1 Information Systems Project Management ISQS 4350 Zhangxi Lin.
1 Business System Analysis & Decision Making - Lecture 10 Zhangxi Lin ISQS 5340 July 2006.
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Transcript of 1 Business System Analysis & Decision Making - Lecture 10 Zhangxi Lin ISQS 5340 July 2006.
![Page 1: 1 Business System Analysis & Decision Making - Lecture 10 Zhangxi Lin ISQS 5340 July 2006.](https://reader035.fdocuments.in/reader035/viewer/2022062803/56649f0c5503460f94c1f6f7/html5/thumbnails/1.jpg)
1
Business System Analysis & Decision Making- Lecture 10
Zhangxi Lin
ISQS 5340
July 2006
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2
Modeling Uncertainty
Probability Review Using Data
Histograms Descriptive Statistics Regression
Value of Information Conditional Probability and Bayes’ Theorem Expected Value of Perfect Information Expected Value of Imperfect Information
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3
Probability Review
P(A|B) = P(A and B) / P(B) “Probability of A given B”
Example, there are 40 female students in a class of 100. 10 of them are from some foreign countries. 20 male students are also foreign students. Even A: student from a foreign country Even B: a female student
If randomly choosing a female student to present in the class, the probability she is a foreign student: P(A|B) = 10 / 40 = 0.25, or P(A|B) = P (A & B) / P (B) = (10 /100) / (40 / 100) = 0.1 / 0.4 = 0.25
That is, P(A|B) = # of A&B / # of B = (# of A&B / Total) / (# of B / Total) = P(A & B) / P(B)
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4
Venn Diagrams
Female(30)
ForeignStudent(20)
Female foreign student (10)
(10)
30+10 = 40 20+10 = 30
Male non-foreign student(40)
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5
Probability Review
Complement )(1)( APAP
Female
Foreignstudent
Non Female
Non ForeignStudent
)(1)( BPBP
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6
Bayes’ Theorem
)&()()|()(
)&()|(
)&()()|()(
)&()|(
BAPAPABPAP
BAPABP
BAPBPBAPBP
BAPBAP
)()|()()|(
)()|(
)(
)()|()|(
)()|()()|(
BPBAPBPBAP
BPBAP
AP
BPBAPABP
APABPBPBAP
So:
The above formula is referred to as Bayes’ theorem. It is extremely Useful in decision analysis when using information.
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7
Using Data
We have addressed briefly behavioral judgments and theoretical probability issues under certainty and uncertainty. We now consider how to use data to conduct our decision analysis.
Why need data? No data no decision. Think about why search engine is
so hot. How to make data useful
IT helps us to cope with information explosion Models and methods are important to guide us how to
analyze data.
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8
Histograms
Bin Math Video
<=2 4 0
3-5 6 1
6-8 9 10
>8 3 11
0
2
4
6
8
10
12
<=2 3-5 6-8 >8
Math
Video
The histogram is based on the survey dataFrom the ISQS 5340 class
The scale: 1-10 indicatingStrong negative to strong positive response to the survey questions.
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Descriptive Statistics
Math Video Note
Mean 5.59091 8.59091 The average of the data
Median 6 8.5
Standard Deviation 2.61241 1.46902 Dev = V0.5
Sample Variance 6.82468 2.15801 V = (x- mean)2 / # of obs
Range 9 5
Minimum 1 5
Maximum 10 10
Sum 123 189
Count 22 22
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10
The Relationship between Data
Chart Title
0
2
4
6
8
10
12
0 2 4 6 8 10 12
Case
Vid
eo
Case-Video
Linear (Case-Video)
Math-Video
0
2
4
6
8
10
12
0 2 4 6 8 10 12
Math
Vid
eo
Math-Video
Linear (Math-Video)
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11
Regression
Y = a + b*X Example: Video_point = a + b*Math_point
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Regression: Math - Video
Regression Statistics
R Square 0.0248
Standard Error 1.487
Observations 22
Coefficie
ntsStandard
Error t Stat P-valueLower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 9.086 0.7632 11.9051.56E-
10 7.494 10.678 7.494 10.678
X Variable 1 -0.0885 0.1242 -0.713 0.4843 -0.3475 0.1705 -0.348 0.1705
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Regression: Case - Video
Regression Statistics
R Square 0.227
Standard Error 1.323
Observations 22
Coefficie
ntsStandard
Error t StatP-
value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 6.623 0.859 7.708 2E-07 4.83 8.415 4.83 8.415
X Variable 1 0.297 0.122 2.425 0.025 0.042 0.552 0.042 0.552
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Value of Information
When facing uncertain prospects we need information in order to reduce uncertainty
Information gathering includes consulting experts, conducting surveys, performing mathematical or statistical analyses, etc.
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Use insurancePay $100+$2 = $102
Not use insurancePay $100
Buyer
$18Good
Bad- $100
Bad
0.99
0.01
0.99
0.01
Good
Expected Value of Perfect Information (EVPI)
$20
- $2
Revisit the previous question: An buyer is to buy something online
EMV = $18.8
EMV = $17.8
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Expected Value of Imperfect Information (EVII) We rarely access to perfect information, which is common. Thus
we must extend our analysis to deal with imperfect information. Now suppose we can access the online reputation to estimate
the risk in trading with a seller. Someone provide their suggestions to you according to their
experience. Their predictions are not 100% correct: If the product is actually good, the person’s prediction is 90%
correct, whereas the remaining 10% is suggested bad. If the product is actually bad, the person’s prediction is 80%
correct, whereas the remaining 20% is suggested good. Although the estimate is not accurate enough, it can be used to
improve our decision making: If we predict the risk is high to buy the product online, we
purchase insurance
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17
Decision Tree
Buyer
Insurance
No Ins
Insurance
No Ins
Bad (?)
Good (?)Predict: Good (?)
Predict: Bad (?)
Questions:
1. Given the suggestion
What is your decision?
2. What is the probability
wrt the decision you made?
3. How do you estimate
The accuracy of aSuggestion?
Bad (?)
Good (?)
Bad (?)
Good (?)
Bad (?)
Good (?)
$18
- $100
$20
- $2
$18
$20
- $2
- $100
Extended from the previous online trading question
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Applying Bayes’ Theorem Let “Good” be even A Let “Bad” be even B Let “Suggest Good” be event G Let “Suggest Bad” be event W According to the previous information, we know:
P(G|A) = 0.9, P(W|A) = 0.1 P(W|B) = 0.8, P(G|B) = 0.2 P(A) = 0.99, P(B) = 0.01
We want to learn the probability the outcome is good providing the suggestion is “good”. i.e. P(A|G) = ?
We want to learn the probability the outcome is bad providing the suggestion is “bad”. i.e. P(B|W) = ?
We may apply Bayes’ theorem to solve this with imperfect information
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Applying Bayes’ Theorem
According to previous formula, we have P(A|G) = P(G|A)P(A) / P(G)
= P(G|A)P(A) / [P(G|A)P(A) + P(G|B)P(B)]= P(G|A)P(A) / [P(G|A)P(A) + P(G|B)(1 - P(A))]= 0.9 * 0.99 / [0.9 * 0.99 + 0.2 * 0.01]= 0.9978 > 0.99
P(B|W) = P(W|B)P(B) / P(W) = P(W|B)P(B) / [P(W|B)P(B) + P(W|A)P(A)]= P(W|B)P(B) / [P(W|B)P(B) + P(W|A)(1 - P(B))]= 0.8 * 0.01 / [0.8 * 0.01 + 0.1 * 0.99]= 0.0748 > 0.01
Apparently, the suggestion provide better information than the original probability
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Decision Tree
Buyer
Insurance
No Ins
Insurance
No Ins
Bad (0.0022)
Good (0.9978)Predict: GoodP(G) = 0.893
Predict: Bad P(W) = 0.107
Bad (0.0748)
Good (0.9252)
Bad (0.0748)
Good (0.9252)
Bad (0.0022)
Good (0.9978) $18
- $100
$20
- $2
$18
$20
- $2
- $100
EMV = $19.87Your choice
EMV = $17.78
EMV = $11.03
EMV = $16.50Your choice
With the help of other people’s suggestion your decision making accuracy is improved
P(Good) = 0.99, P(Bad) = 0.01
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Exercise 3 There is only two events in a scenario: A and B. If P(A) = 0.7, P(B) = 0.5, P(A|B)
= 0.4, and P(A & B) = 0.2, calculate P(B|A). You are to buy a new digital camera. It costs $400 (but worth $600 to you). You
are offered to buy a 3-year warrantee for $50, which allows you to exchange for a brand new camera if your camera get any problem. Otherwise, your camera could be useless if it stops working. To decide if this is necessary, you ask your friend for advice. You friend can provide a correct advice with 80% probability if the camera will be in good quality. He can also identify the possible quality problem with 70% probability, which will encourage you to buy the warrantee. You know the probability that the camera will have problems in a period of 3 years is 10%.
(1) Draw a decision tree Calculate the conditional probability that you buy a good camera given that your friend
provide a positive advice. Calculate the conditional probability you buy a camera in poor quality given that your
friend provide a negative advice. Calculate EMVs under different situations
(2) If you have a utility function U(x) = x0.6, without the advice, what will be your choice? Compare the difference between the solution with the one from (1)
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Homework #3 Suppose you have three choices of investment:
High risk stock with a 0.5 probability of making $10,000 if the market will be up, a 0.3 probability of making $100 if the market is flat, and a 0.2 probability of losing $1,600 if the market is down.
Low risk stock with a 0.5 of probability making $3,600 if the market will be up, a 0.3 probability of making $900 if the market is flat, and a 0.2 probability of losing $625 if the market is down.
You can also save the money in the saving account making $2500 Draw a decision tree Calculate the EMV and make your decision If the utility function is U(x) = +x0.6, what are expected utilities of the
choices? Which one should be your choice? Explain why the decision outcomes are different wrt different criteria.