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.
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References
AbTech, 1990. Abductory Inductive Models—User’s Manual. AbTech, Inc., Charlottesville, VA.
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.
J.J. Brennan and J.J. Elam, 1986. Understanding and Validating Results in Model-Based Decision Support Systems. Decision Support Systems 2, 49–54.
Complementary Solutions Inc., 1992. Automate Anytime User Guide. At lanta, GA.
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.
EXECUCOM, 1992. Interactive Financial Planning System:User’s Man ual. EXECUCOM, Austin, TX.
S. J. Farlow (Ed.), 1984. Self-Organizing Methods in Modeling:GMDH Type Algorithms. Marcel Dekker, New York.
W.J. Frawley, G. Piatetsky-Shapiro and C.J. Matheus, 1992. Knowledge Discovery in Databases:An Overview. AI Magazine. Fall, 1992, 57–70.
A.M. Geoffrion, 1976. The Purpose of Mathematical Programming Is In sight, Not Numbers. Interfaces. 7:1, 81–92.
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.
H.J. Greenberg, 1988. ANALYZE Rulebase in G. Mitra (ed.). Mathemat ical Models for Decision Support. Springer-Verlag, Berlin, 229–238.
H.J. Greenberg, 1990. A Primer of ANALYZE, Working Paper, University of Colorado at Denver.
F.S. Hillier and G.L. Lieberman, 1990. Introduction to Operations Re search (5e), Holden-Day, Inc. Oakland, CA.
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.
D.W. Kosy and B.P. Wise, 1984. Self-explanatory Financial Planning Mod els. Proceedings of the National Conference of Artificial Intelligence, 176–181.
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.
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.
Microsoft, 1992. Microsoft Excel User’s Guide 2 (Version 4.0). Microsoft Corporation.
M. H. Prager, 1988. Group Method of Data Handling:A New Method for Stock Identification. Transactions of the American Fishery Society 117, 290–296.
Y.V. Reddy, 1985. The Role of Introspective Simulation in Managerial Decision Making. DSS-85 Transactions, IADSS, University of Texas at Austin, 18–32.
S. L. Savage, 1992. The ABC’s of Optimization Using What’s Best! LINDO Systems Inc, Chicago.
A. Saltelli and T. Homma, 1992. Sensitivity Analysis for Model Output. Computational Statistics and Data Analysis 13, 73–94.
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.
H. M. Wagner, 1993. Global Sensitivity Analysis. To appear.
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© 1995 Springer Science+Business Media New York
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Sharda, R., Steiger, D.M. (1995). Using Artificial Intelligence to Enhance Model Analysis. In: Nash, S.G., Sofer, A., Stewart, W.R., Wasil, E.A. (eds) The Impact of Emerging Technologies on Computer Science and Operations Research. Operations Research/Computer Science Interfaces Series, vol 4. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-2223-2_13
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DOI: https://doi.org/10.1007/978-1-4615-2223-2_13
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