A Model for the Interpretation of Verbal Predictions

Abstract There is a marked gap between the demands on forecasting and the results that numerical forecasting techniques usually can provide. It is suggested that this gap can be closed by the implementation of experts' qualitative predictions into numerical forecasting systems. A formal analysis of these predictions can then be integrated into quantitative forecasts. In the framework of possibility theory, a model is developed which accounts for the verbal judgments in situations where predictions are made or knowledge is updated in the light of new information. The model translates verbal expressions into elastic constraints on a numerical scale. This numerical interpretation of qualitative judgments can then be implemented into numerical forecasting procedures. The applicability of this model was tested experimentally. The results indicate that the numerical predictions from the model agree well with the actual judgments and the evaluation behavior of the subjects. The applicability of this model is demonstrated in a study where bank clerks had to predict exchange rates. The analysis of qualitative judgments according to this model provided significantly more information than numerical predictions. A general framework for an interactive forecasting systems is suggested for further developments.

[1]  Myles I. Friedman,et al.  Human nature and predictability , 1981 .

[2]  William F. Butler,et al.  Methods and techniques of business forecasting , 1976 .

[3]  A. H. Murphy,et al.  Subjective Probability Forecasting: Some Real World Experiments , 1975 .

[4]  Ian Begg,et al.  On the Interpretation of Syllogisms , 1982 .

[5]  A. Tversky,et al.  On the psychology of prediction , 1973 .

[6]  A. Tversky,et al.  Judgment under Uncertainty , 1982 .

[7]  P. Meehl,et al.  Clinical versus Statistical Prediction. , 1955 .

[8]  J. Shanteau,et al.  Livestock judges: How much information can an expert use? , 1978 .

[9]  A. Caramazza,et al.  A Fuzzy Set Approach to Modifiers and Vagueness in Natural Language , 2005 .

[10]  L. Sjöberg.  Aided and unaided decision making: Improving intuitive judgement , 1982 .

[11]  John C. Chambers,et al.  An executive's guide to forecasting , 1984 .

[12]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[13]  L. Ross,et al.  Human Inference: Strategies and Shortcomings of Social Judgment. , 1981 .

[14]  R. Yager.  An introduction to applications of possibility theory (+ commentaries by L.A. Zadeh, W. Bandler, T. Saaty, A. Kandel, D. Dubois & H. Prade, R.M. Tong and M. Kochen) , 1982 .

[15]  W. Edwards,et al.  Conservatism in a simple probability inference task. , 1966, Journal of experimental psychology.

[16]  H. Carter.  Fuzzy Sets and Systems — Theory and Applications , 1982 .

[17]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[18]  G. Lindzey,et al.  Seer versus sign. , 1965 .

[19]  Edward H. Shortliffe,et al.  Computer-based medical consultations, MYCIN , 1976 .

[20]  Richard E. Nisbett,et al.  Human Inference: Strategies and Shortcomings of Social Judgement , 1981 .

[21]  Timothy D. Wilson,et al.  Telling more than we can know: Verbal reports on mental processes. , 1977 .

[22]  Douglas B. Lenat,et al.  The Nature of Heuristics , 1982, Artif. Intell..

[23]  Spyros Makridakis,et al.  Forecasting and Planning: An Evaluation , 1981 .