合運算之表現亦普遍不佳；此意謂著以模糊集合運算充當描述性運算仍有一段距離。另外，以所提之認知表示法進行模糊數運算，可得到較接近人類認知之口語綜合(相符比由0.62提昇為0.77)。為瞭解決策者綜合口語資訊所採用之決策法則，本研究並以多維尺度法(Multi-Dimensional Scaling)分析三個實驗的資料。分析結果指出受試者綜合口語資訊時，除了受口語詞的數值均值影響外，尚會考慮口語詞的「極值」與「極性」。
在變異運算方面，本研究以基準比較法獲取受試者的主觀判斷，並以因子實驗探討受試者之口語變異估計受那些因素影響。其結果發現受試者之口語變異估計會受口語詞的「數值變異」、「熵值」、「極性」、「數值變異與極性的交互作用」、「熵值與極性的交互作用」、及「數值變異、熵值、與極性的交互作用」所影響。而且「熵值」比「數值變異」更能描述人類認知之口語變異估計。
本論文之結果可用以輔助現有數量方法處理定性資料。論文最後並說明這些研究發現的可能應用。
關鍵詞：口語資訊、認知直覺運算、口語表達、口語意見綜合、口語共識判斷。

以文找文

Verbal information plays a pivot role in human daily communication. Recent research has pointed out that the performance of human cognition in processing verbal information has no significant difference from that in processing numerical information. However, no proper model is available to describe human cognition in processing of verbal information. Therefore, this dissertation explores the difference between human cognition and normative models in processing verbal terms, and further analyzes the decision rules employed by decision-makers to illustrate the proper form of a descriptive model. The explored verbal operations include the following statistics: representation, mean, and variance.
In the study of verbal representation, the differences among numerical representation, fuzzy representation, and cognitive representation of Likert verbal evaluations are revealed. This cognitive representation is obtained by the proposed interval estimation method. The proposed method can simultaneously construct the verbal categories in a Likert scale. The result shows that the cognitive representation is inconsistent with the assumption of equal interval in numerical representation, and those of symmetry and equal space in fuzzy representation.
In the study of verbal mean operation, the research first investigated the differences among numerical, fuzzy, and cognitive methods in aggregating verbal terms by conducting three experiments. The results reveal that the numerical operation deviates much from actually decision making. The performances of fuzzy aggregations are also poor. This fact shows that fuzzy aggregations are still not qualified as descriptive operators. However, using cognitive representation to conduct fuzzy number operations can obtain a higher match-rate with the human decision (from 0.62 to 0.77). To understand the decision rules underlying human cognition, the research conduct a Multi-Dimensional Scaling (MDS) analysis. The results show that, other than numerical mean, subjects use two intuitive rules to aggregate opinions, namely, extreme-value and polarity.
In the study of verbal variance operation, the research obtained the subjective judgments by a paired-comparison procedure. Furthermore, a factorial experiment is conducted to investigate the factors that might influence subjects’ verbal consensus judgment. The results show that subjects’ verbal consensus judgment is related to numerical variance, entropy, polarity, the interaction between numerical variance and polarity, the interaction between entropy and polarity, and the interaction among numerical variance, entropy, and polarity. Above all, entropy is a more significant descriptive operator than numerical variance.
The results of the dissertation could complement the current numerical methods in processing qualitative data. Possible applications of the research findings are also discussed.
Keywords: verbal information, cognitive operation, verbal representation, aggregation of verbal opinions, and consensus judgment of verbal opinions.

以文找文

[1] 林信惠與劉永森(1993)，”層級分析(AHP)之區間判斷及綜合函數研究，”管理科學學報，第十卷第二期，第285-303頁。 new window

[2] 張新華(1991)，資訊學概論。台北市：臺灣商務。
[3] Aczel, J. and Alsina, C. (1986). “On synthesis of judgment,” Socio-Economic Planning Sciences, Vol. 20(6), pp. 333-339.
[4] Agresti, A. (1996). An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc.
[5] Agresti, A. and Finlay, B. (1997). Statistical Methods for the Social Sciences 3rd edition, Prentice-Hall.
[6] Allais, M. (1953). “Le comportement de l’homme rationnel devant le risque: Critique des postulates et axioms de l’ecole americaine,” Econometrica, Vol. 21, pp. 503-546.
[7] Anderson, J.R. (1990). Cognitive Psychology and Its Implications (3rd edition). New York:W.H. Freeman and Company.
[8] Anderson, N.H. (ed.)(1990). Contributions to Information Integration Theory Volume I: Cognition. Lawrence Erlbaum Associates, Publishers, Hillsdale.
[9] Aster, R. and Seidman, R. (1997). Professional SAS Programming Secrets �o Updated with new features of releases 6.08-6.10, McGraw-Hill.
[10] Bailey, K.D. (1990). Social Entropy Theory, State University of New York.
[11] Ball, C. (1997). "A comparison of single-step and multiple-step transition analyses of multi-attribute decision strategies," Organizational Behavior and Human Decision Processes, Vol. 69(3), pp. 195-204.
[12] Bamber, E.M. and Watson, R.T. (1996). “The effects of group support system technology on audit group decision making,” Auditing, Vol. 15(1), pp. 122-134. new window

[13] Bell, D.E. (1982). “Regret in decision making under uncertainty,” Operations Research, Vol. 30, pp. 961-981.
[14] Bell, D.E. (1985). “Disappointment in decision making under uncertainty,” Operations Research, Vol. 33(1), pp. 1-27. new window

[15] Beyth-Marom, R. (1982). “How probable is probable? A numerical translation of verbal probability expressions,” Journal of Forecasting, Vol. 1, pp. 257-269. new window

[16] Bockenholt, U. (1992). “Multivariate models of preference and choice,” in F.G. Ashby (eds.), Multidimensional Models of Perception and Cognition. Lawrence Erlbaum Associates, Publishers, Hillsdale, pp. 89-113.
[17] Borg, I. and Groenen, P. (1997). Modern Multidimensional Scaling �o Theory and Applications. Springer-Verlag, New York.
[18] Brun, W. and Teigen, K.H. (1988). “Verbal probabilities: ambiguous, context-dependent, or both?” Organizational Behavior and Human Decision Processes, Vol. 41, pp. 390-404.
[19] Buckley, J.J. (1985). “Fuzzy hierarchical analysis,” Fuzzy Sets and Systems, Vol. 17, pp. 233-247.
[20] Budescu, D.V. and Wallsten, T.S. (1990). “Dyadic decisions with numerical and verbal probabilities,” Organizational Behavior and Human Decision Processes, Vol. 46, pp. 240-263.
[21] Budescu, D.V., Weinberg, S., and Wallsten, T.S. (1988). “Decisions based on numerically and verbally expressed uncertainties,” Journal of Experimental Psychology: Human Perception and Performance, Vol. 14, 281-294.
[22] Camerer, C.F. (1989). “An experimental test of several generalized utility theories,” Journal of Risk and Uncertainty, pp. 61-104.
[23] Camerer, C.F. (1992). “Recent tests of generalizations of expected utility theory,” in w. Edwards (ed.), Utility: Theories, Measures and Applications, Boston: Kluwer Academic Publishers, pp. 207-251.
[24] Carmines, E.G. and Zeller, R.A. (1994). “Reliability and Validity Assessment,” in M.S. Lewis-Beck (ed.), Basic Measurement. International Handbooks of Quantitative Applications in the Social Sciences, Vol. 4. Sage Publications, pp. 1-58.
[25] Carroll, J.D. (1980). “Models and methods for multidimensional analysis of preferential choice (or other dominance) data,” in E.D. Lantermann and H. Feger (eds.), Similarity and choice. Bern: Huber, pp. 234-289.
[26] Chen, S.H. (1985). “Ranking fuzzy numbers with maximizing set and minimizing set,” Fuzzy Sets and Systems, Vol. 17, pp. 113-129.
[27] Chen, S.J. and Hwang, C.L. (1992). “V. Fuzzy multiple attribute decision making methods,” Fuzzy Multiple Attribute Decision Making �o Methods and Applications, Springer-Verlag, pp. 289-486.
[28] Cheng, C.H. and Mon, D.L. (1994). “Evaluating weapon system by analytical hierarchy process based on fuzzy scales,” Fuzzy Sets and Systems, Vol. 63, pp. 1-10.
[29] Cholewa, W. (1985). “Aggregation of fuzzy opinions -- an axiomatic approach,” Fuzzy Sets and Systems, Vol. 17, pp. 249-258.
[30] David, H.A. (1988). The Method of Paired Comparisons (2nd Edition). Charles Griffin & Company Ltd, London.
[31] De Luca, A. and Termini, S. (1972). “A definition of nonprobabilistic entropy in the setting of fuzzy sets theory,” Information and Control, Vol. 20, pp. 301-312.
[32] Dubois, D. and Koning, J.L. (1991). “Social choice axioms for fuzzy set aggregation,” Fuzzy Sets and Systems, Vol. 43, pp. 257-274.
[33] Dubois, D. and Prade, H. (1985). “A review of fuzzy set aggregation connectives,” Information Science, Vol. 36, pp. 85-121.
[34] Dutta, A. (1985). “Reasoning with Imprecise Knowledge in Expert Systems,” Information Sciences, Vol. 37, pp. 3-24.
[35] Dyckhoff, H. and Pedrycz, W. (1984). “Generalized means as a model of compensative connectives,” Fuzzy Sets and Systems, Vol. 14, pp. 143-154.
[36] Eliashberg, J. and Hauser, J. (1985). “A measurement error approach for modeling consumer risk preference,” Management Science, Vol. 31, pp. 1-25.
[37] Ellsberg, D. (1961). “Risk, ambiguity, and the Savage axioms,” Quarterly Journal of Economics, Vol. 75, pp. 643-669.
[38] Ferrell, W.R. (1985). “Combining individual judgements,” in: G. Wright. Behavioral Decision Making, Plenum Press, New York, pp. 111-145.
[39] Fishhoff, B., Slovic, P., and Lichtenstein, S. (1979). “Subjective sensitivity analysis,” Organizational Behavior and Human Performance, Vol. 23, pp. 339-359.
[40] Fung, L.W. and Fu, K.S. (1975). An axiomatic approach to rational decision making in a fuzzy environment, in: L.A. Zadeh et al., eds. Fuzzy Sets and Their Applications to Cognitive and Decision Processes, Academic Press, New York, pp. 227-256.
[41] Garrett, H.E. (1966). Statistics in psychology and education (6th ed.). New York: David Mckay.
[42] Giles, R. (1976). “Lukasiewicz logic and fuzzy set theory,” International Journal of Man-Machine Studies, Vol. 8, pp. 313-327.
[43] Goldstein, W.M. and Einhorn, H. (1987). “A theory of preference reversals,” Psychological Review, Vol. 94(2), pp. 236-254.
[44] Guilford, H. (1965). Fundamental Statistics in Psychology and Education. New York: McGraw-Hill.
[45] Hair, J.F., Jr., Anderson, R.E., Tatham, R.L., and Black, W.C. (1995). Multivariate Data Analysis with Readings (4th edition). Prentice Hall International Editions.
[46] Hakel, M.D. (1968). “How often is often?” American Psychologist, Vol. 23, pp. 533-534.
[47] Hamm, R.M. (1991). “Selection of verbal probabilities: a solution for some problems of verbal probability expression,” Organizational Behavior and Human Decision Processes, Vol. 48, pp. 193-223.
[48] Harnad, S. (1987). “Introduction: Psychophysical and cognitive aspects of categorical perception: A critical overview,” in Harnad, S. (ed.) Categorical Perception �o The Groundwork of Cognition, pp. 1-25.
[49] Hatcher, L and Stepanski, E.J. (1994). A Step-by-Step Approach to Using the SAS System for Univariate and Multivariate Statistics. SAS Institute Inc., Cary, NC, USA.
[50] Hersh, H.M. and Caramazza, A.A. (1976). “A fuzzy set approach to modifiers and vagueness in natural language,” Journal of Experimental Psychology (General), Vol. 105, pp. 254-276.
[51] Hershey, J.C., Kunreuther, H.C., and Schoemaker, P.J.H. (1982). “Sources of bias in assessment procedures for utility functions,” Management Science, Vol. 28, pp. 936-954.
[52] Herskey, J.C. and Schoemaker, P.J.H. (1985). “Probability versus certainty equivalence methods in utility measurement: are they equivalent?” Management Science, Vol. 31, pp. 1213-1231.
[53] Hildebrand, D.K., Laing, J.D., and Rosenthal, H. (1977). Analysis of Ordinal Data, Sage University Paper series on Quantitative Applications in the Social Sciences, series no. 07-008. Beverly Hills and London: Sage Publications.
[54] Hipel, K.W. (1982). “Fuzzy set methodologies in multicriteria modelling,” Fuzzy Information and Decision Processes, pp. 279-287.
[55] Hoerl, A. and Falein, H.K. (1974). “Reliability of subjective evaluation in a high incentive situation,” Journal of the Royal Statistical Society, 137, 227-230.
[56] Hoffman, D. (1994). “Note on the Ventana coefficient of consensus,” Technical Report CS08/94, University College, UNSW.
[57] Hogarth, R. (1987). Judgment and Choice, 2nd Edition. John Wiley & Sons, Chichester.
[58] Hong, T.P. and Lee, C.Y. (1996). “Induction of fuzzy rules and membership functions from training examples,” Fuzzy Sets and Systems, Vol. 84, pp. 33-47.
[59] Huizingh, E.K.R.E. and Vrolijk, H.C.J. (1997). “A comparison of verbal and numerical judgments in the analytic hierarchy process,” Organizational Behavior and Human Decision Processes, Vol. 70(3), pp. 237-247.
[60] Ishii, K. and Sugeno, M. (1985). “A model of human evaluation process using fuzzy measure,” International Journal of Man-Machine Studies, Vol. 22, pp. 19-38.
[61] Kabus, I. (1976). You Can Bank on Uncertainty, Harvard Business Review, May-June, 95-105.
[62] Kahneman, D. and Tversky, A. (1972). “Subjective probability: a judgment of representativeness,” Cognitive Psychology, Vol. 3, pp. 430-454.
[63] Kahneman, D. and Tversky, A. (1973). “On the psychology of prediction,” Psychological Review, Vol. 80, pp. 237-251.
[64] Kahneman, D. and Tversky, A. (1979). “Prospect theory: An analysis of decision under risk,” Econometrica, Vol. 47, pp. 263-291.
[65] Kickert, W.J.M. (1978). Fuzzy Theories on Decision-Making. Martinus Nijhoff, Boston.
[66] Kirk, R.E. (1968). Experimental Design Procedures for the Behavioral Sciences. Wadsworth Publishing Company, Inc.
[67] Kleindorfer, P.R., Kunreuther, H.C., and Schoemaker, P.J.H. (1993). Decision Sciences �o An integrative perspective, Cambridge University Press.
[68] Klir, G.J. and Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic: Theory and Applications, pp. 50-96, Prentice-Hall.
[69] Kosko, B. (1993). “Fuzzy systems as universal approximators,” IEEE Transactions on Computers.
[70] Kuipers, B., Moskowitz, A.J., and Kassirer, J.P. (1988). “Critical decisions under uncertainty: representation and structure,” Cognitive Science, Vol. 12, pp. 177-210.
[71] Lewis-Beck, M.S. (1994). Basic Measurement. International Handbooks of Quantitative Applications in the Social Sciences, Vol. 4, Sage Publications.
[72] Lichtenstein, S. and Slovic, P. (1971). “Reversals of Preference between bids and choices in gambling decisions,” Journal of Experimental Psychology, Vol. 89(1), pp. 140-151. new window

[73] Lin, H.H. and Hsiao, W.F. (1998). "Ranking inconsistency among fuzzy, numeric, and cognitive aggregations", IEEE Fuzzy Conference, Anchorage, Alaska.
[74] Lin, H.H. and Hsiao, W.F. (2000). “Aggregation of verbal evaluations based on numerical, fuzzy, and cognitive methods,” Decision Sciences, submitted.
[75] Loomes, G. and Rugden, R. (1982). “Regret theory: an alternative approach to rational choice under uncertainty,” Economic Journal, Vol. 92, pp. 805-824.
[76] McEvoy, J.P. and Zarate, O. (1995). Stephen Hawking for Beginners, Trumpington, Cambridge: Icon Books.
[77] McIver, J.P. and Carmines, E.G. (1994). “Unidimensional scaling,” in M.S. Lewis-Beck (ed.), Basic Measurement. International Handbooks of Quantitative Applications in the Social Sciences, Vol. 4. Sage Publications, pp. 139-228.
[78] Mejias, R.J. and Shepherd, M.M. (1997). “Consensus and perceived satisfaction levels: A cross-cultural comparison of GSS and non-GSS outcomes within and between the United States and Mexico,” Journal of Management Information Systems, Vol. 13(3), pp. 137-161.
[79] Merz, J.F., Druzdzel, M.J., and Mazur, D.J. (1991). “Verbal expressions of probability in informed consent litigation,” Medical Decision Making, Vol. 11, pp. 273-281.
[80] Mohanty, B.K. and Singh, N. (1994). “Fuzzy relational equations in analytical hierarchy process,” Fuzzy Sets and Systems, Vol. 63, pp. 11-19.
[81] Montero, F.J. (1985). “A note on Fung-Fu’s theorem,” Fuzzy Sets and Systems, Vol. 17, pp. 259-269.
[82] Montgomery, D.C. (1991). Design and Analysis of Experiments (3rd edition), John Wiley & Sons, Singapore.
[83] Murphy, A.H. and Brown, B.G. (1985). “A comparative evaluation of objective and subjective weather forecasts in the united states,” in Wright, G. (ed.) Behavioral Decision Making, Plenum, New York.
[84] Onisawa, T, Sugeno, M., Nishiwaki, Y., Kawai, H., and Harima, Y. (1986). “Fuzzy measure analysis of public attitude towards the use of nuclear energy,” Fuzzy Sets and Systems, Vol. 20, pp. 259-289.
[85] Ostle, B. and Mensing, R.W. (1975). Statistics in Research, The IOWA state university press.
[86] Payne, J.W., Bettman, J.R. and Johnson, E.J. (1993). The Adaptive Decision Maker. Cambridge University Press.
[87] Pepper, S. and Prytulak, L.S. (1974). “Sometimes frequently means seldom: Context effects in the interpretation of quantitative expressions,” Journal of Research in Personality, Vol. 8, pp. 95-101.
[88] Quinlan, J.R. (1986). “Induction of Decision Trees,” Machine Learning, Vol. 1, pp. 81-106. new window

[89] Raporport, A. (1989). Decision Theory and Decision Behavior �o Normative and Descriptive Approaches, Kluwer Academic Publishers, Dordrecht.
[90] Renooij, S. and Witteman, C. (1999). “Talking probabilities: communicating probabilistic information with words and numbers,” International Journal of Approximate Reasoning, Vol. 22, pp. 169-194.
[91] Rosch, E. (1973a). “Natural categories,” Cognitive Psychology, Vol. 4, pp. 328-350.
[92] Rosch, E. (1973b). “On the internal structure of perceptual and semantic categories,” In Moore, T.E. (ed.) Cognitive Development and the Acquisition of Language, New York: Academic Press.
[93] Saaty, T.L. (1980). The Analytic Hierarchy Process, McGraw-Hill, New York.
[94] Samuelson, W. and Zeckhauser, R. (1988). “Status-quo bias in decision making,” Journal of Risk and Uncertainty, Vol. 1, pp. 7–59. new window

[95] SAS Institute Inc. (1997). Getting Started with the Market Research Application, Cary, NC: SAS Institute Inc.
[96] SAS Institute Inc. (1997). SAS/STAT Software: Changes and Enhancements through Release 6.12. NC: SAS Institute Inc.
[97] Schoemaker, P.J.H. (1982). “The expected utility model: its variants, purposes, evidence and limitations,” Journal of Economic Literature, Vol. 20, pp. 529-563.
[98] Schweizer, B. and Sklar, A. (1963). “Associative functions and abstract semi-groups,” Publicationes Mathematicae, Debrecen, Vol. 10, pp. 69-81.
[99] Simon, H.A. (1993). The Sciences of the Artificial 3rd Edition, pp. 131-138, The MIT Press.
[100] Slovic, P., Fischhoff, B., and Lichtenstein, S. (1976). “Cognitive processes and societal risk taking,” in J.S. Carroll and J.W. Payne (eds.), Cognition and Social Behavior. Hillsdale, NJ: Erlbaum.
[101] Smithson, M. (1987). Fuzzy Set Analysis for Behavioral and Social Sciences, Springer-Verlag, New York.
[102] Soete, G.D. and Carroll, J.D. (1992). “Probabilistic Multidimensional Models of Pairwise Choice Data,” in Ashby, F.G. (ed.) Multidimensional Models of Perception and Cognition, Lawrence Erlbaum Associates, Publishers, pp. 61-88.
[103] Stanovich, K.E. and West, R.F. (1999). “Discrepancies between normative and descriptive models of decision making and the understanding/acceptance principle,” Cognitive Psychology, Vol. 38, pp. 349–385.
[104] Sugeno, M. (1972). “Fuzzy measures and fuzzy integrals,” Trans. S.I.C.E., Vol. 8(2).
[105] Turksen, I.B., “Measurement of membership functions and their acquisition,” Fuzzy Sets and Systems, (40) 1991, pp. 5-38.
[106] Turksen, I.B., Stochastic fuzzy sets: A survey, in: Kacprzyk, J. and Fedrizzi, M. (eds.), Combining Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making, Springer-Verlag, New York, 1988, pp. 168-183.
[107] Tversky, A, Sattath, S., and Slovic, P. (1988). “Contingent weighting in judgment and choice,” Psychological Review, Vol. 95, pp. 371-384.
[108] Tversky, A. (1969). “Intransitivity of Preference,” Psychological Review, Vol. 76, pp. 31-48.
[109] Tversky, A. (1972). “Elimination by aspects: A theory of choice,” Psychological Review, Vol. 79, pp. 281-299.
[110] Tversky, A. and Kahneman, D. (1973). “Availability: a heuristic for judging frequency and probability,” Cognitive Psychology, Vol. 5(2), pp. 207-232.
[111] Tversky, A. and Kahneman, D. (1974). “Judgment under uncertainty: heuristics and biases,” Science, Vol. 185, pp. 1124-1131.
[112] Tversky, A., Sattath, S., and Slovic, P. (1988). “Contingent weighting in judgment and choice,” Psychological Review, Vol. 95, pp. 371-384.
[113] Tversky, A., Slovic, P., and Kahneman, D. (1990). “The causes of preference reversal,” American Economic Review, Vol. 80, pp. 204-217.
[114] Underwood, B.J. and Shaughnessy, J.J. (1975). Experimentation in Psychology. New York, Wiley.
[115] Wallsten, T.S. (1976). “Using conjoint-measurement models to investigate a theory about probabilistic information processing,” Journal of Mathematical Psychology, Vol. 14, pp. 144-185.
[116] Wallsten, T.S., Budescu, D.V. and Zwick, R. (1993). “Comparing the calibration and coherence of numerical and verbal probability judgments,” Management Science, Vol. 39(2), pp. 176-190.
[117] Ward, L.M. (1975). “Heuristics use or information integration in the estimation of subjective likelihood?” Bulletin of the Psychonomic Society, Vol. 6, pp. 43-46.
[118] Washio, T., Takahashi, H., and Kitamura, M. (1992). “A method for supporting decision making on plant operation based on human reliability analysis by fuzzy integral,” Proceedings 2nd International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, pp. 841-845.
[119] Yager, R.R. (1988). “On ordered weighted averaging aggregation operators in multicriteria decision making,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 18(1), pp. 183-190. new window

[120] Yager, R.R. (1993). “A general approach to criteria aggregation using fuzzy measures,” International Journal of Man-Machine Studies, Vol. 39, pp. 187-213.
[121] Yaniv, I. and Schul, Y. (2000). “Acceptance and elimination procedures in choice: noncomplementarity and the role of implied status quo,” Organizational Behavior and Human Processes, Vol. 82(2), pp. 293-313.
[122] Zadeh, L.A. (1965). “Fuzzy sets,” Information and Control, Vol. 8, pp. 338-353.
[123] Zadeh, L.A. (1975). “The Concept of a Linguistic Variable and Its Application to Approximate Reasoning,” Parts 1, 2, and 3, Information Science, Vol. 8, pp. 199-249; Vol. 8, pp. 301-357; Vol. 9, pp. 43-96. new window

[124] Zadeh, L.A. (1996). “Fuzzy Logic = Computing with Words,” IEEE Transactions on Fuzzy Systems, Vol. 4, pp. 103-111.
[125] Zimmer, A.C. (1983). “Verbal vs. numerical processing of subjective probabilities,” in Scholz, R.W. (ed.), Decision Making Under Uncertainty, New York: North-Holland, pp. 377-398.
[126] Zimmer, A.C. (1984). “A model for the interpretation of verbal predictions,” International Journal of Man-Machine Studies, Vol. 20, pp. 121-134.
[127] Zimmermann, H.-J. and Zysno, P. (1980). “Latent connectives in human decision making,” Fuzzy Sets and Systems, Vol. 4, pp. 37-51.
[128] Znotinas, N.M. and Hipel, K.W. (1979). “Comparison of alternative engineering designs,” Water Resources Bulletin, Vol. 15(1), pp. 44-59. new window

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