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Using Artificial Intelligence to Enhance Model Analysis

  • Ramesh Sharda
  • David M. Steiger
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 4)

Abstract

The purpose of mathematical modeling is to generate insights into the decision making environment being modeled. Such insights are often generated through the analysis of several, if not many, related model instances. However, little theory and only a few systems have been developed to support this basic goal of modeling. Nonlinear modeling capabilities of neural networks and related methods can be employed to identify patterns within the multiple ‘what-if’ instances.

The purpose of this paper is to describe a prototype, artificial intelligence-based system, named INSIGHT, which analyzes multiple, related model instances to identify key model parameters and develop insights into how these key parameters interact to influence the model solution.

Keywords

Mixed Integer Linear Programming Model Instance Scenario Manager Global Sensitivity Analysis Auxiliary Model 
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.

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References

  1. [1]
    AbTech, 1990. Abductory Inductive Models—User’s Manual. AbTech, Inc., Charlottesville, VA.Google Scholar
  2. [2]
    R. L. Barron, A. N. Mucciardi, F. J. Cook, A. R. Barron and J. N. Craig, 1984. Adaptive Learning in Networks:Development and Applica tions in the U.S. of Algorithms Related to GMDH. In S. J. Farlow (Ed.) Self-Organizing Methods in Modeling:GMDH Type Algorithms. Marcel Dekker, New York, 25–66.Google Scholar
  3. [3]
    J.J. Brennan and J.J. Elam, 1986. Understanding and Validating Results in Model-Based Decision Support Systems. Decision Support Systems 2, 49–54.Google Scholar
  4. [4]
    Complementary Solutions Inc., 1992. Automate Anytime User Guide. At lanta, GA.Google Scholar
  5. [5]
    J.J. Elam and B. Konsynski, 1987. Using Artificial Intelligence Techniques to Enhance the Capabilities of Model Management Systems. Decision Sci ences 18:3, 487–501.CrossRefGoogle Scholar
  6. [6]
    EXECUCOM, 1992. Interactive Financial Planning System:User’s Man ual. EXECUCOM, Austin, TX.Google Scholar
  7. [7]
    S. J. Farlow (Ed.), 1984. Self-Organizing Methods in Modeling:GMDH Type Algorithms. Marcel Dekker, New York.Google Scholar
  8. [8]
    W.J. Frawley, G. Piatetsky-Shapiro and C.J. Matheus, 1992. Knowledge Discovery in Databases:An Overview. AI Magazine. Fall, 1992, 57–70.Google Scholar
  9. [9]
    A.M. Geoffrion, 1976. The Purpose of Mathematical Programming Is In sight, Not Numbers. Interfaces. 7:1, 81–92.Google Scholar
  10. [10]
    H.J. Greenberg, 1983. A Functional Description of ANALYZE:A Com puter Assisted Analysis System for Linear Programming Models. ACM Transactions on Mathematical Software. 9:1, 18–56.CrossRefGoogle Scholar
  11. [11]
    H.J. Greenberg, 1988. ANALYZE Rulebase in G. Mitra (ed.). Mathemat ical Models for Decision Support. Springer-Verlag, Berlin, 229–238.CrossRefGoogle Scholar
  12. [12]
    H.J. Greenberg, 1990. A Primer of ANALYZE, Working Paper, University of Colorado at Denver.Google Scholar
  13. [13]
    F.S. Hillier and G.L. Lieberman, 1990. Introduction to Operations Re search (5e), Holden-Day, Inc. Oakland, CA.Google Scholar
  14. [14]
    S.O. Kimbrough, S.A. Moore, C.W. Pritchett and C.A. Sherman, 1992. On DSS Support for Candle-Lighting Analysis. Transactions of DSS-92, 118–135.Google Scholar
  15. [15]
    D.W. Kosy and B.P. Wise, 1984. Self-explanatory Financial Planning Mod els. Proceedings of the National Conference of Artificial Intelligence, 176–181.Google Scholar
  16. [16]
    D.W. Rosy and B.P. Wise, 1986. Overview of Rome:A Reason-Oriented Modeling Environment in L.F. Psu (ed.), Artificial Intelligence in Eco nomics and Management. Elesvier Science Publishers, North-Holland, 21–30.Google Scholar
  17. [17]
    W.G. Kurator and R.P. O’Neill, 1980. PERUSE:An Interactive System for Mathematical Programs. ACM Transactions on Mathematical Software 6:4, 489–509.CrossRefGoogle Scholar
  18. [18]
    Microsoft, 1992. Microsoft Excel User’s Guide 2 (Version 4.0). Microsoft Corporation.Google Scholar
  19. [19]
    M. H. Prager, 1988. Group Method of Data Handling:A New Method for Stock Identification. Transactions of the American Fishery Society 117, 290–296.Google Scholar
  20. [20]
    Y.V. Reddy, 1985. The Role of Introspective Simulation in Managerial Decision Making. DSS-85 Transactions, IADSS, University of Texas at Austin, 18–32.Google Scholar
  21. [21]
    S. L. Savage, 1992. The ABC’s of Optimization Using What’s Best! LINDO Systems Inc, Chicago.Google Scholar
  22. [22]
    A. Saltelli and T. Homma, 1992. Sensitivity Analysis for Model Output. Computational Statistics and Data Analysis 13, 73–94.Google Scholar
  23. [23]
    A. Saltelli and M. Marivoet, 1990. Non-parametric Statistics in Sensitivity Analysis for Model Output:A Comparison of Selected Techniques. Relia bility Engineering and Systems Safety 28, 220–253.Google Scholar
  24. [24]
    H. M. Wagner, 1993. Global Sensitivity Analysis. To appear.Google Scholar

Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Ramesh Sharda
    • 1
  • David M. Steiger
    • 2
  1. 1.College of Business AdministrationOklahoma State UniversityStillwaterUSA
  2. 2.School of BusinessUniversity of North Carolina, GreensboroGreensboroUSA

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