1. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-1 Chapter 4 Modeling and Analysis Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition

2. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-2 Learning Objectives Understand basic concepts of MSS modeling. Describe MSS models interaction. Understand different model classes. Structure decision making of alternatives. Learn to use spreadsheets in MSS modeling. Learn to structure linear program modeling.

3. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-3 Learning Objectives Examine search methods for MSS models. Handle multiple goals. Understand terms sensitivity, automatic, what-if analysis, goal seeking. Know key issues of model management.

4. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-4 Dupont Simulates Rail Transportation System and Avoids Costly Capital Expense Vignette Promodel simulation created representing entire transport systemPromodel Applied what-if analyses Visual simulation Identified bottlenecks

5. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-5 MSS Modeling Key element in DSS Many classes of models Specialized techniques for each model Allows for rapid examination of alternative solutions Multiple models often included in a DSS

6. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-6 Simulations Explore problem at hand Identify alternative solutions Can be object-oriented Enhances decision making View impacts of decision alternatives

7. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-7 DSS Models Algorithm-based models Statistic-based models Linear programming models Graphical models Quantitative models Qualitative models Simulation models

8. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-8 Problem Identification Environmental scanning and analysis Identify variables and relationships Forecasting –Increased amounts of information available through technology

9. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-9 Static Models Single photograph of situation Time can be rolled forward, a photo at a time Usually repeatable

10. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-10 Dynamic Model Represent changing situations Time dependent Varying conditions Occurrence may not repeat

11. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-11 Decision-Making Certainty –Assume complete knowledge –All potential outcomes known –Easy to develop –Resolution determined easily –Can be very complex

12. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-12 Decision-Making Uncertainty –Several outcomes for each decision –Probability of occurrence of each outcome unknown –Insufficient information –Assess risk and willingness to take it

13. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-13 Decision-Making Probabilistic Decision-Making –Decision under risk –Probability of each of several possible outcomes occurring –Risk analysis Calculate value of each alternative Select best expected value

14. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-14 Influence Diagrams Graphical representation of model Provides relationship framework Examines dependencies of variables Any level of detail Shows impact of change Shows what-if analysis

15. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-15 Influence Diagrams Decision Intermediate or uncontrollable Variables: Result or outcome (intermediate or final) Certainty Uncertainty Arrows indicate type of relationship and direction of influence Amount in CDs Interest earned Price Sales

16. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-16 Influence Diagrams Random (risk) Place tilde above variable’s name ~ Demand Sales Preference (double line arrow) Graduate University Sleep all day Ski all day Get job Arrows can be one-way or bidirectional, based upon the direction of influence

17. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-17

18. 18Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

19. 19 Analytica Influence Diagram of a Marketing Problem: The Marketing Model (Figure 5.2a) (Courtesy of Lumina Decision Systems, Los Altos, CA) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

20. 20 Analytica: Price Submodel (Figure 5.2b) (Courtesy of Lumina Decision Systems, Los Altos, CA) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

21. 21 Analytica: Sales Submodel (Figure 5.2c) (Courtesy of Lumina Decision Systems, Los Altos, CA) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

22. Graduation Science related college Remain at university Non- science related college fails IT Medicine related practical college Basic science literature Languages and admin. IT jobs teaching Hospitals and labs teaching Admin. succeed

23. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-23 Modeling with Spreadsheets Flexible and easy to use End-user modeling tool Features what-if analysis, data management, macros Seamless and transparent

24. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-24

25. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-25 Decision Tables Decision tables are a convenient way to organize information in a systematic manner. It can be done in a spreadsheet and thus looks like a table

26. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-26 Decision Tables Multiple criteria decision analysis Features include: –Decision variables (alternatives) –Uncontrollable variables –Result variables Applies principles of certainty, uncertainty, and risk

27. Example. An investment company is considering investing in one of three alternatives: bonds, stocks, or certificates of deposit (CDs). The company is interested in one goal: maximizing the yield on the investment after one year. If it were interested in other goals, such as safety or liquidity, the problem would be classified as one of multi-criteria decision analysis. The yield depends on the state of the economy sometime in the future (called the state of nature), which can be in solid growth, stagnation, or inflation. Experts estimated the following annual yields: If there is solid growth in the economy, bonds will yield 12%, stocks 15%, and time deposits 6.5%. If stagnation prevails, bonds will yield 6%, stocks 3%, and time deposits 6.5%. If inflation prevails, bonds will yield 3%, stocks will bring a loss of 2%, andtime deposits will yield 6.5%. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-27

28. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-28

29. Treatment the risk The most common method for solving this risk analysis problem is to select the alternative with the greatest expected value. Assume that the experts estimate the chance of solid growth at 50%, the chance of stagnation at 30%, and the chance of inflation at 20%. The decision table is then rewritten with the known probabilities (see next table). An expected value is computed by multiplying the results (outcomes) by their respective probabilities and adding them. For example, investing in bonds yields an expected return of 12(0.5)+6(0.3)+3(0.2)=8.4% © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-29

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32. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-32 Decision Tree It is a schematic presentation of alternatives, their potential consequences with their probabilities and payoffs. It is usually used to describe several interrelated decisions over several years.

33. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-33 Decision Tree Graphical representation of relationships Multiple criteria approach Demonstrates complex relationships

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36. Classification –Classification: classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data. Given a database D={t1,t2,…,tn} and a set of classes C={C1,…,Cm}, the Classification Problem is to define a mapping f:DC where each ti is assigned to one class. – Actually divides D into equivalence classes. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-36

37. Training Dataset © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-37

38. Output: A Decision Tree for “buys_computer” © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-38

39. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-39 MSS Mathematical Models Link decision variables, uncontrollable variables, parameters, and result variables together –Decision variables describe alternative choices. –Uncontrollable variables are outside decision-maker’s control. –Result variables are dependent on chosen solution and uncontrollable variables.

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42. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-42 Multiple Goals Simultaneous, often conflicting goals sought by management Determining single measure of effectiveness is difficult Handling methods: –Goal programming –Linear programming with goals as constraints

43. Example Is employee salaries. This constitutes a decision variable for management. It determines employee satisfaction (i.e., intermediateoutcome), which in turn determines the productivity level (i.e., final result). © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-43

44. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-44 Sensitivity, What-if, and Goal Seeking Analysis Sensitivity –Allows for adaptability and flexibility –Eliminates or reduces variables –Can be automatic or trial and error What-if –Assesses solutions based on changes in variables or assumptions Goal seeking –Backwards approach, starts with goal –Determines values of inputs needed to achieve goal

45. The role of heuristics in modeling The role of heuristics in modeling is to arrive at satisfactory solutions more quickly and less expensively than trying to get optimal solution. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-45

46. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-46 Search Approaches Heurisitic –Repeated, step-by-step searches –“Good enough” solution, but, eventually, will obtain optimal goal –Examples of heuristics Tabu search –Remembers and directs toward higher quality choices Genetic algorithms –Randomly examines pairs of solutions and mutations

47. 47 Simulation Technique for conducting experiments with a computer on a model of a management system Frequently used DSS tool Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

48. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-48 Simulations Imitation of reality Allows for experimentation and time compression Can include complexities, but requires special skills Handles unstructured problems Optimal solution not guaranteed Methodology –Problem definition –Construction of model –Testing and validation –Design of experiment –Experimentation –Evaluation –Implementation

49. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-49 Simulations Time-dependent or time-independent Visual interactive modeling –Graphical –Decision-makers interact with simulated model –may be used with artificial intelligence Can be objected oriented

50. Define visual simulation and compare it to conventional simulation. Visual simulation uses graphical representation to show the situation to the end-user. It does everything of a conventional simulation, which is any technique for conducting experiments (such as what-if analyses) with a digital computer on a model of a management system, but does it using visually pleasing representation. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-50

51. model base management system A model base management system calls for a software package with capabilities similar to those of a DBMS. An effective MBMS will make structural and algorithmic aspects of model organization and associated data processing invisible to users of the MBMS. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-51

52. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-52 Model-Based Management System Software that allows model organization with transparent data processing Capabilities –DSS user has control –Flexible in design –Gives feedback –GUI based –Increase in consistency –Communication between combined models

53. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-53 Distinguish between a static model and a dynamic model. Give an example of each. In a static model we look at one set of input data and one set of output data at one point of time. In dynamic models one looks at several possible inputs and outputs over an extended period.

54. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-54 What is the role of heuristics in modeling? The role of heuristics in modeling is to arrive at satisfactory solutions more quickly and less expensively than trying to get optimal solution.

55. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-55 Define visual interactive modeling (VIM). VIM uses computer graphic displays to present the impact of different management decision.

56. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-56 What is a model base management system? A model base management system calls for a software package with capabilities similar to those of a DBMS. An effective MBMS will make structural and algorithmic aspects of model organization and associated data processing invisible to users of the MBMS.

57. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-57 What is the relationship between environmental analysis and problem identification? A manager scans the environment to identify any problems that may exist and require addressing. In a way, the manager is “looking for trouble” (or opportunities!).

58. What are the major types of models used in DSS? The major types are: optimization, financial, statistical, simulation, static, or dynamic. They can be standard or custom made. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-58

59. What is an influence diagram? What is it used for? It is a diagram that shows all the variables in a model, what type they are, and how they are related to each other. For example, it will show you that profit is determined by revenues and expenses © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 4-59