1. Chapter 8
Decision Analysis
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2. Decision Analysis
A method for determining optimal strategies when faced with several decision alternatives and an uncertain pattern of future events.
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3. The Decision Analysis Approach
Identify the decision alternatives - di
Identify possible future events - sj
mutually exclusive - only one state can occur
exhaustive - one of the states must occur
Determine the payoff associated with each decision and each state of nature - Vij
Apply a decision criterion
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4. Types of Decision Making Situations
Decision making under certainty
state of nature is known
decision is to choose the alternative with the best payoff
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5. Types of Decision Making Situations
Decision making under uncertainty
The decision maker is unable or unwilling to estimate probabilities
Apply a common sense criterion
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6. Decision Making Under Uncertainty
Maximax Criterion (for profits) - optimistic
list maximum payoff for each alternative
choose alternative with the largest maximum payoff
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8. Decision Making Under Uncertainty
Maximin Criterion (for profits) - pessimistic
list minimum payoff for each alternative
choose alternative with the largest minimum payoff
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10. Decision Making Under Uncertainty
Minimax Regret Criterion
calculate the regret for each alternative and each state
list the maximum regret for each alternative
choose the alternative with the smallest maximum regret
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11. Decision Making Under Uncertainty
Minimax Regret Criterion
Regret - amount of loss due to making an incorrect decision - opportunity cost
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13. Types of Decision Making Situations
Decision making under risk
Expected Value Criterion
compute expected value for each decision alternative
select alternative with “best” expected value
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14. Computing Expected Value
Let:
P(sj)=probability of occurrence for state sj
and
N=the total number of states
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15. Computing Expected Value
Since the states are mutually exclusive and exhaustive
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16. Types of Decision Making Situations
Then the expected value of any decision di is
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18. Decision Trees A graphical representation of a decision situation
Most useful for sequential decisions
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19. $200K 2 $-20K $150K 3 1 $20K $100K 4 $60K P(S1) = .3 Large P(S2) = .7
Medium 3 1
$20K
P(S2) = .7
Small
P(S1) = .3
$100K 4
$60K
P(S2) = .7
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20. $200K 2 $-20K $150K 1 3 $20K $100K 4 $60K EV2 = 46 P(S1) = .3 Large
Medium 1 3
$20K
P(S2) = .7
Small
EV4 = 72
P(S1) = .3
$100K 4
$60K
P(S2) = .7
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21. Decision Making Under Risk: Another Criterion
Expected Regret Criterion
Compute the regret table
Compute the expected regret for each alternative
Choose the alternative with the smallest expected regret
The expected regret criterion will always yield the same decision as the expected value criterion.
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22. Expected Regret Criterion
The expected regret for the preferred decision is equal to the Expected Value of Perfect Information - EVPI
EVPI is the expected value of knowing which state will occur.
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23. EVPI – Alternative to Expected Regret
EVPI – Expected Value of Perfect Information
EVwPI – Expected Value with Perfect Information about the States of Nature
EVwoPI – Expected Value without Perfect Information about the States of Nature
EVPI=|EVwPI-EVwoPI|
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24. Analyze this decision using the maximax (optimistic) approach.
Example 1: Mass. Bay Production (MBP) is planning a new manufacturing facility for a new product. MBP is considering three plant sizes, small, medium, and large. The demand for the product is not fully known, but MBP assumes two possibilities, 1. High demand, and 2. Low demand. The profits (payoffs) associated with each plant size and demand level is given in the table below.
Decision
State of Nature
Plant Size
High Demand (S1)
Low Demand (S2)
Large (d1)
$200 K
$-20 K
Medium (d2)
$150 K
$ 20 K
Small (d3)
$100 K
$ 60 K
Analyze this decision using the maximax (optimistic) approach.
Analyze this decision using the maximin (conservative) approach.
Analyze this decision using the minimax regret criterion.[1]
Now assume the decision makers have probability information about the states of nature. Assume that P(S1)=.3, and P(S2)=.7. Analyze the problem using the expected value criterion.[2]
How much would you be willing to pay in this example for perfect information about the actual demand level? (EVPI)
Compute the expected opportunity loss (EOL) for this problem. Compare EOL and EVPI.
[1] D.W. Bunn discusses the regret criterion as follows. “The minimax regret criterion often has considerable appeal, particularly wherever decision makers tend to be evaluated with hindsight. Of course, hindsight is an exact science, and our actions are sometimes unfairly compared critically with what might have been done. Many organizations seem implicitly to review and reward their employees in this way.” Bunn, D.
W., Applied Decision Analysis.
[2] Note that that P(S1) and P(S2) are complements, so that that P(S1)+P(S2)=1.0.
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27. Bayes Law
In this equation, P(B) is called the prior probability of B and P(B|A) is called the posterior, or sometimes the revised probability of B. The idea here is that we have some initial estimate of P(B) , and then we get some additional information about whether A happens or not, and then we use Bayes Law to compute this revised probability of B.
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28. Now suppose that MBP has the option of doing market research to get a better estimate of the likely level of demand. Market Research Inc. (MRI) has done considerable research in this area and established a documented track record for forecasting demand. Their accuracy is stated in terms of probabilities, conditional probabilities, to be exact.
Let F be the event: MRI forecasts high demand (i.e., MRI forecasts S1)
Let U be the event: MRI forecasts low demand (i.e., MRI forecasts S2)
The conditional probabilities, which quantify MRI’s accuracy, would be:
Suppose that
This would say that 80% of the time when demand is high, MRI forecasts high demand. In addition, 75% of the time when the demand is low, MRI forecasts low demand. In the calculations, which follow, however, we will need to reverse these conditional probabilities. That is, we will need to know:
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29. Blank page for work
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30. Bayes Law Using a Tabular Approach (finding posteriors for F given)
Bayes Law can also be computed using a tabular approach as in the tables below.
Bayes Law Using a Tabular Approach (finding posteriors for F given)
.30
.80
(.80)(.30)=.24
.70
.25
(.25)(.70)=.175
Note: The two numbers above are complements
States of Nature
Prior Probabilities
Conditional Probabilities
Joint Probabilities
Posterior Probabilities
Bayes Law Using a Tabular Approach (finding posteriors for U given)
.30
.20
(.20)(.30)=.06
.70
.75
(.75)(.70)=.525
Note: The two numbers above are complements
Posterior Probabilities
States of Nature
Prior Probabilities
Conditional Probabilities
Joint Probabilities
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31. Now, using Bayes Law, we can construct a new decision tree, which will give us a decision strategy: Should we pay MRI for the market research? If we do not do the market research, what should our decision be? If we do the market research and get an indication of high demand, what should our decision be? If we get an indication of low demand, what should our decision be? We will use a decision tree as shown below to determine this strategy.
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32. $200K 4 Large $-20K $150K Medium 5 2 $20K Favorable Forecast $100K
EV4= $107.16K
$200K
P(S1|F)= .578 4
Large
$-20K
P(S2|F)=.422
EV2=
EV5= $95.14K
$150K
P(S1|F)= .578
Medium 5 2
$20K
Favorable Forecast
P(S2|F)=.422
EV6= $83.12K
$100K
P(S1|F)= .578
Small 6
$60K
P(F)= .415
P(S2|F)=.422
EV7= $2.66K 1
$200K
$-20K
$150K
$20K
$100K
$60K
EV1= $81.98K
P(S1|U)= .103
P(S1|U)=.103
P(S2|U)=.897
Large 7
P(U)= .585
Unfavorable Forecast
EV8= $33.39K
Medium 8 3
Do Survey
EV3= 64.12
EV9= $64.12K
Small 9
Don’t do
Survey
$72K
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33. Expected Value of Sample Information – EVSI
EVSI – Expected Value of Sample Information
EVwSI – Expected Value with Sample Information about the States of Nature
EVwoSI – Expected Value without Sample Information about the States of Nature
EVSI=|EVwSI-EVwoSI|
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34. Efficiency of Sample Information – E
Perfect Information has an efficiency rating of 100%, the efficiency rating E for sample information is computed as follows:
Note: Low efficiency ratings for sample information might lead the decision maker to look for other types of information
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35. Example 2: The LaserLens Company (LLC) is considering introducing a new product, which to some extent will replace an existing product. LLC is unsure about whether to do this because the financial results depend upon the state of the economy. The payoff table below gives the profits in K$ for each decision and each economic state.
Decision
State of Nature
Strong Economy (S1)
Weak Economy (S2)
Introduce New Product (d1)
$140K
$-12 K
Keep Old Product (d2)
$ 25 K
$ 35 K
Analyze this decision using the maximax (optimistic) approach.
Analyze this decision using the maximin (conservative) approach.
Analyze this decision using the minimax regret criterion.
Now assume the decision makers have probability information about the states of nature. Assume that P(S1)=.4. Analyze the problem using the expected value criterion.
How much would you be willing to pay in this example for perfect information about the actual state of the economy? (EVPI)
Compute the expected opportunity loss (EOL) for this problem. Compare EOL and EVPI.
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36. Now suppose that LLC has the option of contracting with an economic forecasting firm to get a better estimate of the future state of the economy. Economics Research Inc. (ERI) is the forecasting firm being considered. After investigating ERI’s forecasting record, it is found that in the past, 64% of the time when the economy was strong, ERI predicted a strong economy. Also, 95% of the time when the economy was weak, ERI predicted a weak economy.
Bayes Law Using a Tabular Approach (finding posteriors)
States of Nature
Prior Probabilities
Conditional Probabilities
Joint Probabilities
Posterior Probabilities
Bayes Law Using a Tabular Approach (finding posteriors)
States of Nature
Prior Probabilities
Conditional Probabilities
Joint Probabilities
Posterior Probabilities
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37. 7a. Determine LLC’s best decision strategy
7a. Determine LLC’s best decision strategy. Should they hire ERI or go ahead without additional information? If they buy the economic forecast, what should their subsequent decision strategy be?
7b. Determine how much LLC should be willing to pay (maximum) to ERI for an economic forecast.
7c. What is the efficiency of the information provided by ERI?
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38. $140K 4 d1 $-12K 2 Favorable Forecast $25K d2 5 $35K 1 $140K $-12K
EV4= $124.04K
$140K
P(S1|F)= .895 4 d1
$-12K
P(S2|F)=.105 2
Favorable Forecast
EV5= $26.05K
P(S1|F)= .895
$25K d2 5
$35K
P(F)= .286
P(S2|F)=.105 1
EV6= $18.70K
$140K
$-12K
$25K
$35K
EV1= $59.02
P(S1|U)= .202
P(S2|U)=.798 d1 6
P(U)= .714
Unfavorable Forecast 3
Hire ERI
EV7= $32.98K d2 7
Don’t hire
ERI
$48.8K
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39. Decision Making with Cost Data
Consider the following payoff table, which gives three decisions and their costs under each state of nature. The company’s objective is to minimize cost.
State of Nature
Decision S1 S2 S3 d1
100 K$
40 K$ d2
30 K$
110 K$ d3
60 K$
75 K$
120 K$
1. Apply the optimistic (minimin cost) criterion.
2. Apply the conservative (minimax cost) criterion.
3. Apply the minimax regret criterion.
4. Assume that P(S1)=.40 and P(S2)=.20 Apply the expected value criterion.
5. Compute EVPI.
6. Compute EOL.
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