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Decision Support System

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Decision Support System
Decision Support Systems
Marek J. Druzdzel and Roger R. Flynn
Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh, PA 15260 {marek,flynn}@sis.pitt.edu http://www.sis.pitt.edu/∼dsl

To appear in Encyclopedia of Library and Information Science, Second Edition, Allen Kent (ed.), New York: Marcel Dekker, Inc., 2002

1

Contents
Introduction Decisions and Decision Modeling Types of Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Judgment and Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeling Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Components of Decision Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision Support Systems Normative Systems Normative and Descriptive Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision-Analytic Decision Support Systems . . . . . . . . . . . . . . . . . . . . . . . . . . Equation-Based and Mixed Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . User Interfaces to Decision Support Systems Support for Model Construction and Model Analysis . . . . . . . . . . . . . . . . . . . . . 3 4 4 4 5 5 6 7 7 8 10 11 11

Support for Reasoning about the Problem Structure in Addition to Numerical Calculations 11 Support for Both Choice and Optimization of Decision Variables . . . . . . . . . . . . . . Graphical Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary 12 12 12

2

Introduction
Making decisions concerning complex systems (e.g., the management of organizational operations, industrial processes, or investment portfolios; the command and control of military units; or the control of nuclear power plants) often strains our cognitive capabilities. Even though individual interactions among a system’s variables may be



References: [1] Gregory F. Cooper and Edward Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4):309–347, 1992. [2] Robyn M. Dawes. Rational Choice in an Uncertain World. Hartcourt Brace Jovanovich, Publishers, 1988. [3] Marek J. Druzdzel. Probabilistic Reasoning in Decision Support Systems: From Computation to Common Sense. PhD thesis, Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, December 1992. 13 [4] Marek J. Druzdzel. Explanation in probabilistic systems: Is it feasible? will it work? In Proceedings of the Fifth International Workshop on Intelligent Information Systems (WIS–96), pages 12–24, D¸blin, Poland, 2–5 JUne 1996. e [5] Marek J. Druzdzel. Five useful properties of probabilistic knowledge representations from the point of view of intelligent systems. Fundamenta Informaticæ, Special issue on Knowledge Representation and Machine Learning, 30(3/4):241–254, June 1997. [6] Marek J. Druzdzel. ESP: A mixed initiative decision-theoretic decision modeling system. In Working Notes of the AAAI–99 Workshop on Mixed-initiative Intelligence, pages 99–106, Orlando, FL, 18 July 1999. [7] Marek J. Druzdzel. SMILE: Structural Modeling, Inference, and Learning Engine and GeNIe: A development environment for graphical decision-theoretic models. In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI–99), pages 902–903, Orlando, FL, July 18–22 1999. [8] Marek J. Druzdzel and F. Javier D´ Criteria for combining knowledge from different sources ıez. in probabilistic models. In Working Notes of the workshop on Fusion of Domain Knowledge with Data for Decision Support, Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI–2000), pages 23–29, Stanford, CA, 30 June 2000. [9] Marek J. Druzdzel and Herbert A. Simon. Causality in Bayesian belief networks. In Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence (UAI–93), pages 3–11, San Francisco, CA, 1993. Morgan Kaufmann Publishers, Inc. [10] Marek J. Druzdzel and Linda C. van der Gaag. Building probabilistic networks: “Where do the numbers come from?” guest editors’ introduction. IEEE Transactions on Knowledge and Data Engineering, 12(4):481–486, July–August 2000. [11] Clark Glymour and Gregory F. Cooper, editors. Computation, Causation, and Discovery. AAAI Press, Menlo Park, CA, 1999. [12] David E. Heckerman, Dan Geiger, and David M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3):197–243, 1995. [13] Max Henrion, John S. Breese, and Eric J. Horvitz. Decision Analysis and Expert Systems. AI Magazine, 12(4):64–91, Winter 1991. [14] Samuel Holtzman. Intelligent Decision Systems. Addison-Wesley, Reading, MA, 1989. [15] Ronald A. Howard and James E. Matheson. Influence diagrams. In Ronald A. Howard and James E. Matheson, editors, The Principles and Applications of Decision Analysis, pages 719– 762. Strategic Decisions Group, Menlo Park, CA, 1984. [16] Daniel Kahneman, Paul Slovic, and Amos Tversky, editors. Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge, 1982. [17] Paul E. Lehner, Theresa M. Mullin, and Marvin S. Cohen. A probability analysis of the usefulness of decision aids. In M. Henrion, R.D. Shachter, L.N. Kanal, and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence 5, pages 427–436. Elsevier Science Publishers B.V. (North Holland), 1990. [18] Tsai-Ching Lu, Marek J. Druzdzel, and Tze-Yun Leong. Causal mechanism-based model construction. In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI–2000), pages 353–362, San Francisco, CA, 2000. Morgan Kaufmann Publishers, Inc. 14 [19] Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1988. [20] Judea Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK, 2000. [21] Judea Pearl and Thomas S. Verma. A theory of inferred causation. In J.A. Allen, R. Fikes, and E. Sandewall, editors, KR–91, Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, pages 441–452, Cambridge, MA, 1991. Morgan Kaufmann Publishers, Inc., San Mateo, CA. [22] Andrew P. Sage. Decision Support Systems Engineering. John Wiley & Sons, Inc., New York, 1991. [23] Leonard J. Savage. The Foundations of Statistics (Second Revised Edition). Dover Publications, New York, NY, 1972. [24] Herbert A. Simon. Causal ordering and identifiability. In William C. Hood and Tjalling C. Koopmans, editors, Studies in Econometric Method. Cowles Commission for Research in Economics. Monograph No. 14, chapter III, pages 49–74. John Wiley & Sons, Inc., New York, NY, 1953. [25] Herbert A. Simon, Jayant R. Kalagnanam, and Marek J. Druzdzel. Performance budget planning: The case of a research university. In preparation, 2000. [26] Peter Spirtes, Clark Glymour, and Richard Scheines. Springer Verlag, New York, 1993. Causation, Prediction, and Search. [27] Detlof von Winterfeldt and Ward Edwards. Decision Analysis and Behavioral Research. Cambridge University Press, Cambridge, 1988. [28] Haiqin Wang and Marek J. Druzdzel. User interface tools for navigation in conditional probability tables and elicitation of probabilities in Bayesian networks. In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI–2000), pages 617–625, San Francisco, CA, 2000. Morgan Kaufmann Publishers, Inc. 15

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