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Decision Analytic Networks in Artificial Intelligence

Author

Listed:
  • Izhar Matzkevich

    (Investment Technology Group, Inc., 400 Corporate Pt., Culver City, California 90230)

  • Bruce Abramson

    (Carnegie Mellon University Department of Engineering and Public Policy, 3105 Hawthorne St. NW, Washington, DC 20008)

Abstract

Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a family of graphical models of decision theory known as influence diagrams or as belief networks. These models are equally attractive to theoreticians, decision modelers, and designers of knowledge-based systems. From a theoretical perspective, they combine graph theory, probability theory and decision theory. From an implementation perspective, they lead to powerful automated systems. Although many practicing decision analysts have already adopted influence diagrams as modeling and structuring tools, they may remain unaware of the theoretical work that has emerged from the artificial intelligence community. This paper surveys the first decade or so of this work.

Suggested Citation

  • Izhar Matzkevich & Bruce Abramson, 1995. "Decision Analytic Networks in Artificial Intelligence," Management Science, INFORMS, vol. 41(1), pages 1-22, January.
    • Handle: RePEc:inm:ormnsc:v:41:y:1995:i:1:p:1-22
    DOI: 10.1287/mnsc.41.1.1
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    Citations

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    Cited by:

    1. Abramson, Bruce & Brown, John & Edwards, Ward & Murphy, Allan & Winkler, Robert L., 1996. "Hailfinder: A Bayesian system for forecasting severe weather," International Journal of Forecasting, Elsevier, vol. 12(1), pages 57-71, March.
    2. Donald L. Keefer & Craig W. Kirkwood & James L. Corner, 2004. "Perspective on Decision Analysis Applications, 1990–2001," Decision Analysis, INFORMS, vol. 1(1), pages 4-22, March.
    3. Burström, Thommie & Parida, Vinit & Lahti, Tom & Wincent, Joakim, 2021. "AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research," Journal of Business Research, Elsevier, vol. 127(C), pages 85-95.
    4. Concha Bielza & Peter Müller & David Ríos Insua, 1999. "Decision Analysis by Augmented Probability Simulation," Management Science, INFORMS, vol. 45(7), pages 995-1007, July.

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