DECISION ANALYSIS
Slide 2
Decision Analysis

Structuring the Decision Problem / Developing a
Decision Strategy

Decision Making Without Probabilities

Decision Making with Probabilities

Expected Value of Perfect Information

Decision Analysis with Sample Information

Expected Value of Sample Information
Slide 3
Structuring the Decision
Problem

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A decision problem is characterized by decision
alternatives, states of nature, and resulting payoffs.
The decision alternatives are the different possible
strategies the decision maker can employ.
The states of nature refer to future events, not
under the control of the decision maker, which may
occur. States of nature should be defined so that they
are mutually exclusive and collectively exhaustive.
For each decision alternative and state of nature,
there is a resulting payoff. These are often
represented in matrix form called a payoff table.
Slide 4
Decision Trees
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A decision tree is a chronological representation of the
decision problem.
Each decision tree has two types of nodes: round
nodes correspond to the states of nature - square
nodes correspond to the decision alternatives.
The branches leaving round nodes represent the
different states of nature while the branches leaving
square nodes represent different decision alternatives.
At the end of each limb of a tree are the payoffs
attained from the series of branches making up that limb.
Slide 5
Decision Making Under
Uncertainty


If the decision maker does not know with certainty
which state of nature will occur, then he is said to be
doing decision making under uncertainty.
Three commonly used criteria for decision making
under uncertainty when probability information
regarding the likelihood of the states of nature is
unavailable are:
• the optimistic approach
• the conservative approach
• the minimax regret approach.
Slide 6
The Optimistic Approach

The optimistic approach would be used by an
optimistic decision maker.

The decision with the largest possible payoff is
chosen.

If the payoff table was in terms of costs, the
decision with the lowest possible cost is chosen.
Slide 7
The Conservative Approach
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
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The conservative approach would be used by a
conservative decision maker.
For each decision the minimum payoff is listed
and then the decision corresponding to the
maximum of these minimum payoffs is selected.
(Hence, the minimum possible payoff is
maximized.)
If the payoff was in terms of costs, the maximum
costs would be determined for each decision and
then the decision corresponding to the minimum
of these maximum costs is selected. (Hence, the
maximum possible cost is minimized.)
Slide 8
The Minimax Regret Approach
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The minimax regret approach requires the
construction of a regret table or an opportunity
loss table.
This is done by calculating for each state of
nature the difference between each payoff and
the largest payoff for that state of nature.
Then, using this regret table, the maximum
regret for each possible decision is listed.
The decision chosen is the one corresponding
to the minimum of the maximum regrets.
Slide 9
Expected Value Approach
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If probabilistic information regarding the
states of nature is available, one may use the
expected value (EV) approach.
Here the expected return for each decision is
calculated by summing the products of the
payoff under each state of nature and the
probability of the respective state of nature
occurring.
The decision yielding the best expected return
is chosen.
Slide 10
Expected Value of Perfect
Information - EVPI
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Frequently information is available which can
improve the probability estimates for the
states of nature.
The expected value of perfect information
(EVPI) is the increase in the expected profit
that would result if one knew with certainty
which state of nature would occur.
The EVPI provides an upper bound on the
expected value of any sample or survey
information.
Slide 11
Expected Value of Perfect
Information - EVPI

EVPI Calculation
• Step 1:
Determine the optimal return
corresponding to each state of nature.
• Step 2:
Compute the expected value of these
optimal returns.
• Step 3:
Subtract the EV of the optimal decision
from the amount determined in Step 2.
Slide 12
Bayes’ Theorem and Posterior
Probabilities
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Knowledge of sample or survey information can be
used to revise the probability estimates for the states of
nature.
Prior to obtaining this information, the probability
estimates for the states of nature are called prior
probabilities.
With knowledge of conditional probabilities for the
outcomes or indicators of the sample or survey
information, these prior probabilities can be revised by
employing Bayes' Theorem.
The outcomes of this analysis are called posterior
probabilities.
Slide 13
Posterior Probabilities

Posterior Probabilities Calculation
• Step 1:
For each state of nature, multiply the prior
probability by its conditional probability for the
indicator -- this gives the joint probabilities for the
states and indicator.
• Step 2:
Sum these joint probabilities over all states -- this
gives the marginal probability for the indicator.
• Step 3:
For each state, divide its joint probability by the
marginal probability for the indicator -- this gives
the posterior probability distribution.
Slide 14
Expected Value of Sample
Information - EVSI
 The
expected value of sample
information (EVSI) is the additional
expected profit possible through
knowledge of the sample or survey
information.
Slide 15
Expected Value of Sample
Information

EVSI Calculation
• Step 1:
Determine the optimal decision and its expected
return for the possible outcomes of the sample or survey
using the posterior probabilities for the states of nature.
• Step 2:
Compute the expected value of these optimal returns.
• Step 3:
Subtract the EV of the optimal decision obtained
without using the sample information from the amount
determined in Step 2.
Slide 16
Efficiency of Sample Information
Efficiency of sample information is the
ratio of EVSI to EVPI.
 As the EVPI provides an upper bound for
the EVSI, efficiency is always a number
between 0 and 1.

Slide 17
PROBLEMS / EXAMPLES
Slide 18
Example
Consider the following problem with three decision
alternatives and three states of nature with the
following payoff table representing profits:
States of Nature
s1
s2
s3
d1
Decisions d2
d3
4
0
1
4
3
5
-2
-1
-3
Slide 19
Example

Optimistic Approach
An optimistic decision maker would use the
optimistic approach. All we really need to do is to
choose the decision that has the largest single value in
the payoff table. This largest value is 5, and hence the
optimal decision is d3.
Maximum
Decision
Payoff
d1
4
d2
3
choose d3
d3
5
maximum
Slide 20
Example

1
2
3
4
5
6
7
8
9
Spreadsheet for Optimistic Approach
A
B
PAYOFF TABLE
Decision
Alternative
d1
d2
d3
C
State
s1
4
0
1
D
of Nature
s2
s3
4
-2
3
-1
5
-3
Best Payoff
E
F
Maximum Recommended
Payoff
Decision
4
3
5
d3
5
Slide 22
Example

Conservative Approach
A conservative decision maker would use the
conservative approach. List the minimum payoff for
each decision. Choose the decision with the maximum
of these minimum payoffs.
Minimum
Decision
Payoff
d1
-2
choose d2
d2
-1
maximum
d3
-3
Slide 23
Example

Spreadsheet for Conservative Approach
A
B
C
D
1 PAYOFF TABLE
2
3 Decision
State of Nature
4 Alternative
s1
s2
s3
5
d1
4
4
-2
6
d2
0
3
-1
7
d3
1
5
-3
8
9
Best Payoff
E
F
Minimum Recommended
Payoff
Decision
-2
-1
d2
-3
-1
Slide 25
Example

Minimax Regret Approach
For the minimax regret approach, first compute a
regret table by subtracting each payoff in a column
from the largest payoff in that column. In this
example, in the first column subtract 4, 0, and 1 from
4; in the second column, subtract 4, 3, and 5 from 5;
etc. The resulting regret table is:
s1 s2 s3
d1
d2
d3
0
4
3
1
2
0
1
0
2
Slide 26
Example

Minimax Regret Approach (continued)
For each decision list the maximum regret. Choose
the decision with the minimum of these values.
choose d1
Decision
d1
d2
d3
Maximum Regret
1
minimum
4
3
Slide 27
Example

Spreadsheet for Minimax Regret Approach
1
2
3
4
5
6
7
8
9
10
11
12
13
14
PAYOFF TABLE
Decision
State
Alternative
s1
d1
4
d2
0
d3
1
of Nature
s2
s3
4
-2
3
-1
5
-3
OPPORTUNITY LOSS TABLE
Decision
State of Nature
Maximum Recommended
Alternative
s1
s2
s3
Regret
Decision
d1
0
1
1
1
d1
d2
4
2
0
4
d3
3
0
2
3
Minimax Regret Value
1
Slide 29
Example

Decision Tree
Using a decision tree to find the optimal decision if
Payoffs
P(s1) = .20, P(s2) = .50, P(s3) = .30:
s1
d1
1
d2
2
s2
s3
s1
3
s2
s3
d3
s1
4
s3
s2
4
4
-2
0
3
-1
1
5
-3
Slide 30
Example

Expected Value
To calculate the expected values at nodes 2, 3, and 4,
multiply the payoffs by the corresponding probabilities
and then sum. The decision with the maximum
expected value of 2.2, d1, is then chosen.
EV(Node 2) = .20(4) + .50(4) + .30(-2) = 2.2
EV(Node 3) = .20(0) + .50(3) + .30(-1) = 1.2
EV(Node 4) = .20(1) + .50(5) + .30(-3) = 1.8
Slide 31
Example

Expected Value of Perfect Information
The EVPI is calculated by multiplying the
maximum payoff for each state by the corresponding
probability, summing these values, and then
subtracting the expected value of the optimal decision
from this sum.
Thus, the EVPI = [.20(4) + .50(5) + .30(-1)] - 2.2 = .8
Slide 32
Example: Burger Prince
Burger Prince Restaurant is contemplating
opening a new restaurant on Main Street. It has three
different models, each with a different seating
capacity. Burger Prince estimates that the average
number of customers per hour will be 80, 100, or 120.
The payoff table for the three models is as follows:
Average Number of Customers Per Hour
s1 = 80 s2 = 100 s3 = 120
Model A
Model B
Model C
$10,000
$ 8,000
$ 6,000
$15,000
$18,000
$16,000
$14,000
$12,000
$21,000
Slide 33
Example: Burger Prince

Payoffs
Decision Tree
2
d1
d3
.4
.2
10,000
15,000
.4
14,000
d2
1
s1
s2
s3
3
s1
s2
.4
s3
.4
.2
8,000
18,000
12,000
4
s1
s2
s3
.4
6,000
.2
16,000
.4
21,000
Slide 35
Example: Burger Prince

Expected Value For Each Decision
d1
EMV = .4(10,000) + .2(15,000) + .4(14,000)
= $12,600
2
Model A
1
Model B
Model C
d2
d3
3
EMV = .4(8,000) + .2(18,000) + .4(12,000)
= $11,600
EMV = .4(6,000) + .2(16,000) + .4(21,000)
= $14,000
4
Choose the model with largest EV -- Model C.
Slide 36
Example: Burger Prince

Spreadsheet for Expected Value Approach
A
B
C
D
E
F
1 PAYOFF TABLE
2
3 Decision
State of Nature
Expected Recommended
4 Alternative s1 = 80 s2 = 100 s3 = 120 Value
Decision
5
Model A
10,000 15,000 14,000
12600
6
Model B
8,000
18,000 12,000
11600
7
Model C
6,000
16,000 21,000
14000
Model C
8 Probability
0.4
0.2
0.4
9
Maximum Expected Value
14000
Slide 38
Example: Burger Prince

Expected Value of Perfect Information
Calculate the expected value for the optimum
payoff for each state of nature and subtract the EV of
the optimal decision.
EVPI= .4(10,000) + .2(18,000) + .4(21,000) - 14,000 = $2,000
Slide 39
Example: Burger Prince

Spreadsheet for Expected Value of Perfect Information
A
B
C
D
E
1 PAYOFF TABLE
2
3
Decision
State of Nature
Expected
4 Alternative s1 = 80 s2 = 100 s3 = 120 Value
5 d1 = Model A 10,000
15,000
14,000
12600
6 d2 = Model B 8,000
18,000
12,000
11600
7 d3 = Model C 6,000
16,000
21,000
14000
8 Probability
0.4
0.2
0.4
9
Maximum Expected Value
14000
10
11
Maximum Payoff
EVwPI
12
10,000
18,000
21,000
16000
F
Recommended
Decision
d3 = Model C
EVPI
2000
Slide 40
Example: Burger Prince

Sample Information
Burger Prince must decide whether or not to
purchase a marketing survey from Stanton Marketing
for $1,000. The results of the survey are "favorable" or
"unfavorable". The conditional probabilities are:
P(favorable ¦ 80 customers per hour) = .2
P(favorable ¦ 100 customers per hour) = .5
P(favorable ¦ 120 customers per hour) = .9
Should Burger Prince have the survey performed
by Stanton Marketing?
Slide 41
Example: Burger Prince

Posterior Probabilities
Favorable
State
80
100
120
Prior
.4
.2
.4
Conditional Joint
.2
.08
.5
.10
.9
.36
Total .54
Posterior
.148
.185
.667
1.000
P(favorable) = .54
Slide 42
Example: Burger Prince

Posterior Probabilities
Unfavorable
State
80
100
120
Prior
.4
.2
.4
Conditional
.8
.5
.1
Total
Joint
.32
.10
.04
.46
Posterior
.696
.217
.087
1.000
P(unfavorable) = .46
Slide 43
Example: Burger Prince

Spreadsheet for Posterior Probabilities
A
B
Market Research Favorable
Prior
State of Nature Probabilities
s1 = 80
0.4
s2 = 100
0.2
s3 = 120
0.4
C
D
1
2
Conditional
Joint
3
Probabilities Probabilities
4
0.2
0.08
5
0.5
0.10
6
0.9
0.36
7
P(Favorable) =
0.54
8
9 Market Research Unfavorable
10
Prior
Conditional
Joint
11 State of Nature Probabilities Probabilities Probabilities
12
s1 = 80
0.4
0.8
0.32
13
s2 = 100
0.2
0.5
0.10
14
s3 = 120
0.4
0.1
0.04
15
P(Favorable) =
0.46
E
Posterior
Probabilities
0.148
0.185
0.667
Posterior
Probabilities
0.696
0.217
0.087
Slide 45
Example: Burger Prince

Decision Tree (top half)
$10,000
4
s2 (.185)
d1
$15,000
$14,000
$8,000
d2
2
I1
(.54)
5
$18,000
$12,000
d3
$6,000
6
1
s2 (.185)
s2 (.185)
$16,000
$21,000
Slide 46
Example: Burger Prince

Decision Tree (bottom half)
1
$10,000
I2
(.46)
d1
7
s2 (.217)
$15,000
$14,000
d2
3
8
s2 (.217)
$8,000
$18,000
$12,000
d3
$6,000
9
s2 (.217)
$16,000
$21,000
Slide 47
Example: Burger Prince
d1
$17,855
d2
2
4
EMV = .148(10,000) + .185(15,000)
+ .667(14,000) = $13,593
5
EMV = .148 (8,000) + .185(18,000)
+ .667(12,000) = $12,518
6
EMV = .148(6,000) + .185(16,000)
+.667(21,000) = $17,855
7
EMV = .696(10,000) + .217(15,000)
+.087(14,000)= $11,433
8
EMV = .696(8,000) + .217(18,000)
+ .087(12,000) = $10,554
9
EMV = .696(6,000) + .217(16,000)
+.087(21,000) = $9,475
d3
I1
(.54)
1
d1
I2
(.46)
d2
3
$11,433
d3
Slide 48
Example: Burger Prince

Expected Value of Sample Information
If the outcome of the survey is "favorable" choose
Model C. If it is unfavorable, choose Model A.
EVSI = .54($17,855) + .46($11,433) - $14,000 = $900.88
Since this is less than the cost of the survey, the
survey should not be purchased.
Slide 49
Example: Burger Prince

Efficiency of Sample Information
The efficiency of the survey:
EVSI/EVPI = ($900.88)/($2000) = .4504
Slide 50
The End
Slide 51