Enhancing user understanding in a decision support system: A theoretical basis and framework

Journal of Management Information Systems

Volumn 15, Issue 2, 1998, Pages 199-220

  Steiger, David M ^a,b,c  

^a UNIVERSITY OF NORTH CAROLINA AT GREENSBORO   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c OKLAHOMA STATE UNIVERSITY   (United States)

Author keywords

Artificial intelligence; Decision support systems; Design theory; Model analysis; Model management systems; Post optimality analysis; Sensitivity analysis; Theory of understanding

Indexed keywords

EID: 0032223744     PISSN: 07421222     EISSN: None     Source Type: Journal    
DOI: 10.1080/07421222.1998.11518214     Document Type: Article

References (82)

1. Abductory Inductive Models - User's Manual. Charlottesville, VA: Ab-Tech, 1990.
2. Barron, R.L. Predicted square error: a criterion for automatic model selection. In J. Farlow (ed.), Self-Organizing Methods in Modeling: GMDH Type Algorithms. New York: Marcel Dekker, 1984, pp. 25-66.
3. Bettonvil, B. Detection of Important Factors by Sequential Bifurcation. Tilburg, Netherlands: Tilburg University Press, 1990.
4. Bettonvil, B., and Kleijnen, J.P.C. Identifying the important factors in simulation models with many factors. Working paper, Department of IS and Center, Tilburg University, Tilburg, Netherlands, 1994.
5. Bhargava, H.K. Dimensional analysis in mathematical modeling systems: a simple numerical method. ORSA Journal on Computing, 5, 1 (Winter 1993), 33-39.
6. Boland, R.J., Jr., and Day, W. The process of system design: a phenomenological approach. Proceedings of the Third International Conference on Information Systems, Ann Arbor, MI, December 1982, pp. 31-45.
7. Bonczek, R.H.; Holsapple, C.W.; and Whinston, A.B. Future directions for developing decision support systems. Decision Sciences, 11, 4 (October 1980), 616-631.
8. Branley, B.; Fradin, R.; Kimbrough, S.O.; and Shafer, T. On heuristic mapping of decision surfaces for post-evaluation analysis. Proceedings of the Twenty-Ninth Annual Hawaii International Conference on System Sciences. Los Alamitos, CA: IEEE Computer Society Press, 1977, 416-425.
9. Brennan, J.J., and Elam, J. J. Understanding and validating results in model-based decision support systems. Decision Support Systems, 2 (February 1986), 49-54.
10. Burstein, M.H. Concept formation by incremental analogical reasoning and debugging. In R.S. Michalski, J. Carbonell, and T.M. Mitchell (eds.), Machine Learning: An Artificial Intelligence Approach, vol. 2. Los Altos, CA: Morgan Kaufmann, 1986, pp. 351-370.
11. Carbonell, J. Deviational analogy: a theory of reconstructive problem solving and expertise acquisition. In R.S. Michalski, J. Carbonell, and T.M. Mitchell (eds.), Machine Learning: An Artificial Intelligence Approach, vol. 2. Los Altos, CA: Morgan Kaufmann, 1986, pp. 351-370.
12. Dolk, D.R. A generalized model management system for mathematical programming. ACM Transactions on Mathematical Software, 12, 2 (June 1986), 92-126.
13. Dunker, K. On problem solving. Psychological Monographs, 58, 270 (1945).
14. EXECUCOM. Interactive Financial Planning System: User's Manual. Austin, TX: EXECUCOM, 1992.
15. Farlow, S.J. Self-Organizing Methods in Modeling: GMDH Type Algorithms. New York: Marcel Dekker, 1984.
16. Geoffrion, A.M. The purpose of mathematical programming is insight, not numbers. Interfaces, 7, 1 (January-February 1976), 81-92.
17. Geoffrion, A.M. An introduction to structured modeling. Management Science, 33, 5 (May 1987), 547-588.
18. Geoffrion, A.M., and Nauss, R. Parametric and postoptimality analysis in integer linear programming. Management Science, 23, 5 (May 1977), 453-466.
19. Golberg, D.E. Genetic Algorithms in Search Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1991.
20. Green, G.H. Quantitative Discovery: Using dependencies to discover nonlinear terms. Master's thesis, University of Illinois at Urbana, Champaign, 1988.
21. Greenberg, H.J. A tutorial on computer-assisted analysis. In H.J. Greenberg, F.H. Murphy, and S.H. Shaw (eds.), Advanced Techniques in the Practice of Operations Research. New York: Elsevier Science Publishing, 1982, pp. 212-249.
22. Greenberg, H.J. A Computer-Assisted Analysis System for Mathematical Programming Models and Solutions: A User's Guide for ANALYZE. Boston: Kluwer, 1993a.
23. Greenberg, H.J. Enhancements of ANALYZE: a computer-assisted analysis system for linear programming. ACM Transactions on Mathematical Software, 19, 2 (June 1993b), 223-256.
24. Greenberg, H.J. Syntax-directed report writing in linear programming using ANALYZE. European Journal of Operational Research, 72, 2 (January 27, 1994), 300-311.
25. Hammond, K.F. Case-based planning. Proceedings of a Workshop on Case-based Reasoning, May 10-13, 1988, pp. 17-20.
26. Hilgard, E.R. Theories of Learning, 2d ed. New York: Appleton-Century-Crofts, 1956.
27. Ivakhnenko, G.G. Polynomial theory of complex systems. IEEE Transactions on Systems, Man and Cybernetics, 4, 3 (March 1971), 364-384.
28. Jones, C.V. User interfaces. In E.G. Coffman, J.K. Lenstra, and A.Y. Kan (eds.), Handbooks of Operations Research and Management Science, vol. 3. Amsterdam: North Holland/Elsevier, 1992, pp. 603-668.
29. Kasper, G.M. A theory of decision support system design for user calibration. Information Systems Research, 7, 2 (June 1996), 215-232.
30. Kaufman, K.A.; Michalski, R.S.; and Kershberg, L. Mining for knowledge in databases; goals and general description of the INLEN system. In G. Piatetsky-Shapiro and W. Frawley (eds.), Knowledge Discovery in Databases. Menlo Park, CA: AAAI Press/MIT Press, 1991, pp. 449-462.
31. Kimbrough, S.O.; Moore, S.A.; Pritchett, C.W.; and Sherman, C.A. On DSS support for candle-lighting analysis. Transactions of DSS-92 (1992), 118-135.
32. Kimbrough, S.O.; Oliver, J.R.; and Pritchett, C.W. On post-evaluation analysis: candle-lighting and surrogate models. Interfaces, 23, 3 (May-June 1993), 17-28.
33. Kimbrough, S.O., and Oliver, J.R. On automating candle-lighting analysis: insight from search with genetic algorithms and approximate models. Proceedings of the Twenty-seventh Annual Hawaii International Conference on System Sciences, 1994, pp. 536-544.
34. Kimbrough, S.O.; Pritchett, C.W.; Bieber, M.P.; and Bhargava, H.K. The Coast Guard's KSS project. Interfaces, 20, 6 (November-December 1990), 5-16.
35. Kleijnen, J.P.C. Sensitivity analysis and optimization of simulation models. Working paper, Center and Department of I.S., Tilburg University, Tilburg, Netherlands, 1994.
36. Kleijnen, J.P.C. Statistical Tools for Simulation Practitioners. New York: Marcel Dekker, 1987.
37. Klingman, D.; Phillips, N.; Steiger, D.; Wirth, R.; Padman, R.; and Krishnan, R. An optimization based integrated short-term refined petroleum product planning system. Management Science, 33, 7 (July 1987), 813-830.
38. Klingman, D.; Phillips, N.; Steiger, D.; and Young, R. The successful deployment of management science throughout Citgo Petroleum Corporation. Interfaces, 17, 1 (January-February 1987), 4-25.
39. Kohler, W. The Mentality of Apes, trans. by E. Winter. New York: Harcourt, Brace, 1925.
40. Kohler, W. The Task of Gestalt Psychology. Princeton, NJ: Princeton University Press, 1969.
41. Kolodner, J.L. Extended problem solver capabilities through case-based inference. Proceedings of a Workshop on Case-Based Reasoning, May 10-13, 1988, pp. 21-30.
42. Kosy, D.W., and Wise, B.P. Self-explanatory financial planning models. Proceedings of the National Conference of Artificial Intelligence, August 1984, pp. 176-181.
43. Kosy, D.W., and Wise, B.P. Overview of Rome: a reason-oriented modeling environment. In L.F. Psu (ed.), Artificial Intelligence in Economics and Management. Amsterdam: North-Holland, Elsevier Science Publishers, 1986, pp. 21-30.
44. Krishnan, R. Model management: survey, future research directions and a bibliography. ORSA CSTS Newsletter, 14, 1 (Spring 1993), 1-22.
45. Kurator, W.G., and O'Neill, R.P. PERUSE: an interactive system for mathematical programs. ACM Transactions on Mathematical Software, 6, 4 (December 1980), 489-509.
46. Lee, D.L. Perception, intuition and insight. In W.R. Niblett (ed.), How and Why Do We Learn. London: Faber and Faber, 1965.
47. Logan, F.A., and Ferraro, D.P. Systematic Analyses of Learning and Motivation. New York: John Wiley and Sons, 1978.
48. Metcalf, J. Premonitions of insight predict impending error. Journal of Experimental Psychology: Learning, Memory and Cognition, 12, 4 (October 1986), 623-634.
49. Metcalf, J. Feeling of knowing in memory and problem solving. Journal of Experimental Psychology: Learning, Memory and Cognition, 12, 2 (April 1986), 288-294.
50. Metcalf, J., and Wiebe, D. Intuition in insight and noninsight problem solving. Memory and Cognition, 15, 3 (May 1987), 238-246.
51. Michalski, R.S., and Stepp, R. Revealing conceptual structure in data by inductive learning. Machine Intelligence, 10 (1982), 173-196.
52. Mitchell, T.M.; Keller, R.M.; and Kedar-Cabelli, S.T. Explanation-based generalization: a unifying view. Machine Learning, 1, 1 (January 1986), 47-80.
53. Newell, A., and Simon, H.A. Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall, 1972.
54. Newton, N. Foundations of Understanding. Amsterdam: John Benjamin Publishing, 1996.
55. Nickerson, R.S.; Perkins, D.N.; and Smith, E.E. The Teaching of Thinking. Hillsdale, NJ: Lawrence Erlbaum Associates, 1985.
56. O'Leary, D.E. Measuring the quality of computer model performance. European Journal of Operational Research, 56, 3 (February 10, 1992), 319-331.
57. Parsaye, K.; Chignell, M.; Khoshafian, S.; and Wong, H. Intelligent databases. AI Expert (March 1990), 38-47.
58. Perkins, D.B. Knowledge As Design. Hillsdale, NJ: Lawrence Erlbaum Associates, 1986
59. Piela, P.C.; Epperly, T.; Westerberg, K.; and Westerberg, A. An object-oriented computer environment for modeling and analysis, part 1 - the modeling language. Computers and Chemical Engineering, 15, 1 (January 1991), 53-72.
60. Prager, M.H. Group method of data handling: a new method for stock identification. Transactions of the American Fishery Society, 177 (1988), 290-296.
61. Quinlan, J.R. Discovering rules from large collections of examples: a case study. In D. Michie (ed.), Expert Systems in the Micro Electronic Age. Edinburgh: Edinburgh University Press, 1979, pp. 168-201.
62. Quinlan, J.R. Learning efficient classification procedures and their application to chess end-games. In M.S. Michalski, J. Carbonell, and T.M. Mitchell (eds.), Machine Learning: An Artificial Intelligence Approach. Los Altos, CA: Morgan Kaufmann, 1983, pp. 463-482.
63. Reddy, Y.V. The role of introspective simulation in managerial decision making. DSS-85 Transactions, IADSS. Austin, TX: University of Texas, 1985, pp. 18-32.
64. Saaty, T.S. Decision Making: The Analytical Hierarchical Process. Pittsburgh, PA: University of Pittsburgh Press, 1988.
65. Saltelli, A., and Homma, T. Sensitivity analysis for model output. Computational Statistics and Data Analysis, 13, 1 (January 1992), 73-94.
66. Saltelli, A., and Marivoet, J. Non-parametric statistics in sensitivity analysis for model output: a comparison of selected techniques. Reliability Engineering and Systems Safety, 28, 4 (April 1990), 229-253.
67. Schank, R., and Reisbeck, C. Inside Case-Based Reasoning. Hillsdale, NJ: Lawrence Erlbaum Associates, 1989.
68. Sharda, R., and Steiger, D.M. Using artificial intelligence to enhance model analysis. In S.G. Nash and A. Sofer (eds.), The Impact of Emerging Technologies on Computer Science and Operations Research. Boston: Kluwer, 1995, pp. 263-280.
69. Sharda, R., and Steiger, D.M. Inductive model analysis systems: enhancing model analysis in decision support systems. Information Systems Research, 7, 3 (September 1996), 328-341.
70. Simon, H.A., and Newell, A. Human problem solving: the state of the theory in 1970. American Psychologist, 26, 2 (February 1971), 145-159.
71. Sprague, R.H., Jr., and Carlson, E.D. Building Effective Decision Support Systems. Englewood Cliffs, NJ: Prentice-Hall, 1982.
72. Steiger, D.M. Model instance management systems: an extension of MMS/DSS for the intelligent management and analysis of multiple model instances. Working paper no. ISOM960502, University of North Carolina, Greensboro, 1996.
73. Stephanopoulos, G.; Henning, G.; and Leone, H. MODEL.LA. a modeling language for process engineering - I: the formal framework. Computers and Chemical Engineering, 14, 8 (August 1990), 813-846.
74. Stephanopoulos, G.; Henning, G.; and Leone, H. MODEL.LA. a modeling language for process engineering - II: multifaceted modeling of processing systems. Computers and Chemical Engineering, 14, 8 (August 1990), 847-869.
75. Thorndike, E.L. Animal Intelligence: An Experimental Study of the Associative Processes in Animals. New York: Macmillan, 1898.
76. Wagner, H.M. Global sensitivity analysis. Operations Research, 43, 6 (November-December 1995), 948-969.
77. Walls, J.G.; Widmeyer, G.R.; and El Sawy, O.A. Building an information system design theory for vigilant EIS. Information Systems Research, 3, 1 (March 1992), 36-59.
78. Webster's New Collegiate Dictionary. Springfield, MA: Merriam, 1961.
79. Wertheimer, M. Productive Thinking, rev. ed. New York: Harper and Row, 1959.
80. Weiss, S.M., and Kulikowski, C.A. Computer Systems That Learn. San Mateo, CA: Morgan Kaufman Publishers, 1991.
81. Wise, B.P., and Kosy, D.W. Model-based evaluation of long-range resource allocation plans. In L.F. Pau (ed.), Artificial Intelligence in Economics and Management. Amsterdam: North-Holland, Elsevier Science Publishers, 1986, pp. 93-102.
82. Zeleny, M. Multiple Criteria Decision Making. New York: McGraw-Hill, 1982.