1. 1 CHAPTER 2 Decision Making, Systems, Modeling, and Support

2. 2  Conceptual Foundations of Decision Making  The Systems Approach  How Support is Provided  Opening Vignette: How to Invest $10,000,000 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

3. 3 Typical Business Decision Aspects n Decision may be made by a group n Group member biases n Groupthink n Several, possibly contradictory objectives n Many alternatives n Results can occur in the future n Attitudes towards risk n Need information n Gathering information takes time and expense n Too much information n “What-if” scenarios n Trial-and-error experimentation with the real system may result in a loss n Experimentation with the real system - only once n Changes in the environment can occur continuously n Time pressure Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

4. 4 n How are decisions made??? n What methodologies can be applied? n What is the role of information systems in supporting decision making? DSS n Decision n Support n Systems Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

5. 5 Decision Making n Decision Making: a process of choosing among alternative courses of action for the purpose of attaining a goal or goals n Managerial Decision Making is synonymous with the whole process of management (Simon, 1977) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

6. 6 Decision Making versus Problem Solving Simon’s 4 Phases of Decision Making 1. Intelligence 2. Design 3. Choice 4. Implementation Decision making and problem solving are interchangeable Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

7. 7 Systems n A SYSTEM is a collection of objects such as people, resources, concepts, and procedures intended to perform an identifiable function or to serve a goal n System Levels (Hierarchy): All systems are subsystems interconnected through interfaces Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

8. 8 The Structure of a System Three Distinct Parts of Systems (Figure 2.1) n Inputs n Processes n Outputs Systems n Surrounded by an environment n Frequently include feedback The decision maker is usually considered part of the system Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

9. 9 System Input(s) Feedback Environment Output(s) Boundary Processes Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

10. 10 How to Identify the Environment? Two Questions (Churchman, 1975) 1. Does the element matter relative to the system's goals? [YES] 2. Is it possible for the decision maker to significantly manipulate this element? [NO] Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

11. 11 An Information System n Collects, processes, stores, analyzes, and disseminates information for a specific purpose n Is often at the heart of many organizations n Accepts inputs and processes data to provide information to decision makers and helps decision makers communicate their results Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

12. 12 System Effectiveness and Efficiency Two Major Classes of Performance Measurement n Effectiveness is the degree to which goals are achieved Doing the right thing! n Efficiency is a measure of the use of inputs (or resources) to achieve outputs Doing the thing right! n MSS emphasize effectiveness Often: several non-quantifiable, conflicting goals Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

13. 13 Models n Major component of DSS n Use models instead of experimenting on the real system n A model is a simplified representation or abstraction of reality. n Reality is generally too complex to copy exactly n Much of the complexity is actually irrelevant in problem solving Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

14. 14 Degrees of Model Abstraction (Least to Most) n Iconic (Scale) Model: Physical replica of a system n Analog Model behaves like the real system but does not look like it (symbolic representation) n Mathematical (Quantitative) Models use mathematical relationships to represent complexity Used in most DSS analyses Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

15. 15 The Modeling Process-- A Preview n How Much to Order for the Ma-Pa Grocery? n Bob and Jan: How much bread to stock each day? Solution Approaches n Trial-and-Error n Simulation n Optimization n Heuristics Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

16. 16 The Decision-Making Process Systematic Decision-Making Process (Simon, 1977) n Intelligence n Design n Choice n Implementation (Figure 2.2) Modeling is Essential to the Process Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

17. 17 n Intelligence phase –Reality is examined –The problem is identified and defined n Design phase –Representative model is constructed –The model is validated and evaluation criteria are set n Choice phase –Includes a proposed solution to the model –If reasonable, move on to the n Implementation phase –Solution to the original problem Failure: Return to the modeling process Often Backtrack / Cycle Throughout the Process Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

18. 18 The Intelligence Phase Scan the environment to identify problem situations or opportunities Find the Problem n Identify organizational goals and objectives n Determine whether they are being met n Explicitly define the problem Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

19. 19 Problem Classification Structured versus Unstructured Programmed versus Nonprogrammed Problems Simon (1977) NonprogrammedProgrammedProblems Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

20. 20 n Problem Decomposition: Divide a complex problem into (easier to solve) subproblems Chunking (Salami) n Some seemingly poorly structured problems may have some highly structured subproblems n Problem Ownership Outcome: Problem Statement Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

21. 21 The Design Phase n Generating, developing, and analyzing possible courses of action Includes n Understanding the problem n Testing solutions for feasibility n A model is constructed, tested, and validated Modeling n Conceptualization of the problem n Abstraction to quantitative and/or qualitative forms Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

22. 22 Mathematical Model n Identify variables n Establish equations describing their relationships n Simplifications through assumptions n Balance model simplification and the accurate representation of reality Modeling: an art and science Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

23. 23 The Principle of Choice n What criteria to use? n Best solution? n Good enough solution? Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

24. 24 Selection of a Principle of Choice Not the choice phase A decision regarding the acceptability of a solution approach n Normative n Descriptive Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

25. 25 Normative Models n The chosen alternative is demonstrably the best of all (normally a good idea) n Optimization process n Normative decision theory based on rational decision makers Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

26. 26 Rationality Assumptions n Humans are economic beings whose objective is to maximize the attainment of goals; that is, the decision maker is rational n In a given decision situation, all viable alternative courses of action and their consequences, or at least the probability and the values of the consequences, are known n Decision makers have an order or preference that enables them to rank the desirability of all consequences of the analysis Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

27. 27 Suboptimization n Narrow the boundaries of a system n Consider a part of a complete system n Leads to (possibly very good, but) non-optimal solutions n Viable method Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

28. 28 Descriptive Models n Describe things as they are, or as they are believed to be n Extremely useful in DSS for evaluating the consequences of decisions and scenarios n No guarantee a solution is optimal n Often a solution will be good enough n Simulation: Descriptive modeling technique Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

29. 29 Satisficing (Good Enough) n Most human decision makers will settle for a good enough solution n Tradeoff: time and cost of searching for an optimum versus the value of obtaining one n Good enough or satisficing solution may meet a certain goal level is attained (Simon, 1977) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

30. 30 Why Satisfice? Bounded Rationality (Simon) n Humans have a limited capacity for rational thinking n Generally construct and analyze a simplified model n Behavior to the simplified model may be rational n But, the rational solution to the simplified model may NOT BE rational in the real-world situation n Rationality is bounded by –limitations on human processing capacities –individual differences n Bounded rationality: why many models are descriptive, not normative Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

31. 31 Developing (Generating) Alternatives n In Optimization Models: Automatically by the Model! Not Always So! n Issue: When to Stop? Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

32. 32 Predicting the Outcome of Each Alternative n Must predict the future outcome of each proposed alternative n Consider what the decision maker knows (or believes) about the forecasted results n Classify Each Situation as Under –Certainty –Risk –Uncertainty Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

33. 33 Measuring Outcomes n Goal attainment n Maximize profit n Minimize cost n Customer satisfaction level (minimize number of complaints) n Maximize quality or satisfaction ratings (surveys) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

34. 34 Importance of Scenarios in MSS n Help identify potential opportunities and/or problem areas n Provide flexibility in planning n Identify leading edges of changes that management should monitor n Help validate major assumptions used in modeling n Help check the sensitivity of proposed solutions to changes in scenarios Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

35. 35 The Choice Phase n The CRITICAL act - decision made here! n Search, evaluation, and recommending an appropriate solution to the model n Specific set of values for the decision variables in a selected alternative The problem is considered solved only after the recommended solution to the model is successfully implemented Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

36. 36 Search Approaches n Analytical Techniques n Algorithms (Optimization) n Blind and Heuristic Search Techniques Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

37. 37 Evaluation: Multiple Goals, Sensitivity Analysis, What-If, and Goal Seeking n Evaluation (with the search process) leads to a recommended solution n Multiple goals n Complex systems have multiple goals Some may conflict n Typically, quantitative models have a single goal n Can transform a multiple-goal problem into a single-goal problem Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

38. 38 Sensitivity Analysis n Change inputs or parameters, look at model results Sensitivity analysis checks relationships Types of Sensitivity Analyses n Automatic n Trial and error Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

39. 39 Trial and Error n Change input data and re-solve the problem n Better and better solutions can be discovered n How to do? Easy in spreadsheets (Excel) –What-if –Goal seeking Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

40. 40 What-If Analysis n Figure 2.9 - Spreadsheet example of a what-if query for a cash flow problem Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

41. 41 Goal Seeking n Backward solution approach n Example: Figure 2.10 What interest rate causes an the net present value of an investment to break even? n In a DSS the what-if and the goal-seeking options must be easy to perform Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

42. 42 The Implementation Phase There is nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things (Machiavelli, 1500s) *** The Introduction of a Change *** Important Issues n Resistance to change n Degree of top management support n Users’ roles and involvement in system development n Users’ training Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

43. 43 How Decisions Are Supported Specific MSS technologies relationship to the decision making process (see Figure 2.10) n Intelligence: DSS, ES, ANN, MIS, Data Mining, OLAP, EIS, GSS n Design and Choice: DSS, ES, GSS, Management Science, ANN n Implementation: DSS, ES, GSS Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

44. 44 Alternative Decision Making Models n Paterson decision-making process n Kotter’s process model n Pound’s flow chart of managerial behavior n Kepner-Tregoe rational decision-making approach n Hammond, Kenney, and Raiffa smart choice method n Cougar’s creative problem solving concept and model n Pokras problem-solving methodology n Bazerman’s anatomy of a decision n Harrison’s interdisciplinary approaches n Beach’s naturalistic decision theories Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

45. 45 Naturalistic Decision Theories n Focus on how decisions are made, not how they should be made n Based on behavioral decision theory n Recognition models n Narrative-based models Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

46. 46 Recognition Models n Policy n Recognition-primed decision model Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

47. 47 Narrative-based Models (Descriptive) n Scenario model n Story model n Argument-driven action (ADA) model n Incremental models n Image theory Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

48. 48 Other Important Decision- Making Issues n Personality types n Gender n Human cognition n Decision styles Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ