Decision theory is a framework for making operational decisions under various future conditions, incorporating alternatives and associated payoffs. It categorizes decision environments into certainty, risk, and uncertainty, and employs models like influence diagrams, payoff tables, and decision trees to represent decision problems. The document explains decision-making approaches such as optimistic, conservative, and minimax regret, as well as the use of expected values when probabilities are available.

1. Decision Theory

2. Decision Theory represents a general
approach to decision making which is suitable for a
wide range of operations management decisions,
including:
product and
service design
product and
service design
equipment
selection
location
planning
Decision TheoryDecision Theory
Capacity
planning

3. A set of possible future conditions exists that will have a bearing
on the results of the decision
A list of alternatives for the manager to choose from
A known payoff for each alternative under each possible future
condition
Decision Theory ElementsDecision Theory Elements

4. Identify possible future conditions called states of nature
Develop a list of possible alternatives, one of which may be to do
nothing
Determine the payoff associated with each alternative for every
future condition
Decision Theory ProcessDecision Theory Process

5. If possible, determine the likelihood of each
possible future condition
Evaluate alternatives according to some decision
criterion and select the best alternative
Decision Theory Process (Cont’d)Decision Theory Process (Cont’d)

6. Bounded Rationality
The limitations on decision
making caused by costs,
human abilities, time, technology, and availability of
information
Causes of Poor DecisionsCauses of Poor Decisions

7. Suboptimization
The result of different
departments each attempting
to reach a solution that is
optimum for that department
Causes of Poor Decisions (Cont’d)Causes of Poor Decisions (Cont’d)

8. Certainty - Environment in which relevant
parameters have known values
Risk - Environment in which certain future events
have probable outcomes
Uncertainty - Environment in which it is impossible to
assess the likelihood of various future events
Decision EnvironmentsDecision Environments

9. Problem Formulation
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 will ultimately affect decision
results. States of nature should be defined so that they are
mutually exclusive and contain all possible future events that
could affect the results of all potential decisions.

10. Decision Theory Models
Decision theory problems are generally represented as one of the
following:
– Influence Diagram
– Payoff Table
– Decision Tree

11. Influence Diagrams
An influence diagram is a graphical device showing the relationships
among the decisions, the chance events, and the consequences.
Squares or rectangles depict decision nodes.
Circles or ovals depict chance nodes.
Diamonds depict consequence nodes.
Lines or arcs connecting the nodes show the direction of influence.

12. Payoff Tables
The consequence resulting from a specific combination of a
decision alternative and a state of nature is a payoff.
A table showing payoffs for all combinations of decision alternatives
and states of nature is a payoff table.
Payoffs can be expressed in terms of profit, cost, time, distance or
any other appropriate measure.

13. Decision Trees
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 while square nodes
correspond to the decision alternatives.
The branches leaving each round node represent the different
states of nature while the branches leaving each square node
represent the 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.

14. Example: CAL Condominium Complex
A developer must decide how large a luxury condominium
complex to build – small, medium, or large. The
profitability of this complex depends upon the future level
of demand for the complex’s condominiums.

15. CAL Condos: Elements of Decision Theory
States of nature: The states of nature could be defined as
low demand and high demand.
Alternatives: CAL could decide to build a small, medium,
or large condominium complex.
Payoffs: The profit for each alternative under each potential
state of nature is going to be determined.
We develop different models for this problem on the following slides.

16. CAL Condos: Payoff Table
Alternatives Low High
Small 8 8
Medium 5 15
Large -11 22
States of Nature
(payoffs in millions of dollars)
THIS IS A PROFIT PAYOFF TABLE

17. Decision Making without Probabilities
Three commonly used criteria for decision making when
probability information regarding the likelihood of the states of
nature is unavailable are:
– the optimistic approach
– the conservative approach
– the minimax regret approach.

18. Optimistic Approach
The optimistic approach would be used by an optimistic decision
maker.
The decision with the best possible payoff is chosen.
If the payoff table was in terms of costs, the decision with the
lowest cost would be chosen.
If the payoff table was in terms of profits, the decision with the
highest profit would be chosen.

19. Conservative Approach
The conservative approach would be used by a conservative decision maker.
For each decision the worst payoff is listed and then the decision
corresponding to the best of these worst payoffs is selected. (Hence, the
worst 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.)
If the payoff was in terms of profits, the minimum profits would be determined
for each decision and then the decision corresponding to the maximum of
these minimum profits is selected. (Hence, the minimum possible profit is
maximized.)

20. Minimax Regret Approach
1. 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 best payoff for that state of nature.
2. Then, using this regret table, the maximum regret for each
possible decision is listed.
3. The decision chosen is the one corresponding to the minimum of
the maximum regrets.

21. Solving CAL Condominiums Problem
Suppose that information regarding the probability (or likelihood) that there will be
a high or low demand is unavailable.
– A conservative or pessimistic decision maker would select the
decision alternative determined by the conservative approach.
– An optimistic decision maker would select the decision
alternative rendered by the optimistic approach.
– The minimax regret approach is generally selected by a
decision maker who reflects on decisions “after the fact”, and
complains about or “regrets” their decisions based upon the
profits that they could have made (or cheaper costs that they
could have spent) had a different decision been selected.

22. CAL Condos: Optimistic Decision
If the optimistic approach is selected:
STATES OF NATURE BEST
Alternatives Low High PROFIT
Small 8 8 8
Medium 5 15 15
Large -11 22 22
MaximaxMaximax
payoffpayoff
MaximaMaxima
xx
decisiondecision

23. CAL Condos: Conservative Decision
If the conservative approach is selected:
STATES OF NATURE WORST
Alternatives Low High PROFIT
Small 8 8 8
Medium 5 15 5
Large -11 22 -11
MaximinMaximin
payoffpayoff
MaximiMaximi
nn
decisiondecision
The decision with the profit from the column of worst profits is selected.

24. CAL Condos: Minimax Regret Decision
If the minimax regret approach is selected:
Step 1: Determine the best payoff for each state of nature and create a regret
table.
STATES OF NATURE
Alternatives Low High
Small 8 8
Medium 5 15
Large -11 22
Best Profit
for Low
8
Best Profit
for High
22

25. CAL Condos: Minimax Regret Decision
If the minimax regret approach is selected:
Step 1: Create a regret table (continued).
STATES OF NATURE
Alternatives Low High
Small 0 14
Medium 3 7
Large 19 0
For a profit payoff
table, entries in
the regret table
represent profits
that could have
been earned.
If they knew in advanced that the demand would be low, they would have built a
small complex. Without this “psychic insight”, if they decided to build a medium
facility and the demand turned out to be low, they would regret building a medium
complex because they only made 5 million dollars instead of 8 million had they built
a small facility instead. They regret their decision by 3 million dollars.

26. CAL Condos: Minimax Regret Decision
If the minimax regret approach is selected:
Step 2: Create a regret table (continued).
Step 3: Determine the maximum regret for each decision.
STATES OF NATURE Max
Alternatives Low High Regret
Small 0 14 14
Medium 3 7 7
Large 19 0 19
Regret not getting a profit
of 19 more than not making
a profit of 0.

27. CAL Condos: Minimax Regret Decision
If the minimax regret approach is selected:
Step 4: Select the decision with the minimum value from the column of max
regrets.
STATES OF NATURE Max
Alternatives Low High Regret
Small 0 14 14
Medium 3 7 7
Large 19 0 19
MinimaMinima
xx
RegretRegret
payoffpayoff
MinimaxMinimax
RegretRegret
decisiondecision

28. Generic Example
Consider the following problem with three decision alternatives
and three states of nature with the following payoff table
representing costs:
States of Nature
s1 s2 s3
d1 4.5 3 2
Decisions d2 0.5 4 1
d3 1 5 3
COST PAYOFF TABLE

29. Generic Example : Optimistic Decision
Optimistic Approach
An optimistic decision maker would use the optimistic
(maximax) approach. We choose the decision that has the best
single value in the payoff table.
Best
Decision Cost
d1 2
d2 0.5
d3 1
MaximaxMaximax
payoffpayoffMaximaxMaximax
decisiondecision

30. Generic Example: Conservative Approach
Conservative Approach
A conservative decision maker would use the conservative
(maximin) approach. List the worst payoff for each decision.
Choose the decision with the best of these worst payoffs.
Worst
Decision Payoff
d1 4.5
d2 4
d3 5
MaximinMaximin
decisiondecision
MaximinMaximin
payoffpayoff

31. Minimax Regret Approach
States of Nature
s1 s2 s3
d1 4.5 3 2
Decisions d2 0.5 4 1
d3 1 5 3
Best cost for each state of nature.
Generic Example: Minimax Regret Decision
For a cost payoff
table, entries in
the regret table
represent
overpayments
(i.e. higher costs
incurred).

32. Example
Minimax Regret Approach (continued)
For each decision list the maximum regret. Choose the
decision with the minimum of these values.
States of Nature Max
s1 s2 s3 Regret
d1 4 0 1 4
Decisions d2 0 1 0 1
d3 0.5 2 2 2
MinimaxMinimax
decisiondecision
MinimaxMinimax
regretregret

33. Decision Making with Probabilities
Expected Value Approach
– 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.

34. Expected Value of a Decision Alternative
The expected value of a decision alternative is the sum of weighted
payoffs for the decision alternative.
The expected value (EV) of decision alternative di is defined as:
where: N = the number of states of nature
P(sj) = the probability of state of nature sj
Vij = the payoff corresponding to decision
alternative di and state of nature sj
EV( ) ( )d P s Vi j ij
j
N
=
=
∑1
EV( ) ( )d P s Vi j ij
j
N
=
=
∑1

35. 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 and probability is .4,.2 and .4 respectively The payoff table
(profits) for the three models is on the next slide.

36. Example: Burger Prince
Payoff Table
Average Number of Customers Per Hour
s1 = 80 (.4) s2 = 100(.2) s3 = 120(.4)
Model A $10,000 $15,000 $14,000
Model B $ 8,000 $18,000 $12,000
Model C $ 6,000 $16,000 $21,000

37. Example: Burger Prince
Expected Value Approach
Calculate the expected value for each decision. The decision
tree on the next slide can assist in this calculation. Here d1, d2, d3
represent the decision alternatives of models A, B, C, and s1, s2, s3
represent the states of nature of 80, 100, and 120.

38. 38
Example: Burger Prince
Decision Tree
11
.2
.4
.4
.4
.2
.4
.4
.2
.4
d1
d2
d3
s1
s1
s1
s2
s3
s2
s2
s3
s3
Payoffs
10,000
15,000
14,000
8,000
18,000
12,000
6,000
16,000
21,000
22
33
44

39. 39
Example: Burger Prince
Expected Value For Each Decision
Choose the model with largest EV, Model C.
33
d1
d2
d3
EMV = .4(10,000) + .2(15,000) + .4(14,000)
= $12,600
EMV = .4(8,000) + .2(18,000) + .4(12,000)
= $11,600
EMV = .4(6,000) + .2(16,000) + .4(21,000)
= $14,000
Model A
Model B
Model C
22
11
44

40. CAL Condos Revisited
Suppose market research was conducted in the community where
the complex will be built. This research allowed the company to
estimate that the probability of low demand will be 0.35, and the
probability of high demand will be 0.65. Which decision alternative
should they select.

41. CAL Condos Revisited
STATES OF NATURE
Alternatives Low (0.35) High (0.65)
Small 8 8
Medium 5 15
Large -11 22

42. CAL Condos Revisited
STATES OF NATURE
Alternatives Low High
(0.35) (0.65) Expected value (EV)
Small 8 8 8(0.35) + 8(0.65) = 8
Medium 5 15 5(0.35) + 15(0.65) = 11.5
Large -11 22 -11(0.35) + 22(0.65) = 10.45
Recall that this is a profit payoff table. Thus since the decision to build a medium
complex has the highest expected profit, this is our best decision.

43. Expected Value of Perfect Information
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.

44. Expected Value of Perfect Information
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).

45. 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(15,000) + .2(18,000) + .4(21,000) - 14,000 = $2,000

46. Sensitivity Analysis
Some of the quantities in a decision analysis, particularly the
probabilities, are often intelligent guesses at best.
It is important to accompany any decision analysis with a sensitivity
analysis.
Sensitivity analysis can be used to determine how changes to the
following inputs affect the recommended decision alternative:
– probabilities for the states of nature
– values of the payoffs
If a small change in the value of one of the inputs causes a change
in the recommended decision alternative, extra effort and care
should be taken in estimating the input value.

47. Sensitivity Analysis
One approach to sensitivity analysis is to arbitrarily assign different
values to the probabilities of the states of nature and/or the payoffs
and resolve the problem. If the recommended decision changes,
then you know that the solution is sensitive to the changes.
For the special case of two states of nature, a graphical technique
can be used to determine how sensitive the solution is to the
probabilities associated with the states of nature.

48. CAL Condos: Sensitivity Analysis
This problem has two states of nature. Previously, we stated that
CAL Condominiums estimated that the probability of future low
demand is 0.35 and 0.65 is the probability of high demand. These
probabilities yielded the recommended decision to build the medium
complex.
In order to see how sensitive this recommendation is to changing
probability values, we will let p equal the probability of low demand.
Thus (1-p) is the probability of high demand. Therefore
EV( small) = 8*p + 8*(1-p)= 8
EV( medium) = 5*p + 15*(1-p) = 5p + 15 – 15p = 15 – 10p
EV( large) = -11*p + 22*(1-p) = -11p + 22 – 22p

49. CAL Condos: Sensitivity Analysis
Next we will plot the expected value lines for each decision by
plotting p on the x axis and EV on the y axis.
EV( small) = 8
EV( medium) = 15 – 10p
EV( large) = 22 – 33p

50. CAL Condos: Sensitivity Analysis
0 0.2 0.4 0.6 0.8 1
0
5
10
15
20
25
EV( medium)
EV( large)
EV( small)

51. CAL Condos: Sensitivity Analysis
Since CAL condominiums list payoffs are in terms of profits, we
know that the highest profits is desirable.
Look over the entire range of p (p=0 to p=1) and determine the
range over which each decision yields the highest profits.

52. CAL Condos: Sensitivity Analysis
0 0.2 0.4 0.6 0.8 1
0
5
10
15
20
25
EV( medium)
EV( large)
EV( small)
B1 B2

53. CAL Condos: Sensitivity Analysis
Do not estimate the values of B1 or B2 (the points where the intersection of lines
occur). Determine the exact intersection points.
B1 is the point where the EV( large) line intersects with the EV( medium) line:
To find this point set these two lines equal to each other and solve for p.
22-33p= 15-10p
7= 23p
p=7/23= 0.3403
B2 is the point where the EV( medium) line intersects with the EV( small) line:
15-10p = 8
7 = 10p
p = 0.7
So B1 equals 0.3403
So B2 equals 0.7

54. CAL Condos: Sensitivity Analysis
0 0.2 0.4 0.6 0.8 1
0
5
10
15
20
25
EV( medium)
EV( large)
EV( small)
0.3403 0.7

55. CAL Condos: Sensitivity Analysis
From the graph we see that if the probability of low demand (p) is
between 0 and 0.3403, we recommend building a large complex.
From the graph we see that if the probability of low demand (p) is
between 0.3403 and 0.7, we recommend building a medium
complex.
From the graph we see that if the probability of low demand (p) is
between 0.7 and 1, we recommend building a large complex.
From this sensitivity analysis we see that if CAL Condos estimate of
0.35 for the probability of low demand was slightly lower, the
recommended decision would change.

56. Decision Tree