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

2 Outline Introduction Introduction System System Modeling Modeling How decision How decision Cognition Cognition Decision makers Decision makers Summary Summary

3 1. Introduction  Decision making  Decision making and problem solving  Decision making disciplines

4 1.1 Some Concepts in Decisions of Enterprise The decision may be made by a group The decision may be made by a group Group members may have biases Group members may have biases There are several possibly conflicting objectives There are several possibly conflicting objectives Decision makers are interested in evaluating what-if scenarios Decision makers are interested in evaluating what-if scenarios ….etc. ….etc.

5 2. Systems What is systems? What is systems? Level (or Hierarchy) Level (or Hierarchy)

6 2.1 The structure of a system Three distinct parts of systems (Figure 2.1) (Figure 2.1)(Figure 2.1) Inputs Inputs Processes Processes Outputs Outputs Besides these … Three parts are surrounded by an environment and often include a feedback mechanism. In addition, a human decision maker is considered part of the system.

7 2.2 One way to identify the elements of the environment Two questions ： (Churchman, 1975) 1. Does the element matter relative to the system ’ s goals? [YES] system ’ s goals? [YES] 2. Is it possible for the decision maker to significantly manipulate this elements? [NO] significantly manipulate this elements? [NO]

8 2.3 The Boundary A system is separated from its environment by a boundary. A system is separated from its environment by a boundary. The system is inside the boundary, whereas the environment lies outside. The system is inside the boundary, whereas the environment lies outside.

9 2.4 Closed and Open Systems Closing the system Closing the system Closed System Closed System Open System Open System

Information System An information system collects, processes, stores, analyzes, and disseminates information for a specific purpose. An information system collects, processes, stores, analyzes, and disseminates information for a specific purpose.

11 3. Models Simplified representation or abstract Simplified representation or abstract The reality is too complex The reality is too complex The classification of models ： The classification of models ： 1. Iconic models 1. Iconic models 2. Analog models 2. Analog models 3. Mathematical models 3. Mathematical models

The benefits of Models 1. Compression of time 2. Model manipulation is easier than real system 3.Lower cost 4. Lower cost in trial-and-error experiment 5. Be used to estimate the risks 6. Analyzing large number of possible solutions 7. Help learning & training

The Modeling Process Solution Approaches Trial-and-error Trial-and-error Simulation Simulation Optimization Optimization Heuristics Heuristics Decision-Making Process (Simon, 1977) Intelligence phase Intelligence phase Design phase Design phase Choice phase Choice phase Implementation phase Implementation phase (Figure 2.2)

The Intelligence Phase 1. Finding the Problem 2. Problem Classification － Programmed vs Nonprogrammed problems － Programmed vs Nonprogrammed problems 3.Problem Decomposition 4.Problem Ownership

The Design Phase Finding, developing, and analyzing courses of action Finding, developing, and analyzing courses of action Construct, test, validate a model of decision-making Construct, test, validate a model of decision-making Model － conceptualization of problem Model － conceptualization of problem abstraction to abstraction to quantitative/qualitative forms quantitative/qualitative forms

16 Some topics of modeling (relate to quantitative model) Some topics of modeling (relate to quantitative model) 1. The components of the model 1. The components of the model 2. The structure of the model 2. The structure of the model 3. Selection of a principle of choice 3. Selection of a principle of choice 4. Developing alternatives 4. Developing alternatives 5. Predicting outcomes 5. Predicting outcomes 6. Measuring outcomes 6. Measuring outcomes 7. Scenarios 7. Scenarios

The Component of Quantitative Model Uncontrollable variables Mathematical relationships Decision Variables Result variables

18 Variables Intermediate Result Variables Intermediate Result Variables AreaDecisionVariablesResultVariablesUncontrollablevariables FinancialinvestmentInvestmentamountsTotalprofitInflationrate Marketing Where to advertiseMarketshare Customer ’ s income

The Structure of Quantitative Models The Product-Mix Linear Programming Model MBI Corporation MBI Corporation Decision: How many computers to build next month? Decision: How many computers to build next month? Two types of computers Two types of computers Labor limit Labor limit Materials limit Materials limit Marketing lower limits ConstraintCC7CC8RelLimit Labor (days) =100 Units1>=200 Profit $8,00012,000Max Objective: Maximize Total Profit / Month Marketing lower limits ConstraintCC7CC8RelLimit Labor (days) =100 Units1>=200 Profit $8,00012,000Max Objective: Maximize Total Profit / Month

20 Linear Programming Model Components Decision variables X1,X2 Result variable Z Uncontrollable variables (constraints) Components Decision variables X1,X2 Result variable Z Uncontrollable variables (constraints) Solution X 1 = X 2 = 200 Profit = $5,066,667 Solution X 1 = X 2 = 200 Profit = $5,066,667

Selection of a Principle of Choice Describe the acceptability of a solution approach Describe the acceptability of a solution approach 1. Normative models 1. Normative models 2. Suboptimization 2. Suboptimization 3. Descriptive models 3. Descriptive models 4. Good enough or satisficing 4. Good enough or satisficing ※ Bounded rationality ※ Bounded rationality

Developing (Generating) Alternatives It ’ s necessary to generate alternatives manually It ’ s necessary to generate alternatives manually Searching & creativity － Taking time & costing money Searching & creativity － Taking time & costing money Searching comes after the criteria for evaluating the alternatives Searching comes after the criteria for evaluating the alternatives

Predicting the Outcome of Each Alternative Classify the knowledge into three categories ←Increasing knowledge ←Increasing knowledge Complete Risk Ignorance knowledge Decreasing knowledge→ Decreasing knowledge→

24 Decision Making Under Certainty Decision Making Under Certainty The decision maker is a perfect predictor of the The decision maker is a perfect predictor of the future future Decision Making Under Risk Decision Making Under Risk The decision maker have to consider possible The decision maker have to consider possible outcomes for each alternative outcomes for each alternative Calculating and selecting the best expected value Calculating and selecting the best expected value of an alternative → Risk Analysis of an alternative → Risk Analysis Decision Making Under Uncertainty Decision Making Under Uncertainty The decision maker doesn ’ t know about possible The decision maker doesn ’ t know about possible outcomes outcomes

Measuring Outcomes For example ： For example ： Profit is an outcome Profit maximization is a goal ※ But units of outcomes and goals are the same

Scenarios A statement of assumptions about the operating environment of a system A statement of assumptions about the operating environment of a system Be helpful in simulation & what-if analysis Be helpful in simulation & what-if analysis In MSS, scenarios play an important role. In MSS, scenarios play an important role. (Potential opportunities, problem areas, (Potential opportunities, problem areas, flexibility in planning) flexibility in planning)

The Choice Phase Search Approaches Search Approaches ─ Analytical techniques ─ Algorithms ─ Blind and heuristic search approach

Evaluation: Multiple Goals, Sensitivity Analysis, What-If, and Goal Seeking Multiple goals: Multiple goals: Conflicts ？

29 Sensitivity Analysis: Sensitivity Analysis: ─ Automatic sensitivity analysis ─ Trial and error AHP reference AHP reference

30 Trial and Error: Trial and Error: ─ What-if analysis (Figure 2.9) ─ Goal seeking (Figure 2.10)

The Implementation Phase At more than 400 years ago, Machiavelli said: 『 “ nothing more difficult carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things. ” 』

32 4. How Decisions Are Supported Intelligence Design Choice Implementation ANN MIS EIS GDSS ANN Management science GDSS

33 5. Cognitive Cognition theory Cognition theory Cognitive style Cognitive style Decision style (table 2.4) Decision style (table 2.4)

34 6. The Decision Makers Individual Individual Group Group

35 7. Summary Managerial making is synonymous with the whole process of management Managerial making is synonymous with the whole process of management Problem solving is also opportunity evaluation Problem solving is also opportunity evaluation Systems can be open, interacting with their environment, or closed. Systems can be open, interacting with their environment, or closed. ….etc ….etc

36 input(s) feedback environment output(s) boundary 轉換過程