Encyclopedia of Operations Research and Management Science

2013 Edition
| Editors: Saul I. Gass, Michael C. Fu

Influence Diagrams

  • James E. Matheson
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-1153-7_1160

Introduction

Before influence diagrams were developed, describing and solving decision problems under uncertainty was quite difficult. The first difficulty was determining the probabilistic relationship among uncertain variables, because it is easy to model many variables as jointly related, but extremely difficult to assess their probabilistic relationship. An experienced decision analyst can reasonably assess the uncertainty in a single variable (Spetzler and Staël von Holstein 1975), but more than two variables make the task almost impossible. A better way was needed to understand the relationship among uncertain variables. The second difficulty was understanding and describing the relationship between decisions and uncertainties, particularly indicating which uncertainties would be revealed before which decisions and then transforming the probabilistic descriptions to condition the probabilities in the proper order of information revelation, using Bayes’ Rule. The usual, but very...

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

References

  1. Howard, R. (1965). Bayesian models for systems engineering. IEEE Transactions on SystemsScience and Cybernetics, Vol. SSC-1 No. 1, 36–40.Google Scholar
  2. Howard, R. A. (1989). Knowledge maps. Management Science, 35(8), 903–922.CrossRefGoogle Scholar
  3. Howard, R. A. (1990). From influence to relevance to knowledge. In R. M. Oliver, & J. Q. Smith (Eds.), Influence diagrams, belief netsand decision analysis, Proceedings, May 1988 conference (pp. 3–23). New York: John Wiley & Sons.Google Scholar
  4. Howard, R., & Matheson, J. (1983). Influence diagrams. In R. A. Howard & J. E. Matheson (Eds.), Readings on the principles and applications of decision analysis (pp. 719–763). Menlo Park, CA: Strategic Decisions Group.Google Scholar
  5. Howard, R., & Matheson, J. (2005a). Influence diagrams. Reprinted in the special issue on graphical methods. Decision Analysis, 2 (3), 127–143.Google Scholar
  6. Howard, R. A., & Matheson, J. E. (2005b). Influence diagrams retrospective. Special issue on graphical methods. Decision Analysis, 2 (3), 144–147.Google Scholar
  7. Howard, R. A., Matheson, J. E., Merkhofer, M. W. (lee), Miller, A. C., & Warner North, D. (2006). Comment on influence diagram retrospective. Decision Analysis, 3 (2), 117–119.Google Scholar
  8. Howard, R., Matheson, J., Merkhofer, M., Miller III, A., and Rice, T. (1976). Development of automated aidsfor decision analysis. DARPA Contract MDA 903-74-C-0240. Menlo Park, CA: SRI International.Google Scholar
  9. Matheson, J. (1983). Managing the corporate business portfolio. In R. A. Howard & J. E. Matheson (Eds.), Readings on the principles and applications of decision analysis (pp. 311–326). Menlo Park, CA: Strategic Decisions Group.Google Scholar
  10. Matheson, J. (1990). Using influence diagrams to value information and control. In R. M. Oliver, & J. Q. Smith (Eds.), Influence diagrams, belief netsand decision analysis. Proceedings, May 1988 conference (pp. 25–63). New York: John Wiley & Sons.Google Scholar
  11. Matheson, D., & Matheson, J. (2005). Describing and valuing interventions that observe or control decision situations. In the special issue on graphical methods. Decision Analysis 2 (3), 165–181.Google Scholar
  12. Matheson, D., & Matheson, J. (1998). The smart organization, creating value through strategic R&D. Cambridge, MA: Harvard Business School Press.Google Scholar
  13. Neapolitan, R. (2004). Learning Bayesian networks. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  14. Olmsted, S. (1983). On representing and solvingdecision problems. Ph.D. thesis, EES Department, Stanford University, Stanford, CA.Google Scholar
  15. Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 29, 24l–288l.CrossRefGoogle Scholar
  16. Pearl, J. (2005). Influence diagrams – historical and personal perspectives. Decision Analysis, 2(4), 232–234.CrossRefGoogle Scholar
  17. Shachter, R. (1986). Evaluating influence diagrams. Operations Research, 34(6), 871–882.CrossRefGoogle Scholar
  18. Shachter, R. (1988). Probabilistic inference and influence diagrams. Operations Research, 36(4), 589–604.CrossRefGoogle Scholar
  19. SmartOrg. (2005). Dynamic depression drug: Tutorial. Menlo Park, CA: SmartOrg, Inc.Google Scholar
  20. Spetzler, C., & Staël von Holstein, C. (1975). Probability encoding in decision analysis. Management Science, 22, 340–358.CrossRefGoogle Scholar
  21. World Economic Forum. (2011). Global risks 2011: SixthEdition, an initiative ofthe risk response network. January, Cologny, Geneva.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.SmartOrg, Inc.Menlo ParkUSA