decision analysis (part i)

13
Core Purpose: To Enable Organisations Become Ha Decision Analysis 4 th November 2013

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Page 1: Decision analysis (Part I)

Core Purpose: To Enable Organisations Become Happier

Decision Analysis

4th November 2013

Page 2: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

What is Decision Analysis?

• A quantitative framework for making decisions

• Selection of a decision from a set of possible decision alternatives when uncertainties regarding the future exist

• Goal is to optimize the resulting payoff in terms of a decision criterion

Page 3: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

Decision Models

• Deterministic models

• Probabilistic models• Decision-making under pure uncertainty

• Maxmin

• Maxmax

• Minmax

• Decision-making under risk

• Expected value criterion

• Expected value of perfect information

• Bayesian analysis

Page 4: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

Case Study

States of nature

>1000 points 300-1000 +/-300 -300 to -

1000<-1000 points

Large rise Small rise No change Small fall Large fall

Alternatives

Bonds 9% 7% 6% 0% -1%

Stocks 17% 9% 5% -3% -10%

Fixed deposit 7% 7% 7% 7% 7%

Page 5: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

MaxMin

Pessimistic approach based on worst case scenario

1. Write min for each row

2. Choose max of the above

States of nature

>1000 points

300-1000 +/-300 -300 to -

1000<-1000 points

Large rise

Small rise

No change Small fall Large fall Min

Alternatives

Bonds 9% 7% 6% 0% -1% -4%

Stocks 17% 9% 5% -3% -10% -10%

Fixed deposit 7% 7% 7% 7% 7% 7%

Page 6: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

MaxMax

Pessimistic approach based on worst case scenario

1. Write max for each row

2. Choose max of the above

States of nature

>1000 points

300-1000 +/-300 -300 to -

1000<-1000 points

Large rise

Small rise

No change Small fall Large fall Max

Alternatives

Bonds 9% 7% 6% 0% -1% 9%

Stocks 17% 9% 5% -3% -10% 17%

Fixed deposit 7% 7% 7% 7% 7% 7%

Page 7: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

MinMax

Pessimistic approach to minimize regret or opportunity loss

1. Take the largest number in each coloumn

2. Subtract all the numbers in the coloumn from it

3. Choose maximum number for each option

4. Choose minimum number from step 3

Page 8: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

Case Study

States of nature

>1000 points 300-1000 +/-300 -300 to -

1000<-1000 points

Large rise Small rise No change Small fall Large fall

Alternatives

Bonds 9% 7% 6% 0% -1%

Stocks 17% 9% 5% -3% -10%

Fixed deposit 7% 7% 7% 7% 7%

Page 9: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

Regret Matrix

States of nature

>1000 points 300-1000 +/-300 -300 to -

1000<-1000 points

Large rise Small rise No change Small fall Large fall

Alternatives

Bonds (17%-9%) (9%-7%) (7%-6%) (7%-0%) (7%+1%)

Stocks (17%-17%) (9%-9%) (7%-5%) (7%+3%) (7%+10%)

Fixed deposit (17%-7%) (9%-7%) (7%-7%) (7%-7%) (7%-7%)

Page 10: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

Regret Matrix

States of nature

>1000 points 300-1000 +/-300 -300 to -

1000<-1000 points

Large rise Small rise No change Small fall Large fall Max

Alter

natives

Bonds 8% 2% 1% 7% 8% 8%

Stocks 0% 0% 2% 10% 17% 17%

Fixed deposit 10% 2% 0% 0% 0% 10%

Page 11: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

Applications

• Project solution selection

• Make or buy decisions

Page 12: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

References

• University of Baltimore: http://home.ubalt.edu/ntsbarsh/opre640a/partIX.htm

• John Wiley & Sons

Page 13: Decision analysis (Part I)

Training | Mentoring | Data Analytics | Execution | Deployment

Thanks!!!