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Words about Uncertainty: Analogies and Contexts

  • Michael J. Smithson
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 33)

Abstract

The study of uncertainty in many fields has been beset by debate and even confusion over the meaning(s) of uncertainty and the words that are used to describe it. Normative debates address questions such as whether there is more than one kind of uncertainty and how verbal descriptions of uncertainty ought to be used. Descriptive research, which we shall deal with in this paper, concerns how people actually use words to describe uncertainty and the distinct meanings they apply to those words. The main reason for what might seem an obvious statement is to clarify the somewhat odd context in which most studies of decision making take place.

Keywords

Context Effect Organizational Behavior Subjective Probability Prospect Theory Framing Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Bass, B.M., Cascio, W.F., and O’Connor, E.J. (1974). “Magnitude estimation of expression of frequency and amount.” Journal of Applied Psychology, 59, 313–320.CrossRefGoogle Scholar
  2. Bell, D. E. (1985). Disappointment in decision making under uncertainty. Operations Research, 33, 1–27.MathSciNetCrossRefGoogle Scholar
  3. Beyth-Marom, R. (1982). How probable is probable? A numerical translation of verbal probability expressions. Journal of Forecasting, 1, 257–269.CrossRefGoogle Scholar
  4. Boettcher, W.A. (1995). Context, methods, numbers, and words: Prospect theory in international relations. Journal of Conflict Resolution. 39, 561–583.CrossRefGoogle Scholar
  5. Bonini, N. and Caverni, J.-P. (1995). The “catch-all underestimation bias”: Availability hypothesis vs. category redefinition hypothesis. Current Psychology of Cognition. 14, 301–322.Google Scholar
  6. Brun, W. and Teigen, K.H. (1988) Verbal probabilities: ambiguous, context-dependent, or both? Organizational Behavior and Human Decision Processes. 41, 390–404.CrossRefGoogle Scholar
  7. 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. 14, 281–294.CrossRefGoogle Scholar
  8. Budescu, D.V. and Wallsten, T.S. (1985). Consistency in interpretation of probabilistic phrases. Organizational Behavior and Human Decision Processes, 36, 391–405.CrossRefGoogle Scholar
  9. Cohen, J., Dearnaley, E.J., and Hansel, C.E.M. (1958). Skill and chance: Variations in estimates of skill with an increasing element of chance. British Journal of Psychology, 49, 319–323.CrossRefGoogle Scholar
  10. Curley, S.P., Yates, J.F., and Abrams, R.A. (1986). Psychological sources of ambiguity avoidance. Organizational Behavior and Human Decision Processes, 38, 230–256.CrossRefGoogle Scholar
  11. Dube-Rioux, L. and Russo, J.E. (1988). An availability bias in professional judgment. Journal of Behavioral Decision Making. 1, 223–237.CrossRefGoogle Scholar
  12. Dusenbury, R. and M. G. Fennema, M.G. (1996). Linguistic-Numeric Presentation Mode Effects on Risky Option Preferences. Organizational Behavior and Human Decision Processes. 68, 109–122.CrossRefGoogle Scholar
  13. Einhorn, H. J. and Hogarth, R. M. (1985). Ambiguity and uncertainty in probabilistic inference. Psychological Review. 92, 433–461.CrossRefGoogle Scholar
  14. Ellsberg, D. (1961). Risk, ambiguity, and the Savage axioms. Quarterly Journal of Economics. 75, 643–669.CrossRefGoogle Scholar
  15. Erev, I. and Cohen, B.L. (1990). Verbal versus numerical probabilities: Efficiency, biases, and the preference paradox. Organizational Behavior and Human Decision Processes. 45, 1–18.CrossRefGoogle Scholar
  16. Evans, J.St.B.T. (1993) The mental model theory of conditional reasoning: Critical appraisal and revision. Cognition. 48, 1–20.CrossRefGoogle Scholar
  17. Fillenbaum, S., Wallsten, T.S., Cohen, B.L. and Cox, J.A. (1991). Some effects of vocabulary and communication task on the understanding and use of vague probability expressions. American Journal of Psychology. 104, 35–60.CrossRefGoogle Scholar
  18. Fischhoff, B., Slovic, P, and Lichtenstein, S. (1978). Fault trees: Sensibility of estimated failure probabilities to problem representation. Journal of Experimental Psychology: Human Perception and Performance. 4, 330–344.CrossRefGoogle Scholar
  19. Gilovich. T. and Medvec, V.H. (1995). The experience of regret: What, when, and why. Psychological Review. 102, 379–395.Google Scholar
  20. Gonzalez-Vallejo, C.C., Erev, I. and Wallsten, T.S. (1994). Do decision quality and preference order depend on whether probabilities are verbal or numerical? American Journal of Psychology. 107, 157–172.CrossRefGoogle Scholar
  21. Gonzalez-Vallejo, C.C. and Wallsten, T.S. (1992). Effects of probability mode on preference reversal. Journal of Experimental Psychology: Learning, Memory, and Cognition. 18, 855–864.Google Scholar
  22. Hakel, M. (1968). How often is often? American Psychologist, 23, 533–534.CrossRefGoogle Scholar
  23. Hamm, R.M. (1991). Selection of verbal probabilities: A solution for some problems of verbal probability expressions. Organizational Behavior and Human Decision Processes. 48, 193–223.CrossRefGoogle Scholar
  24. Highhouse, S. and Yiice, P. (1996) Perspectives, Perceptions, and Risk-Taking Behavior. Organizational Behavior and Human Decision Processes, 65, 159–167.CrossRefGoogle Scholar
  25. Hirt, E.R. and Castellan, N.J. Jr. (1988). Probability and category redefinition in the fault tree paradigm. Journal of Experimental Psychology: Human Perception and Performance. 20, 17–32.Google Scholar
  26. Kahneman, D. and Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.MATHCrossRefGoogle Scholar
  27. Keynes, J. M. (1921). A Treatise on Probability. London: Macmillan.MATHGoogle Scholar
  28. Krause, P. and Clark, D. (1993). Representing Uncertain Knowledge: An Artificial Intelligence Approach. Oxford: Intellect.MATHCrossRefGoogle Scholar
  29. Kuhn, K.M. (1997) Communicating Uncertainty: Framing Effects on Responses to Vague Probabilities. Organizational Behavior and Human Decision Processes, 71, 55–83.CrossRefGoogle Scholar
  30. Lichtenstein, S. and Newman, J.R. (1967). Empirical scaling of common verbal phrases associated with numerical probabilities. Psychonomic Sciences, 9, 563–564.Google Scholar
  31. Lichtenstein, S. and Slovic, P. (1971). “Reversal of preference between bids and choices in gambling decisions.” Journal of Experimental Psychology, 89, 46–55.CrossRefGoogle Scholar
  32. Lipshitz, R. and Strauss, O. (1997). Coping with Uncertainty: A Naturalistic Decision-Making Analysis. Organizational Behavior and Human Decision Processes, 69, 149–163.CrossRefGoogle Scholar
  33. Loomes, G. and Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. Economic Journal. 92, 805–824.CrossRefGoogle Scholar
  34. Molenaar, N.J. (1982). Response effects of ‘formal’ characteristics of questions. In W. Dijkstra and J. van der Zouwen (eds.), Response Behavior and the Survey Interview. N.Y.: Academic Press.Google Scholar
  35. Newstead, S.E. (1988). Quantifiers as fuzzy concepts. In T. Zetenyi (ed.) Fuzzy Sets in Psychology. Amsterdam: North-Holland, 51–72.CrossRefGoogle Scholar
  36. Payne, S.L. (1951). The Art of Asking Questions. Princeton: Princeton University Press.Google Scholar
  37. Pepper, S. (1981). Problems in the quantification of frequency expressions. In D. Fiske (ed.) New Directions for Methodology of Social and Behavioral Sciences: Problems with Language Imprecision. San Francisco: Jossey-Bass.Google Scholar
  38. Pepper, S. and Prytulak, L.S. (1974). Sometimes frequently means seldom: Context effects in the interpretations of quantitative expressions. Journal of Research in Personality, 8, 95–101.CrossRefGoogle Scholar
  39. Quiggin, J. (1982). A theory of anticipated utility. Journal of Economic Behavior and Organization. 3, 323–343.CrossRefGoogle Scholar
  40. Rachlin, H. (1989). Judgment, Decision, and Choice. N.Y.: Freeman.Google Scholar
  41. Regan, R.T., Mosteller, F. and Youtz, C. (1990). Quantitative meanings of verbal probability expressions. Journal of Applied Psychology. 74, 433–442.CrossRefGoogle Scholar
  42. Rottenstreich, Y. and Tversky, A. (1997). Unpacking, repacking, and anchoring: Advances in support theory. Psychological Review. 104, 406–415.CrossRefGoogle Scholar
  43. Russo, J.E. and Kozlow, K. (1994). Where is the fault in fault trees? Journal of Experimental Psychology: Human Perception and Performance. 20, 17–32.CrossRefGoogle Scholar
  44. Simpson, R.H. (1944). The specific meanings of certain terms indicating differing degrees of frequency. Quarterly Journal of Speech, 30, 328–330.CrossRefGoogle Scholar
  45. Simpson, R.H. (1963). Stability in meanings for quantitative terms: A comparison over 20 years. Quarterly Journal of Speech, 49, 146–151.CrossRefGoogle Scholar
  46. Smithson, M. (1987). Fuzzy Set Analysis for Behavioral and Social Sciences. New York: Springer Verlag.CrossRefGoogle Scholar
  47. Smithson, M. (1989). Ignorance and Uncertainly: Emerging Paradigms. New York: Springer-Verlag.CrossRefGoogle Scholar
  48. Smithson, M. (1997). Conflict Aversion. Working paper, Division of Psychology, The Australian National University.Google Scholar
  49. Smithson, M. and Bartos, T. (1997). Judgment under Outcome Ignorance. Working paper, Division of Psychology, The Australian National University.Google Scholar
  50. Stone, D.R. and Johnson, R.J. (1959). A study of words indicating frequency. Journal of Educational Psychology, 50, 224–227.CrossRefGoogle Scholar
  51. Teigen, K.H. (1988). When are low-probability events judged to be ‘probable’? Effects of outcome-set characteristics on verbal probability judgments. Acta Psychologica. 67, 157–174.CrossRefGoogle Scholar
  52. Teigen, K.H. (1994). Variants of subjective probabilities: Concepts, norms, and biases. In G. Wright and P. Ayton (eds.) Subjective Probability. Chichester: Wiley, 211–238.Google Scholar
  53. Teigen, K.H. and Brun, W. (1995). Yes, but it is uncertain: Direction and communicative intention of verbal probabalistic terms. Acta-Psychologica. 88, 233–258.CrossRefGoogle Scholar
  54. Tversky, A. and Kahneman, D. (1981). The framing of decisions and the rationality of choice. Science, 221, 453–458.MathSciNetCrossRefGoogle Scholar
  55. Tversky, A. and Koehler, D. J. (1994). Support theory: a nonextensional representation of subjective probability. Psychological Review. 101, 547–567.CrossRefGoogle Scholar
  56. Tversky, A., Slovic, P. and Kahneman, D. (1990). The causes of preference reversal. The American Economic Review. 80, 204–217.Google Scholar
  57. Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. London: Chapman and Hall.MATHGoogle Scholar
  58. Walley, P. (1996). Inferences from multinonval data: Learning about a bag of marbles. (with discussion) Journal of the Royal Statistical Society, Series B. 58, 3–57.MathSciNetMATHGoogle Scholar
  59. Wallsten, T.S., Budescu, D.V. and Erev, I. (1988). Understanding and using linguistic uncertainties. Acta Psychologica. 68, 39–52.CrossRefGoogle Scholar
  60. Wallsten, T.S., Fillenbaum, S. and Cox, J.A. (1986). Base-rate effects on the interpretation of probability and frequency expressions. Journal of Memory and Language, 25, 571–581CrossRefGoogle Scholar
  61. Wallsten, T.S., Budescu, D.V., Zwick, R. and Kemp, S.M. (1993). Preferences and reasons for communicating probabilistic information in verbal or numerical terms. Bulletin of the Psychonomic Society. 31, 135–138.Google Scholar
  62. Wallsten, T.S., Budescu, D., Rappoport, A., Zwick, R., and Forsyth, B. (1986). Measuring the vague meanings of probability terms. Journal of Experimental Psychology: General, 115, 348–365.CrossRefGoogle Scholar
  63. Zimmer, A.C. (1983). Verbal vs. numerical processing of subjective probabilities. In R.W. Scholz (ed.) Decision Making Under Uncertainty. Amsterdam: North-Holland.Google Scholar
  64. Zimmer, A.C. (1984). A model for the interpretation of verbal predictions. International Journal of Man-Machine Studies, 20, 121–134.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Michael J. Smithson
    • 1
  1. 1.Division of PsychologyAustralian National UniversityCanberraAustralia

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