Encyclopedia of Operations Research and Management Science

2001 Edition
| Editors: Saul I. Gass, Carl M. Harris

Decision analysis

  • David A. Schum
Reference work entry
DOI: https://doi.org/10.1007/1-4020-0611-X_215

INTRODUCTION

The term decision analysisidentifies a collection of technologies for assisting individuals and organizations in the performance of difficult inferences and decisions. Probabilistic inference is a natural element of any choice made in the face of uncertainty. No single discipline can lay claim to all advancements made in support of these technologies. Operations research, probability theory, statistics, economics, psychology, artificial intelligence, and other disciplines have contributed valuable ideas now being exploited in various ways by individuals in many governmental, industrial, and military organizations. As the term decision analysis suggests, complex inference and choice tasks are decomposed into smaller and presumably more manageable elements, some of which are probabilistic and others preferential or value-related. The basic strategy employed in decision analysis is “divide and conquer.” The presumption is that individuals or groups find it more difficult to...

This is a preview of subscription content, log in to check access.

References

  1. [1]
    Breese, J. and Heckerman, D. (1999). “Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment.” In Shanteau, J., Mellers, B., and Schum, D., eds. Decision Science and Technology: Reflections on the Contributions of Ward Edwards. Kluwer Academic Publishers, Boston, Massachusetts, 271–287.Google Scholar
  2. [2]
    Clemon, R.T. (1991). Making Hard Decisions: An Introduction to Decision Analysis, PWS-Kent Publishing Co., Boston.Google Scholar
  3. [3]
    Cohen, L.J. (1977). The Probable and the Provable, Clarendon Press, Oxford.Google Scholar
  4. [4]
    Cohen, L.J. (1989). An Introduction to the Philosophy of Induction and Probability, Clarendon Press, Oxford.Google Scholar
  5. [5]
    De Finetti, B. (1972). Probability, Induction, and Statistics: The Art of Guessing, John Wiley, New York.Google Scholar
  6. [6]
    Dreyfus, S. (1984). “The Risks ! and Benefits ? of Risk-Benefit Analysis,” Omega, 12, 335–340.Google Scholar
  7. [7]
    Edwards, W. (1954). “The Theory of Decision Making,” Psychological Bulletin, 41, 380–417.Google Scholar
  8. [8]
    Edwards, W. (1961). “Behavioral Decision Theory,” Annual Review Psychology, 12, 473–498.Google Scholar
  9. [9]
    Edwards, W. (1962). “Dynamic Decision Theory and Probabilistic Information Processing,” Human Factors, 4, 59–73.Google Scholar
  10. [10]
    Fishburn, P. (1999). “The Making of Decision Theory.” In Shanteau, J., Mellers, B., and Schum, D., eds. Decision Science and Technology: Reflections on the Contributions of Ward Edwards. Kluwer Academic Publishers, Boston, Massachusetts, 369–388.Google Scholar
  11. [11]
    Howard, R. (1966). “Decision Analysis: Applied Decision Theory,” in Hertz, D.B. and Melese, J., eds., Proceedings Fourth International Conference on Operational Research, Wiley-Interscience, New York.Google Scholar
  12. [12]
    Howard, R. (1968). “The Foundations of Decision Analysis,” IEEE Transactions on Systems Science and Cybernetics, SSC-4, 211–219.Google Scholar
  13. [13]
    Howard, R. and Matheson, J. (1981). “Influence Diagrams,” in Howard, R. and Matheson, J., The Principles and Applications of Decision Analysis, Vol. II, Strategic Decisions Group, Menlo Park, California, 1984.Google Scholar
  14. [14]
    Keeney, R. (1992). Value-Focused Thinking. Harvard University Press, Cambridge, Massachusetts.Google Scholar
  15. [15]
    Keeney, R. and Raiffa, H. (1976). Decision With Multiple Objectives: Preferences and Value Tradeoffs, John Wiley, New York.Google Scholar
  16. [16]
    Mellor, D.H. (1990). F.P. Ramsey: Philosophical Papers, Cambridge University Press, Cambridge.Google Scholar
  17. [17]
    Neapolitan, R. (1990). Probabilistic Reasoning in Expert Systems: Theory and Algorithms, John Wiley, New York.Google Scholar
  18. [18]
    Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Reasoning, Morgan Kaufmann Publishers, San Mateo, California.Google Scholar
  19. [19]
    Phillips, L. (1982). “Requisite Decision Modelling: A Case Study,” Jl. Operational Research Society, 33, 303–311.Google Scholar
  20. [20]
    Phillips, L. (1984). “A Theory of Requisite Decision Models,” Acta Psychologica, 56, 29–48.Google Scholar
  21. [21]
    Raiffa, H. (1968). Decision Analysis: Introductory Lectures on Choices Under Uncertainty, Addison-Wesley, Reading, Massachusetts.Google Scholar
  22. [22]
    Rescher, N. (1988). Rationality: A Philosophical Inquiry Into the Nature and Rationale of Reason, Clarendon Press, Oxford.Google Scholar
  23. [23]
    Sage, A. (1991). Decision Support Systems Engineering, John Wiley, New York.Google Scholar
  24. [24]
    Savage, L.J. (1954). The Foundations of Statistics, John Wiley, New York.Google Scholar
  25. [25]
    Schum, D. (1990). “Inference Networks and Their Many Subtle Properties,” Information and Decision Technologies, 16, 69–98.Google Scholar
  26. [26]
    Schum, D. (1994). Evidential Foundations of Probabilistic Reasoning, John Wiley, New York.Google Scholar
  27. [27]
    Shachter, R. (1986). “Evaluating Influence Diagrams,” Operations Research, 34, 871–882.Google Scholar
  28. [28]
    Shachter, R. and Heckerman, D. (1987). “Thinking Backward for Knowledge Acquisition,” AI Magazine, Fall, 55–61. Google Scholar
  29. [29]
    Shafer, G. (1976). A Mathematical Theory of Evidence, Princeton University Press, New Jersey.Google Scholar
  30. [30]
    Shafer, G. (1986). “Savage Revisited,” Statistical Science, 1, 463–501 (with comments).Google Scholar
  31. [31]
    Shanteau, J., Mellers, B., and Schum, D. (1999). Decision Science and Technology: Reflections on the Contributions of Ward Edwards. Kluwer Academic Publishers, Boston, Massachusetts.Google Scholar
  32. [32]
    Smith, J.Q. (1988). Decision Analysis: A Bayesian Approach, Chapman and Hall, London.Google Scholar
  33. [33]
    Tocher, K. (1977). “Planning Systems,” Philosophical Transactions Royal Society London, A287, 425–441.Google Scholar
  34. [34]
    Twining, W. (1990). Rethinking Evidence: Exploratory Essays, Basil Blackwell, Oxford.Google Scholar
  35. [35]
    von Neumann, J. and Morgenstern, O. (1947). Theory of Games and Economic Behavior, Princeton University Press, New Jersey.Google Scholar
  36. [36]
    von Winterfeldt, D. and Edwards, W. (1986). Decision Analysis and Behavioral Research, Cambridge University Press, Cambridge.Google Scholar
  37. [37]
    Watson, S.R. and Buede, D. (1987). Decision Synthesis: The Principles and Practice of Decision Analysis, Cambridge University Press, Cambridge.Google Scholar
  38. [38]
    Watson, S.R., Weiss, J.J., and Donnell, M.L. (1979). “Fuzzy Decision Analysis,” IEEE Transactions on Systems, Man, and Cybernetics, SMC-9(1), 1–9.Google Scholar
  39. [39]
    Winkler, R.L. (1972). Introduction to Bayesian Inference and Decision, Holt, Rinehart, and Winston, New York.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • David A. Schum
    • 1
  1. 1.George Mason UniversityFairfaxUSA