Decision Tree Analysis for statistical tool. The deck provides understanding on the Decision Analysis. It provides practical application and limited theory. Will be useful for MBA students.

1. Decision Analysis
5th Apr 2014

2. Agenda
1. Objective
2. Literature Review
3. Decision making
 Overview
 Decision making Environment
 Decision Making Criteria
4. Models
5. Case
6. References
2

3. Objective
How the Decision Analysis can help in
decision making in the face of uncertainty
3

4. Literature Review
Decision analysis provides a framework and
methodology for rational decision making
when the outcomes are uncertain.
4

5. WCB
Worker’s Compensation Board of British
Columbia, Canada
Over 1,65,000 employers
1.8 million workers
Spends US$1 billion p.a.
Objective – Improve service & Reduce cost
5

6. WCB
Applying Decision Analysis with decision trees WCB is now saving approximately US $4
million per year while also enabling some injured workers return to work sooner
Source: Ernest Urbanovich, Ella E. Young, Martin L. Puterman, Sidney O. Fattedad, (2003) Early Detection of High-Risk Claims at the
Workers' Compensation Board of British Columbia. Interfaces 33(4):15-26.
http://dx.doi.org/10.1287/inte.33.4.15.16372 6

7. Westinghouse
Westinghouse Science and Technology
Center
R&D Arm to develop new technology
Objective – Deliver high impact technology
quickly & Reduce cost
7

8. Westinghouse
OR team developed a decision tree approach to analyzing any R&D proposal while
considering its complete sequence of key decision points.
A decision tree with a progression of decision nodes and intervening event nodes
provided a natural way of depicting and analyzing such an R&D project.
Source: Robert K. Perdue, William J. McAllister, Peter V. King, Bruce G. Berkey, (1999) Valuation of R and D Projects Using Options
Pricing and Decision Analysis Models. Interfaces 29(6):57-74.
http://dx.doi.org/10.1287/inte.29.6.57
8

9. ConocoPhillips
Conoco Inc. and Phillips Petroleum
company
3rd largest integrated energy co. in US
Objective – Judicious Allocation of
investment capital across a set of
exploration projects
9

10. Westinghouse
In early 1990’s – Industry leader
Source: Michael R. Walls, G. Thomas Morahan, James S. Dyer, (1995) Decision Analysis of Exploration Opportunities in the
Onshore US at Phillips Petroleum Company. Interfaces 25(6):39-56.
http://dx.doi.org/10.1287/inte.25.6.39
10
in application of OR
methodology
DISCOVERY – decision
analysis s/w package
• Evaluate exploration projects
• Rank Projects
• Budget Consideration

11. Should You Ask?
Sir, why is my coursework
marks so low? I deserve
higher marks. Hehehe!

12. Which Mobile Phone should I buy?
What are the things
you consider before
making a decision?

13. Whom should I marry?
What are the things
you consider before
making a decision?

14. Decision
A general approach to decision
making that is suitable to a wide
range of operations management
decisions:
14
Capacity
planning
Product
and service
design
Equipment
selection
Location
planning

15. Decision Making Overview
Decision Making
Decision Environment Decision Criteria
Certainty Nonprobabilistic
Uncertainty Probabilistic
15

16. The Decision Environment
Decision Environment Certainty: The results of decision
16
Certainty
Uncertainty
alternatives are known
Example:
Must print 10,000 color brochures
Offset press A: $2,000 fixed cost
+ $.24 per page
Offset press B: $3,000 fixed cost
+ $.12 per page
*

17. The Decision Environment
17
Decision Environment
Certainty
Uncertainty
Uncertainty: The outcome that will occur
after a choice is unknown
Example:
You must decide to buy an item now or wait.
If you buy now the price is $2,000. If you
wait the price may drop to $1,500 or rise to
$2,200. There also may be a new model
available later with better features.
*
(continued)

18. Decision Criteria
Nonprobabilistic Decision Criteria: Decision Decision Criteria
rules that can be applied if the probabilities of
uncertain events are not known.
18
Nonprobabilistic
Probabilistic
*
 maximax criterion
 maximin criterion
 minimax regret criterion

19. Decision Criteria
19
Decision Criteria
Nonprobabilistic
Probabilistic
*
Probabilistic Decision Criteria: Consider the
probabilities of uncertain events and select
an alternative to maximize the expected
payoff of minimize the expected loss
 maximize expected value
 minimize expected opportunity loss
(continued)

20. Step 1
Identify
possible
future
conditions
or state of
nature
Develop a
list of
possible
alternatives
Determine
the payoff
associated
with each
alternative
for every
possible
future
condition
Estimate
the
likelihood of
each
possible
future
conditions
Evaluate
alternatives
based to
some
decision
criterion,
and select
the best
alternative
Decision Making Process:
Step 2 Step 3 Step 4 Step 5

21. 21
Decision Models
Decision
Models
Payoff
Matrix
Decision
Tree

22. Decision Illustration
22
Sunny wants to join WMP from IIM Lucknow, Noida Campus.
She is hopeful that if after completion of course she will get better opportunity and
her salary will be INR 50,00,000, if the economy is good. If the economy is average,
she will get a salary of Rs. 40,00,000. If economy is bad she will get Rs. 30,00,000. The
fee for course is approx. Rs. 8,10,000. Also she estimates that there would be some
incidental expenses of Rs. 2,90,000 on commuting etc.
In case she does not enroll for the course she will get increment on her current salary
of Rs. 20,00,000 @ 30%, 20% or 10% incase of economy is good, average or bad
during the duration of the course. The probability of economy to be good or bad is
30% each and to be average is 40%

23. 23
Payoff Table
A payoff table provides alternatives,
states of nature, and payoffs
Alternative
(Action)
Salary in INR 100,000
Choice (Action)
Good
Economy
Average
Economy
Bad
Economy
Join 39 29 19
Not Join 26 24 22
Probabilities 0.3 0.4 0.3

24. Decision Making - Criteria
24
• Maximax
– An optimistic decision criteria
• Maximin
– A pessimistic decision criteria
• Minimax Regret
– Minimum of worst regrets
• Expected Monetary Value (EMV)
– The expected profit for taking action
• Expected Opportunity Loss (EOL)
– The expected opportunity loss for taking action.
• Expected Profit Under Certainty (EPUC)
– The expected opportunity loss from the best decision
• Expected Value of Perfect Information (EVPI)
– The expected opportunity loss from the best decision

25. 25
Decision Tree
Decision Tree
A Decision Tree is a chronological
representation of the decision process.
A Visual Representation of
Alternatives, Payoffs, and
Probabilities.
25

26. Decision Tree
• A Decision Tree is a chronological representation
of the decision process.
• The tree is composed of nodes and branches.
A branch emanating from a state of
nature (chance) node corresponds to a
particular state of nature, and includes
the probability of this state of nature.
Decision
node
Chance
node
P(S2)
P(S2)
A branch emanating from a
decision node corresponds to a
decision alternative. It includes a
cost or benefit value.
26

27. Decision Tree
50L
40L
25L
26L
24L
22L
29L
24L
0.3
0.4
0.3
0.3
0.4
0.3
29L
11L
Join WMP
Decision Point
Action
Expected
Value
27

28. 28
Kaun Banega Crorepati
You are a contestant on “Kaun Bangega Crorepati?” You already have answered the Rs.
25L question correctly and now must decide if you would like to answer the Rs. 50L
question. You can choose to walk away at this point with Rs. 25L in winnings or you may
decide to answer the Rs. 50L question. If you answer the Rs. 50L question correctly, you
can then choose to walk away with Rs. 50L in winnings or go on and try to answer the Rs.
100L question. If you answer the Rs. 100L question correctly, the game is over and you
win Rs. 100L. If you answer either question incorrectly, the game is over immediately and
you take home “only” Rs. 3.2L.
You have the “phone a friend” lifeline remaining. With this option, you may phone a
friend to obtain advice on the correct answer to a question before giving your answer. You
may use this option only once (i.e., you can use it on either the Rs. 50L question or the Rs.
100L). Since some of your friends are smarter than you are, “phone a friend” significantly
improves your odds for answering a question correctly. Without “phone a friend,” if you
choose to answer the Rs. 50L question you have a 65% chance of answering correctly,
and if you choose to answer the Rs. 100L question you have a 50% chance of answering
correctly (the questions get progressively more difficult). With “phone a friend,” you have
an 80% chance of answering the Rs. 50L question correctly and a 65% chance of
answering the Rs. 100L question correctly.

29. 29
Kaun Banega Crorepati
Crt 50%
Incrt 50%
w/o Life
Crt 65%
Incrt 35%
Crt 50%
Incrt 50%
Don’t Play
100L
3.2L
50L
3.2L
100L
3.2L
100L
3.2L
50L
3.2L
25L
Decision Point
Decision Point
Events
Action

30. 30
44.10
Kaun Banega Crorepati
41.92
44.10
51.60
66.12
Crt 50%
51.60
Incrt 50%
66.12
51.60
w/o Life
Crt 65%
Incrt 35%
Crt 50%
Incrt 50%
Don’t Play
100L
3.2L
50L
51.6
3.2L
100L
3.2L
100L
3.2L
50L
66.12
3.2L
25L
Decision Point
Events
Action

31. 31
References
Ernest Urbanovich, Ella E. Young, Martin L. Puterman, Sidney O. Fattedad, (2003) Early
Detection of High-Risk Claims at the Workers' Compensation Board of British Columbia.
Interfaces 33(4):15-26.
http://dx.doi.org/10.1287/inte.33.4.15.16372
Robert K. Perdue, William J. McAllister, Peter V. King, Bruce G. Berkey, (1999) Valuation of R
and D Projects Using Options Pricing and Decision Analysis Models. Interfaces 29(6):57-74.
http://dx.doi.org/10.1287/inte.29.6.57
Michael R. Walls, G. Thomas Morahan, James S. Dyer, (1995) Decision Analysis of Exploration
Opportunities in the Onshore US at Phillips Petroleum Company. Interfaces 25(6):39-56.
http://dx.doi.org/10.1287/inte.25.6.39

32. Annexure