Expert Systems’ Front End: Expert Opinion

  • Roger M. Cooke
Conference paper
Part of the NATO ASI Series book series (volume 48)


Whereas computers have traditionally excelled in deductive reasoning, most areas of science, engineering, medicine and policy analysis are dominated by inductive, or inexact reasoning. Uncertain conclusions are drawn from uncertain evidence via uncertain rules of inference. The ability to reason with uncertainty is sometimes considered the hallmark of human rationality. With the advent of expert systems, computer science has applied itself to the task of representing and implementing inexact reasoning in computer programs.


Expert System Expert Opinion Failure Probability Subjective Probability Probabilistic Assessment 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Christensen-Szalanski, J. and Bushyhead, J. “Physicians’ use of probabilistic information in a real clinical setting” Journal of Experimental Psychology: Human Perception and Performance. 7, 928–935, 1981.CrossRefGoogle Scholar
  2. Cooke, R. Mendel, M. and Thijs, W. Calibration and Information in expert resolution Automatica, Januari 1988.Google Scholar
  3. Cooke, R. “A theory of weights for combining expert opinion” Delft University of Technology, Report 87-25, 1987.Google Scholar
  4. Fisher, G.W. “When oracles fail — a comparison of four procedures for aggregating subjective probability forecasts” Organizational Behavior and Human Performance, 28 69–110, 1981.Google Scholar
  5. Fuller, J. We Almost Lost Detroit Ballantine Books, New York, 1975.Google Scholar
  6. Genest, C. and Zidek, J. Combining probability distributions: a critique and annotated bibliography, Statistical Science, vol. 1 nr. 1, 114–148, 1986.CrossRefMathSciNetGoogle Scholar
  7. De Groot, M. and Fienberg, S. Comparing probability forecasters: basic binary concepts and multivariate extensions. In Bayesian Inference and Decision Techniques, P. Goel and A. Zellner (eds), Elsevier, 1986.Google Scholar
  8. Lichtenstein, S. Fischhoff, B. and Phillips, D. Calibration of probabilities: the state of the art to 1980, In D. Kahneman, P. Slovic, and A. Tversky (eds) Judgment under uncertainty: heuristics and biases. Cambridge, Cambridge University Press 306–335, 1982.Google Scholar
  9. Lindley, D. and Singpurwalla, N. “Reliability and fault tree analysis using expert opinion” J. Amer. Stat. Assoc. 81/393 87–90, 1986.CrossRefMATHMathSciNetGoogle Scholar
  10. Martz, H. and Bryson, M. “On combining data for estimating the frequency of low probability events…”Nuclear Science and Engineering 83, 267–280, 1983.Google Scholar
  11. McConway, K. “Marginalization and linear opinion pools” J. Amer. Statis.Assoc. 76. 410–411, 1981.CrossRefMATHMathSciNetGoogle Scholar
  12. Morris, P. Combining expert judgments: A Bayesian approach, Management Science vol 23, nr. 7, 679–693, 1977.CrossRefMATHGoogle Scholar
  13. Mosleh A. and Apostolakis, G. “Models for the use of expert opinion” Low Probability High Consequence Risk Analysis (ed. Waller & Covello) Plenum Press, New York, 107–124, 1984.Google Scholar
  14. Murphy, A. A new vector partition of the probability score, .J. of Applied Meteorology, 12 595–600, 1973.CrossRefGoogle Scholar
  15. Murphy, A. and Daan, H. “Subjective probability forecasting in the Netherlands, some operational and experimental results” Meteorolog. Rundschau 34/5 99–112. 1982.Google Scholar
  16. Murphy, A. and Daan, H. “Impacts of feedback and experience on the quality of subjective probability forecasts: comparison of results from first and second years of the Zierikzee experiment” Mon. Weather Rev. 112/3 413–423, 1984.CrossRefGoogle Scholar
  17. Reactor Safety Study WASH 1400, NUREG-75/014, 1975.Google Scholar
  18. Reactor Risk Reference Document NUREG/1150, 1987.Google Scholar
  19. Roberts, H. Probabilistic prediction, J. Amer. Statist. Assoc. 60, 50–62, 1965.CrossRefMATHMathSciNetGoogle Scholar
  20. Sackman, H. Delphi Critique. Expert Opinion, Forecasting and Group Processes. Lexington Books, 1975.Google Scholar
  21. Savage, L. Elicitation of personal probabilities and expectations, J. Amer. Statis. Assoc. vol. 66, nr. 336, 783–801, 1971Google Scholar
  22. Seaver. D. “How groups can assess uncertainty: human interaction versus mathematical models” Proc. int. conf. cybernetics and soc. Washington DC. 19–21 sept. 1977.Google Scholar
  23. Shooman and Sinkar, “Generation of reliability and safety data by analysis of expert opinion” Proceedings 1977 Annual Reliability and Maintainability Symposium, p. 186–191.Google Scholar
  24. Shuford, E. Albert, A. and Massengill, H. Admissible probability measurement procedures. Psychometrika. vol 31, nr. 2 125–145, 1966.CrossRefMATHGoogle Scholar
  25. Wagner, C. “Allocation, Lehrer Models and the consensus of probabilities” Theory and Decision. 14, 207–220, 1982.CrossRefMATHMathSciNetGoogle Scholar
  26. Winkler, R. and Murphy, A. “Reliability of subjective probability forecasts of precipitation and temperature” Appl. Stat 26/1 41–471977.CrossRefGoogle Scholar
  27. Winkler, R. and Murphy, A. “Good probability assessors”, J. of Applied Meteorology, vol. 7. 751–758, 1968.CrossRefGoogle Scholar
  28. Winkler, R. The consensus of subjective probability distributions, Management Science, vol. 15 nr. 2, 861–875, 1968.CrossRefGoogle Scholar
  29. Winkler, R. Scoring rules and the evaluation of probability assessors, J. Amer. Statist. Ass. 64, 1073–1078, 1969.CrossRefGoogle Scholar
  30. Winkler, R. On “good probability appraisers”. In Bayesian Inference and Decision Techniques. P. Goel and A. Zellner (eds) Elsevier 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Roger M. Cooke
    • 1
  1. 1.Department of Mathematics and InformaticsDelft University of TechnologyThe Netherlands

Personalised recommendations