Advertisement

Fundamental Basics of the CPBS Approach

  • Julia Pet-Edwards
  • Yacov Y. Haimes
  • Vira Chankong
  • Herbert S. Rosenkranz
  • Fanny K. Ennever

Abstract

In this chapter we will examine four basic methodologies and decision tools that are utilized in the CPBS approach to decision making. The first is Bayesian decision analysis, which forms the heart of the CPBS approach. Tests and measurements that are used to identify or detect a property of interest are generally not perfect. When tests are biased or inaccurate, it is often advantageous to use more than one test. The interpretation of a combination of test results can be problematic because there often exists a variable amount of information overlap (positive dependence) and differences (negative dependence) among the tests. It is a difficult problem to account for both the imperfection of the individual tests as well as their interdependencies in their joint interpretation.

Keywords

Data Item Subjective Probability Fundamental Basic Decision Node Proximity Level 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderberg, M. R., 1973, Cluster Analysis for Applications, Academic Press, New York.Google Scholar
  2. Bellman, R. E., and Dreyfus, S. E., 1962, Applied Dynamic Programming, Princeton University Press, Princeton, New Jersey.Google Scholar
  3. Berger, J. O., 1985, Statistical Decision Theory and Bayesian Analysis, 2nd edition, Springer-Verlag, New York.Google Scholar
  4. Black, M., ed., 1975, Problems of Choice and Decision, proceedings of a colloquium in Aspen, Colorado, 24 June-6 July 1974, Cornell University Program on Science, Technology, and Society, Ithaca, New York.Google Scholar
  5. Buchanan, J. T., 1982, Discrete and Dynamic Decision Analysis, Wiley-Interscience, Chichester, England.Google Scholar
  6. Chankong, V., and Haimes, Y. Y., 1983, Multiobjective Decision Making: Theory and Methodology, Elsevier, North-Holland, New York.Google Scholar
  7. Chankong, V., Haimes, Y. Y., Rosenkranz, H. R., and Pet-Edwards, J., 1985, “The carcinogenicity prediction and battery selection (CPBS) method: A Bayesian approach,” Mutation Res. 153:135–166.Google Scholar
  8. Clifford, H. T., and Stephenson, W., 1975, An Introduction to Numerical Classification, Academic Press, New York.Google Scholar
  9. Churchman, C. W., 1968, The Systems Approach, Dell, New York.Google Scholar
  10. Dubes, R., and Jain, A. K., 1979, Clustering Methodologies in Exploratory Data Analysis, Department of Computer Science, Michigan State University, East Lansing, Michigan.Google Scholar
  11. Easton, A., 1973, Complex Managerial Decisions Involving Multiple Objectives, Wiley, New York.Google Scholar
  12. Finney, D., 1971, Probit Analysis, Cambridge University Press, Cambridge, England.Google Scholar
  13. Gnanadesikan, R., Kettenring, J. R., and Landwehr, J. M., 1977, “Interpreting and assessing the results of cluster analysis,” Bull. Int. Statist. Inst., 47:451–463.Google Scholar
  14. Goldstein, N., and Dillon, W. R., 1978, Discrete Discriminant Analysis, Wiley, New York.Google Scholar
  15. Good, I. J., 1978, “Alleged objectivity: A threat to the human spirit,” International Statistical Review, 46:65–66.CrossRefGoogle Scholar
  16. Haimes, Y. Y., and Hall, W. A., 1974, “Multiobjectives in water resources systems analysis: The surrogate worth trade-off method,” Water Resources Res., 10:615–623.CrossRefGoogle Scholar
  17. Haimes, Y. Y., Hall, W. A., and Freedman, H. T., 1975, Multiobjective Optimization in Water Resources Systems. The Surrogate Worth Trade-Off Method, Elsevier, New York.Google Scholar
  18. Hamaker, H. C., 1977, “Bayesianism: A Threat to the statistical profession?” Int. Stat. Rev., 45:111–115.CrossRefGoogle Scholar
  19. Holladay, C., 1979, Decision Making Under Uncertainty: Choices and Models, Prentice-Hall, Englewood Cliffs, New Jersey.Google Scholar
  20. Intriligator, M. D., 1971, Mathematical Optimization and Economic Theory, Prentice-Hall, Englewood Cliffs, New Jersey.Google Scholar
  21. Keeney, R., and Raifia, H., 1976, Decisions with Multiple Objectives, Wiley, New York.Google Scholar
  22. Koopmans, T. C., 1951, “Analysis of production as an efficient combination of activities,” in Activity Analysis of Production and Allocation (T. C. Koopmans, ed.), Wiley, New York, pp. 33–97.Google Scholar
  23. Kuhn, H. W., and Tucker, A. W., 1950, Contributions to the Theory of Games, Vol. 1, Princeton University Press, Princeton, New Jersey.Google Scholar
  24. Lindley, D. V., 1985, Making Decisions, 2nd edition, Wiley, London.Google Scholar
  25. Ling, R. F., 1972, “On the theory and construction of k-clusters,” Comput. J., 15:326–332.CrossRefGoogle Scholar
  26. Ling, R. F., 1973, “Probability theory of cluster analysis,” J. Am. Stat. Assoc, 68:159–164.Google Scholar
  27. McKelvey, R. D., and Zavoina, W., 1975, “A statistical model for the analysis of ordinal level dependent variables,” J. Math. Sociol., 4:103–120.CrossRefGoogle Scholar
  28. Meisel, W. S., 1972, Computer-Oriented Approaches to Pattern Recognition, Academic Press, New York.Google Scholar
  29. Moore, P. G., 1978, “The mythical threat of Bayesianism,” Int. Stat. Rev., 46:67–73.CrossRefGoogle Scholar
  30. Nemhauser, G. L., 1966, Introduction to Dynamic Programming, Wiley, New York.Google Scholar
  31. Pet-Edwards, J., Rosenkranz, H. R., Chankong, V., and Haimes, Y. Y., 1985, “Cluster analysis in predicting the carcinogenicity of chemicals using short-term assays,” Mutation Res., 153:167–185.Google Scholar
  32. Raiffa, H., 1968, Decision Analysis: Introductory Lectures on Choices Under Uncertainty, Addison-Wesley, Reading, Massachusetts.Google Scholar
  33. Rigby, F. D., 1964, Heuristic analysis of decision situations, in Human Judgments and Optimality (M. W. Shelly II and G. L. Bryan, eds.), Wiley, New York.Google Scholar
  34. Rosenkranz, H. R., Klopman, G., Chankong, V., Pet-Edwards, J., and Haimes, Y. Y., 1984, “Prediction of environmental carcinogens: A strategy for the mid 1980’s,” Environ. Mutagen., 6:231–258.CrossRefGoogle Scholar
  35. Savage, L. J., 1962, “Subjective probability and statistical practice,” in The Foundation of Statistical Inference (L. J. Savage, ed.), Methuen, London, pp. 9–35 (discussion pp. 62-103).Google Scholar
  36. Savage, L. J., 1954, The Foundation of Statistics, Wiley, New York.Google Scholar
  37. Smith, S. P., and Dubes, R. C., 1979, “The stability of hierarchical clustering,” Technical Report No. TR-79-02, Computer Science Department, Michigan State University, East Lansing, Michigan.Google Scholar
  38. Steuer, R. E., 1986, Multiple Criteria Optimization: Theory, Computation, and Application, Wiley, New York.Google Scholar
  39. Von Neumann, J., and Morgenstern, O., 1953, Theory of Games and Economic Behavior, 3rd. edition, Princeton University Press, Princeton.Google Scholar
  40. Weinstein, M. C., Feinberg, H. V., Elstein, A. S., Frazier, H. S., Neuhauser, D., Neutra, R. R., and McNeil, B. J., 1980, Clinical Decision Analysis, W. B. Saunders, Philadelphia.Google Scholar
  41. Winkler, R. L., 1972, Introduction to Bayesian Inference and Decision, Holt, Reinhart, and Winston, New York.Google Scholar
  42. Yu, P. L., 1985, Multiple-Criteria Decision Making, Concepts, Techniques, and Extensions, Plenum Press, New York.Google Scholar

Copyright information

© Plenum Press, New York 1989

Authors and Affiliations

  • Julia Pet-Edwards
    • 1
  • Yacov Y. Haimes
    • 1
  • Vira Chankong
    • 2
  • Herbert S. Rosenkranz
    • 2
  • Fanny K. Ennever
    • 2
  1. 1.University of VirginiaCharlottesvilleUSA
  2. 2.Case Western Reserve UniversityClevelandUSA

Personalised recommendations