Economics in IS/IT decision-making

1. Economics in IS/IT decision-making 961763 蔡昀芷 961744 張耀元 961725 徐耀宗 961707 牛大維 961714 徐詩婷 941617 黃若靜 961750 涂書維 941621 江秉憲 961618 陳聖祥 941725 劉得準 961755 吳偉豪 941746 陳建翔

2. Decision Tree 961763 蔡昀芷

3. What is Decision Tree A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.

4. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. Another use of decision trees is as a descriptive means for calculating conditional probabilities.

5. SAMPLE Wang is a manager of one famous golf club. He wants to know whether the weather is about the reasons for people to play golf. So that he could adjust the employee .

8. Analysis According to the graph we can easily to understand when the weather become worse , the customers would like stay at home or find another thing to do . Seldom of customers would play golf in bad weather .

9. Advantage Easy to understand and implement Are simple to understand and interpret. Have value even with little hard data. Can be combined with other decision techniques.

10. Disadvantage When there is a lot of event will effect the result , then we need to draw a big graph And users is difficult to find reasons why the data change in complex graph .

11. Scoring Model 961725 徐耀宗

12. What is Scoring Model determine the importance of each criterion to enterprises：This method is initially related to the team based on the organization's goals and strategies to decide to consider the Criterion, then by mutual discussions to determine the importance of each criterion to enterprises, and individual giving different weight Different organization in a different environment, the criteria that are different weights：for the different investment options, calculate the points from each of the criteria and get the weight multiplied by the total score for each program in order to score to determine the level of priority investment projects

13. Why Business use Scoring Model many types of business value: As the business value of IT has brought many species, each different IT investment program for Key Performance Index contribution to different degrees. determine the order of investment：in this multi-objective , Multiple Criterion decision-making situation, how to determine the order of investment? provide a mathematical model: This multi-criteria analysis can be used to weigh project alternatives and provide a mathematical model for selection of projects or products.  keep the decision factors narrowed：The number of requirements and constraints is not important, but it is important to keep the decision factors narrowed to those the organization views as most important. 

14. Scoring Model Rules Rule 1： The total weight for requirements and constraints should equal 100 Requirements+Constraints=Total=100

15. Scoring Model Rules Rule 2： Total Requirements weight = 50 Total Constraints weight = 50

16. Scoring Model Rules Rule 3：use 1,3,9 scoring model because it forces tough decisions and creates separation in the model.

17. Scoring Model Rules Rule 4： The individual weights assigned to individual requirements and constraints are a matter of negotiation among the project selection team.  In  this model, larger numbers indicate the  project is better along that line item. Scoring = Weight × Rating

18. Scoring Model Rules Rule 5： The scoring and summary columns are calculated fields and should not be modified

19. Example The organization has determined that X system is needed.  We have been asked to evaluate three platforms using the organization’s multi-criteria analysis. 

20. Step 1： requirements and constraints have been set based on negotiation and review of the organization’s needs.

21. Step 2, weights for the criteria are applied based on negotiated values.

22. Step 3, alternatives are selected from a “consideration set” and ratings are applied, giving us a final total.  The larger number is the best

23. pros and cons pros： Implement a scoring scheme that is easy to understand and manage. cons： should have quarterly meetings to review the scoring model and tune it based on results from the previous quarter.

24. Analytical Hierarchy Process (AHP) 961714 徐詩婷

25. What is AHP? • A structured technique for dealing with complex decisions. • Rather than prescribing a "correct" decision, the AHP helps the decision makers find the one that best suits their needs and their understanding of the problem. • The AHP provides a comprehensive and rational framework for structuring a decision problem, for representing and quantifying its elements, for relating those elements to overall goals, and for evaluating alternative solutions.

26. A simple AHP hierarchy

27. The procedure for using the AHP • Model the problem as a hierarchy containing the decision goal, the alternatives for reaching it, and the criteria for evaluating the alternatives. • Establish priorities among the elements of the hierarchy by making a series of judgments based on pairwise comparisons of the elements. • Synthesize these judgments to yield a set of overall priorities for the hierarchy. • Check the consistency of the judgments. • Come to a final decision based on the results of this process.

28. Example • In the highly competition market, the organization can attract consumers attention by establishing good brand image. Now, we have to evaluate three alternatives and some criteria which will affect the brand image by AHP. There are three alternatives have been considered: 1. Advertisement 2. Brand representative 3. Placement marketing

29. Step1: Model the problem as a hierarchy • It consists of an overall goal, a group of options or alternatives for reaching the goal, and a group of factors or criteria that relate the alternatives to the goal. Goal Criteria Alternatives

30. Step 2: Establish priorities among the elements of the hierarchy Pairwise comparisons • Once the hierarchy is built, the decision makers systematically evaluate its various elements by comparing them to one another two at a time. • In making the comparisons, the decision makers can use concrete data about the elements, or they can use their judgments about the elements' relative meaning and importance. • It is the essence of the AHP that human judgments, and not just the underlying information, can be used in performing the evaluations.

31. The Fundamental Scale of AHP

32. The Fundamental Scale of AHP • The AHP converts these evaluations to numerical values that can be processed and compared over the entire range of the problem. • A numerical weight or priority is derived for each element of the hierarchy, allowing diverse and often incommensurable elements to be compared to one another in a rational and consistent way.

33. Step 2 Pairwise comparisons of hierarchy 1

34. Step 3: Synthesize these judgments to yield a set of overall priorities for the hierarchy. 1. Sum up all of entry in the same matrix column; 2. Normalized matrix: divide each entry by the sum the same matrix column; and 3. Relative importance of attribute (Wi1): average of each normalized matrix row means attribute’s weight and W is a n*1 matrix Eqn (1)

35. 1. Sum up all of entry in the same matrix column

36. 2. Normalized matrix: divide each entry by the sum the same matrix column

37. 3. Relative importance of attribute (Wi1) n=4 (a): 0.546+0.6+0.471+0.445 = 2.062 (b): 0.181+0.2+0.235+0.222 = 0.838 (c): 0.136+0.1+0.235+0.222 = 0.693 (d): 0.136+0.1+0.059+0.111 = 0.406 Eqn (1)

38. Step 4:Check the consistency of the judgments • Verification on consistency is to inspect the match between subjective decision-making results and the logical transitivity. • Consistency index (C.I.) is the measurement indictor for pairwise consistency and the formula is as follow Eqn (2)

39. Maximal Eigenvalue： Eqn (3) 1. Calculate weighted supermatrix( ) Wi1 from Table2.4 Table2.1 2. The result divided by the average ：

40. C.I. ≤ 0.1, this pairwise comparison matrix is acceptable. 3. Calculate the maximal or principal eigenvalue 4. Calculate Consistency Index (C.I.)

41. 0.54 Do step 2 to 4 over the hierarchy 2

42. Step 5: Come to a final decision based on the results of this process. • Numerical priorities are calculated for each of the decision alternatives. • These numbers represent the alternatives' relative ability to achieve the decision goal, so they allow a straightforward consideration of the various courses of action.

43. 0.30 ↓Table 5.1 The weighted value of each alternatives. Table 4.1 Average Table 4.2 Average Table 4.3 Average Table 4.4 Average ↓Table 5.2 The weighted value of each criteria (from Table 2.4). • Value for Advertisement: • 0.24*0.515+0.33*0.210+0.16*0.173+0.30*0.102 = 0.251 • Value for Brand representative: • 0.14*0.515+0.57*0.210+0.54*0.173+0.16*0.102 = 0.302 • Value for Placement marketing: • 0.64*0.515+0.10*0.210+0.32*0.173+0.54*0.102 = 0.460

44. Result • In the evaluation of expenditure : So, the decision-maker can choose the placement marketing as the alternative to establishing brand image. Placement marketing > Brand representative > Advertisement

45. Application - Decision Making Problems • Planning • Generating a Set of Alternatives • Setting Priorities • Choosing a Best Alternatives • Allocating Resources • Determining Requirements • Predicting Outcomes/Risk Assessment • Designing Systems • Measuring Performance • Insuring the Stability of a System • Optimization • Resolving Conflict

46. Advantages of AHP • Systematized the complex problems from different perspectives. • Improve the communication between team members. • Provide decision-makers choose the appropriate program by the comprehensive assessment.

47. Disadvantages of AHP • As the simplification of the hierarchy structure, some important dependencies may be hided and the decision-making might be over-simplification. • It's hard to compare the attributes between tangible and Intangible.

48. Regression Analysis 961750涂書維

49. About Regression Analysis In statistics, regression analysis helps us understand the relationship between a dependent variable and one or more independent variables. In economics, regression analysis is widely used for understanding the relationship between factors, or prediction and forecasting.

50. Predictions by Regression • Three goals • Modeling :Making assumptions since the true form of the data-generating process is not known. • Predicting :Models for prediction are often useful even when the assumptions are moderately violated. It gives us a acceptable and reasonable result. • Characterization : Howone task leads to and justifies the other tasks.