Decision Making for business leader in company

Decission making text book for business leader

2. Decision Making
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc. Chap 17-1

3. Learning Objectives
In this chapter, you learn:
•To use payoff tables and decision
trees to evaluate alternative courses
of action
•To use several criteria to select an
alternative course of action
•About the concept of utility
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-2

4. Steps in Decision Making
•List Alternative Courses of Action
–Choices or actions
•List Uncertain Events
–Possible events or outcomes
•Determine ‘Payoffs’
–Associate a Payoff with Each Event/Outcome
combination
•Adopt Decision Criteria
–Evaluate Criteria for Selecting the Best Course
of ActionStatistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-3

5. Payoff Table
Apayofftableshowsalternatives,statesof
nature,andpayoffs
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-4
Profit in $1,000’s
(Events)
Investment Choice
(Action)
Large
Factory
Average
Factory
Small
Factory
Strong Economy
Stable Economy
Weak Economy
200
50
-120
90
120
-30
40
30
20

6. Decision Tree
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-5
Large factory
Small factory
Average factory
Strong Economy
Stable Economy
Weak Economy
Strong Economy
Stable Economy
Weak Economy
Strong Economy
Stable Economy
Weak Economy
Payoffs
200
50
-120
40
30
20
90
120
-30

7. Opportunity Loss
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-6
Profit in $1,000’s
(Events)
Investment Choice
(Action)
Large
Factory
Average
Factory
Small
Factory
Strong Economy
Stable Economy
Weak Economy
200
50
-120
90
120
-30
40
30
20
The action “Average factory” has payoff 90 for “Strong Economy”. Given “Strong
Economy”, the choice of “Large factory” would have given a payoff of 200, or
110 higher. Opportunity loss = 110 for this cell.
Opportunity loss is the difference between an actual payoff
for an action and the optimal payoff, given a particular event
Payoff
Table

8. Opportunity Loss
Profit in $1,000’s
(Events)
Investment Choice (Action)
Large
Factory
Average
Factory
Small
Factory
Strong Economy
Stable Economy
Weak Economy
200
50
-120
90
120
-30
40
30
20
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-7
Opportunity Loss in
$1,000’s
(Events)
Investment Choice (Action)
Large
Factory
Average
Factory
Small
Factory
Strong Economy
Stable Economy
Weak Economy
0
70
140
110
0
50
160
90
0
Payoff
Table
Opportunity
Loss Table

9. Decision Criteria
•Expected Monetary Value (EMV)
–The expected profit for taking action A
j
•Expected Opportunity Loss (EOL)
–The expected opportunity loss for taking action A
j
•Expected Value of Perfect Information (EVPI)
–The expected opportunity loss from the best
decision
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-8

10. Expected Monetary Value
•The expected monetary value is the weighted average payoff,
given specified probabilities for each event
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-9


N
i
iij
PXjEMV
1
)(
Where EMV(j) = expected monetary value of action j
X
ij= payoff for action j when event i occurs
P
i= probability of event i
Goal: Maximize expected value

11. Expected Monetary Value
•The expected value is the weighted average
payoff, given specified probabilities for each
event
Profit in $1,000’s
(Events)
Investment Choice
(Action)
Large
Factory
Average
Factory
Small Factory
Strong Economy (0.3)
Stable Economy (0.5)
Weak Economy (0.2)
200
50
-120
90
120
-30
40
30
20
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-10
Suppose these
probabilities
have been
assessed for
these three
events

12. Expected Monetary Value
•Example: EMV (Average factory) = 90(.3) +
120(.5) + (-30)(.2) = 81
Profit in $1,000’s
(Events)
Investment Choice
(Action)
Large
Factory
Average
Factory
Small
Factory
Strong Economy (0.3)
Stable Economy (0.5)
Weak Economy (0.2)
200
50
-120
90
120
-30
40
30
20
EMV (Expected Values) 61 81 31
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-11
Payoff Table:

13. Expected Opportunity Loss
•The expected opportunity loss is the weighted average loss,
given specified probabilities for each event
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc. Chap 17-12


N
1i
iij
PLEOL(j)
Where EOL(j) = expected opportunity loss of action j
L
ij= opportunity loss for action j when event ioccurs
P
i= probability of event i
Goal: Minimize expected opportunity loss

14. Expected Opportunity Loss
•Example: EOL (Large factory) = 0(.3) + 70(.5)
+ (140)(.2) = 63
Opportunity Loss in
$1,000’s
(Events)
Investment Choice (Action)
Large
Factory
Average
Factory
Small
Factory
Strong Economy (0.3)
Stable Economy (0.5)
Weak Economy (0.2)
0
70
140
110
0
50
160
90
0
Expected Opportunity
Loss (EOL)
63 43 93
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-13
Opportunity Loss Table

15. Expected Profit Under Certainty
•Expected
profit under
certainty
= expected
value of the
best
decision,
given perfect
information
Profit in $1,000’s
(Events)
Investment Choice
(Action)
Large
Factory
Average
Factory
Small Factory
Strong Economy (0.3)
Stable Economy (0.5)
Weak Economy (0.2)
200
50
-120
90
120
-30
40
30
20
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-14
Example: Best decision given “Strong
Economy” is “Large factory”
200 120 20
Value of best decision
for each event:

16. Expected Profit Under Certainty
•Now weight
these
outcomes
with their
probabilities
to find the
expected
profit under
certainty:
Profit in $1,000’s
(Events)
Investment Choice
(Action)
Large
Factory
Average
Factory
Small Factory
Strong Economy (0.3)
Stable Economy (0.5)
Weak Economy (0.2)
200
50
-120
90
120
-30
40
30
20
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-15
200 120 20
200(.3)+120(.5)+20(.2)
= 124

17. Value of Information Solution
•Expected Value of Perfect Information (EVPI)
EVPI = Expected profit under certainty
–Expected monetary value of the best decision
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-16
so: EVPI = 124 –81
= 43
Recall: Expected profit under certainty = 124
EMV is maximized by choosing “Average factory,”
where EMV = 81
(EVPI is the maximum you would be willing to spend to obtain perfect information)

18. Accounting for Variability
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-17
Percent Return
(Events)
Stock Choice
(Action)
Stock A Stock B
Strong Economy
(.7)
30 14
Weak Economy
(.3)
-10 8
Expected Return
(EMV)
18.0 12.2
Consider the choice of Stock A vs. Stock B
Stock A has a higher
EMV, but what about
risk?

19. Accounting for Variability
Percent Return
(Events)
Stock Choice
(Action)
Stock A Stock B
Strong Economy (.7) 30 14
Weak Economy (.3) -10 8
Expected Return (EMV) 18.0 12.2
Variance 336.0 7.56
Standard Deviation 18.33 2.75
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-18
Calculate the variance and standard deviation0.336)3(.)1810()7(.)1830()X(Pμ)X(σ
22
N
1i
i
2
i
2
A


Example:

20. Accounting for Variability
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-19
Calculate the coefficient of variation for each stock:%83.101100%
0.18
33.18
100%
EMV
σ
CV
A
A
A
 %54.22100%
2.12
75.2
100%
EMV
σ
CV
B
B
B

Stock A has
much more
relative
variability

21. Return-to-Risk Ratio
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-20j
σ
EMV(j)
RTRR(j)
You might want to consider Stock B if you don’t like risk.
Although Stock A has a higher Expected Return, Stock B has a
much larger return to risk ratio and a much smaller CV.982.0
33.18
0.18
σ
EMV(A)
RTRR(A)
A
 436.4
75.2
2.12
σ
EMV(B)
RTRR(B)
B


22. Utility
•Utility is the pleasure or satisfaction
obtained from an action.
•The utility of an outcome may not be the
same for each individual.
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc. Chap 17-21

23. Utility Example
Each incremental $1 of profit does not have the
same value to every individual:
•A risk averse person, once reaching a goal,
assigns less utility to each incremental $1.
•A risk seeker assigns more utility to each
incremental $1.
•A risk neutral person assigns the same utility to
each extra $1.
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-22

25. Maximizing Expected Utility
•Making decisions in terms of utility, not $
–Translate $ outcomes into utility outcomes
–Calculate expected utilities for each action
–Choose the action to maximize expected
utility
Statistics for Managers Using Microsoft
Excel, 5e © 2008 Prentice-Hall, Inc.
Chap 17-4