basic business statistics, 10e © 2006 prentice-hall, inc. chap 17-1 chapter 17 decision making...
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![Page 1: Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-1 Chapter 17 Decision Making Basic Business Statistics 10 th Edition](https://reader036.vdocument.in/reader036/viewer/2022070402/56649f225503460f94c3a42f/html5/thumbnails/1.jpg)
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-1
Chapter 17
Decision Making
Basic Business Statistics10th Edition
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-2
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
To use Bayes’ theorem to revise probabilities in light of sample information
About the concept of utility
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-3
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 Action
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-4
List Possible Actions or Events
Payoff Table Decision Tree
Two Methods of
Listing
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-5
A Payoff Table
A payoff table shows alternatives, states of nature, and payoffs
Investment Choice
(Action)
Profit in $1,000’s
(Events)
Strong Economy
Stable Economy
Weak Economy
Large factory
Average factory
Small factory
200
90
40
50
120
30
-120
-30
20
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-6
Sample Decision Tree
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
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-7
Opportunity Loss
Investment Choice
(Action)
Profit in $1,000’s
(Events)
Strong Economy
Stable Economy
Weak Economy
Large factoryAverage factorySmall factory
2009040
50120 30
-120-30 20The 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
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-8
Opportunity Loss
Investment Choice
(Action)
Profit in $1,000’s
(States of Nature)
Strong Economy
Stable Economy
Weak Economy
Large factoryAverage factorySmall factory
2009040
50120 30
-120-30 20
(continued)
Investment Choice
(Action)
Opportunity Loss in $1,000’s
(Events)
Strong Economy
Stable Economy
Weak Economy
Large factoryAverage factorySmall factory
0110160
70 0
90
140500
Payoff Table
Opportunity Loss Table
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-9
Decision Criteria
Expected Monetary Value (EMV) The expected profit for taking action Aj
Expected Opportunity Loss (EOL) The expected opportunity loss for taking action Aj
Expected Value of Perfect Information (EVPI) The expected opportunity loss from the best decision
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-10
Expected Monetary Value Solution
The expected monetary value is the weighted average payoff, given specified probabilities for each event
N
1iiijPx)j(EMV
Where EMV(j) = expected monetary value of action j
xij = payoff for action j when event i occurs
Pi = probability of event i
Goal: Maximize expected value
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-11
The expected value is the weighted average payoff, given specified probabilities for each event
Investment Choice
(Action)
Profit in $1,000’s
(Events)
Strong Economy
(.3)
Stable Economy
(.5)
Weak Economy
(.2)
Large factoryAverage factorySmall factory
2009040
50120 30
-120-30 20
Suppose these probabilities have been assessed for these three events
(continued)
Expected Monetary Value Solution
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-12
Investment Choice
(Action)
Profit in $1,000’s
(Events)
Strong Economy
(.3)
Stable Economy
(.5)
Weak Economy
(.2)
Large factoryAverage factorySmall factory
2009040
50120 30
-120-30 20
Example: EMV (Average factory) = 90(.3) + 120(.5) + (-30)(.2)
= 81
Expected Values
(EMV)618131
Maximize expected value by choosing Average factory
(continued)
Payoff Table:
Goal: Maximize expected value
Expected Monetary Value Solution
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-13
Decision Tree Analysis
A Decision tree shows a decision problem, beginning with the initial decision and ending will all possible outcomes and payoffs.
Use a square to denote decision nodes
Use a circle to denote uncertain events
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-14
Add Probabilities and Payoffs
Large factory
Small factory
Decision
Average factory
Uncertain Events
Strong Economy
Stable Economy
Weak Economy
Strong Economy
Stable Economy
Weak Economy
Strong Economy
Stable Economy
Weak Economy
(continued)
PayoffsProbabilities
200
50
-120
40
30
20
90
120
-30
(.3)
(.5)
(.2)
(.3)
(.5)
(.2)
(.3)
(.5)
(.2)
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-15
Fold Back the Tree
Large factory
Small factory
Average factory
Strong Economy
Stable Economy
Weak Economy
Strong Economy
Stable Economy
Weak Economy
Strong Economy
Stable Economy
Weak Economy
200
50
-120
40
30
20
90
120
-30
(.3)
(.5)
(.2)
(.3)
(.5)
(.2)
(.3)
(.5)
(.2)
EMV=200(.3)+50(.5)+(-120)(.2)=61
EMV=90(.3)+120(.5)+(-30)(.2)=81
EMV=40(.3)+30(.5)+20(.2)=31
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-16
Make the Decision
Large factory
Small factory
Average factory
Strong Economy
Stable Economy
Weak Economy
Strong Economy
Stable Economy
Weak Economy
Strong Economy
Stable Economy
Weak Economy
200
50
-120
40
30
20
90
120
-30
(.3)
(.5)
(.2)
(.3)
(.5)
(.2)
(.3)
(.5)
(.2)
EV=61
EV=81
EV=31
Maximum
EMV=81
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-17
Expected Opportunity Loss Solution
The expected opportunity loss is the weighted average loss, given specified probabilities for each event
N
1iiijPL)j(EOL
Where EOL(j) = expected monetary value of action j
Lij = opp. loss for action j when event i occurs
Pi = probability of event i
Goal: Minimize expected opportunity loss
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-18
Expected Opportunity Loss Solution
Investment Choice
(Action)
Opportunity Loss in $1,000’s
(Events)
Strong Economy
(.3)
Stable Economy
(.5)
Weak Economy
(.2)
Large factoryAverage factorySmall factory
0110160
70 0
90
140500
Example: EOL (Large factory) = 0(.3) + 70(.5) + (140)(.2)
= 63
Expected Op. Loss
(EOL)634393
Minimize expected
op. loss by choosing Average factory
Opportunity Loss Table
Goal: Minimize expected opportunity loss
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-19
Expected Opportunity Loss vs. Expected Monetary Value
The Expected Monetary Value (EMV) and the Expected Opportunity Loss (EOL) criteria are equivalent.
Note that in this example the expected monetary value solution and the expected opportunity loss solution both led to the choice of the average size factory.
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-20
Value of Information
Expected Value of Perfect Information, EVPI
Expected Value of Perfect Information
EVPI = Expected profit under certainty
– expected monetary value of the best alternative
(EVPI is equal to the expected opportunity loss from the best decision)
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-21
Expected Profit Under Certainty
Expected profit under certainty
= expected value of the best decision, given perfect information
Investment Choice
(Action)
Profit in $1,000’s
(Events)
Strong Economy
(.3)
Stable Economy
(.5)
Weak Economy
(.2)
Large factoryAverage factorySmall factory
2009040
50120 30
-120-30 20
Example: Best decision given “Strong Economy” is “Large factory”
200 120 20Value of best decision for each event:
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-22
Expected Profit Under Certainty
Investment Choice
(Action)
Profit in $1,000’s
(Events)
Strong Economy
(.3)
Stable Economy
(.5)
Weak Economy
(.2)
Large factoryAverage factorySmall factory
2009040
50120 30
-120-30 20
200 120 20
(continued)
Now weight these outcomes with their probabilities to find the expected value: 200(.3)+120(.5)+20(.2)
= 124Expected profit under certainty
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-23
Value of Information Solution
Expected Value of Perfect Information (EVPI)EVPI = Expected profit under certainty
– Expected monetary value of the best decision
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)
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-24
Accounting for Variability
Stock Choice
(Action)
Percent Return
(Events)
Strong Economy
(.7)
Weak Economy
(.3)
Stock A 30 -10
Stock B 14 8
Consider the choice of Stock A vs. Stock B
Expected Return:
18.0
12.2
Stock A has a higher EMV, but what about risk?
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-25
Stock Choice
(Action)
Percent Return
(Events)
Strong Economy
(.7)
Weak Economy
(.3)
Stock A 30 -10
Stock B 14 8
Variance:
336.0
7.56
Calculate the variance and standard deviation for Stock A and Stock B:
Expected Return:
18.0
12.2
0.336)3(.)1810()7(.)1830()X(Pμ)X(σ 22N
1ii
2i
2A
Example:
Standard Deviation:
18.33
2.75
Accounting for Variability(continued)
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-26
Calculate the coefficient of variation for each stock:
%83.101100%0.18
33.18 100%
EMV
σCV
A
AA
(continued)
%54.22100%2.12
75.2 100%
EMV
σCV
B
BB
Stock A has much more relative variability
Accounting for Variability
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-27
Return-to-Risk Ratio
Return-to-Risk Ratio (RTRR):
jσ
EMV(j)RTRR(j)
Expresses the relationship between the return (expected payoff) and the risk (standard deviation)
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-28
Return-to-Risk Ratio
jσ
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
982.033.18
0.18
σ
EMV(A)RTRR(A)
A
436.475.2
2.12
σ
EMV(B)RTRR(B)
B
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-29
Decision Making with Sample Information
Permits revising old probabilities based on new information
NewInformation
RevisedProbability
PriorProbability
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-30
Revised Probabilities Example
Additional Information: Economic forecast is strong economy When the economy was strong, the forecaster was correct 90% of the time. When the economy was weak, the forecaster was correct 70% of the time.
Prior probabilities from stock choice example
F1 = strong forecast
F2 = weak forecast
E1 = strong economy = 0.70
E2 = weak economy = 0.30
P(F1 | E1) = 0.90 P(F1 | E2) = 0.30
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-31
Revised Probabilities Example
Revised Probabilities (Bayes’ Theorem)
3.)E|F(P , 9.)E|F(P 2111
3.)E(P , 7.)E(P 21
875.)3)(.3(.)9)(.7(.
)9)(.7(.
)F(P
)E|F(P)E(P)F|E(P
1
11111
125.)F(P
)E|F(P)E(P)F|E(P
1
21212
(continued)
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-32
EMV with Revised Probabilities
EMV Stock A = 25.0
EMV Stock B = 11.25
Revised probabilities
Pi Event Stock A xijPi Stock B xijPi
.875 strong 30 26.25 14 12.25
.125 weak -10 -1.25 8 1.00
Σ = 25.0 Σ = 11.25
Maximum EMV
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-33
EOL Table with Revised Probabilities
EOL Stock A = 2.25
EOL Stock B = 14.00
Revised probabilities
Pi Event Stock A xijPi Stock B xijPi
.875 strong 0 0 16 14.00
.125 weak 18 2.25 0 0
Σ = 2.25 Σ = 14.00
Minimum EOL
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-34
Stock Choice
(Action)
Percent Return
(Events)
Strong Economy
(.875)
Weak Economy
(.125)
Stock A 30 -10
Stock B 14 8
Variance:
175.0
3.94
Calculate the variance and standard deviation for Stock A and Stock B:
Expected Return:
25.0
13.25
0.175)125(.)2510()875(.)2530()X(Pμ)Xi(σ 22N
1ii
22A
Example:
Standard Deviation:
13.229
1.984
Accounting for Variability with Revised Probabilities
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-35
The coefficient of variation for each stock using the results from the revised probabilities:
%92.52100%0.25
229.13 100%
EMV
σCV
A
AA
(continued)
%97.14100%25.13
984.1 100%
EMV
σCV
B
BB
Accounting for Variability with Revised Probabilities
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-36
Return-to-Risk Ratio with Revised Probabilities
With the revised probabilities, both stocks have higher expected returns, lower CV’s, and larger return to risk ratios
890.1229.13
0.25
σ
EMV(A)RTRR(A)
A
011.7984.1
25.13
σ
EMV(B)RTRR(B)
B
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-37
Utility
Utility is the pleasure or satisfaction obtained from an action.
The utility of an outcome may not be the same for each individual.
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-38
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.
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-39
Three Types of Utility CurvesU
t ili
ty
$ $ $
Uti
lity
Ut i
lity
Risk Averter Risk Seeker Risk-Neutral
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-40
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
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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-41
Chapter Summary
Described the payoff table and decision trees Opportunity loss
Provided criteria for decision making Expected monetary value Expected opportunity loss Return to risk ratio
Introduced expected profit under certainty and the value of perfect information
Discussed decision making with sample information
Addressed the concept of utility