Quantitative Analysis week 7 Discussion

Each week, you will be asked to respond to the prompt or prompts in the discussion forum. Your initial post should be a minimum of 300 words in length, and, you should respond to two additional posts from your peers.

Decision Analysis

Decision tree is a most important part in Decision Analysis. Please refer to this site to see what is the Decision Tree Analysis and how does it help a business to analyze data? Then give a real world example showing how to use decision tree for more intelligent Decision Analysis?

If you use any source outside of your own thoughts, you should reference that source. Include solid grammar, punctuation, sentence structure, and spelling.

An Introduction to Management Science, 15e Quantitative Approaches to Decision Making

Anderson Sweeney Williams Camm Cochran Fry Ohlmann

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Chapter 13: Decision Analysis

13.1 – Problem Formulation

13.2 – Decision Making without Probabilities

13.3 – Decision Making with Probabilities

13.4 – Risk Analysis and Sensitivity Analysis

13.5 – Decision Analysis with Sample Information

13.6 – Computing Branch Probabilities with Bayes’ Theorem

13.7 – Utility Theory

 

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Decision Analysis

Decision analysis can be used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain or risk-filled pattern of future events.

Even when a careful decision analysis has been conducted, the uncertain future events make the final consequence uncertain.

The risk associated with any decision alternative is a direct result of the uncertainty associated with the final consequence.

Good decision analysis includes risk analysis that provides probability information about the favorable as well as the unfavorable consequences that may occur.

 

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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 may occur. States of nature should be defined so that they are mutually exclusive and collectively exhaustive.

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Example: Pittsburgh Development Corp.

Pittsburgh Development Corporation (PDC) purchased land that will be the site of a new luxury condominium complex. PDC commissioned preliminary architectural drawings for three different projects: one with 30, one with 60, and one with 90 condominiums.

 

The financial success of the project depends upon the size of the condominium complex and the chance event concerning the demand for the condominiums. The statement of the PDC decision problem is to select the size of the new complex that will lead to the largest profit given the uncertainty concerning the demand for the condominiums.

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Influence Diagrams (1 of 2)

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.

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Influence Diagrams (2 of 2)

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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.

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Example: Pittsburgh Development Corp.

Consider the following problem with three decision alternatives and two states of nature with the following payoff table representing profits:

 

 

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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 decision with the largest payoff or lowest cost is chosen.

the conservative approach – for each decision the minimum payoff is listed and the decision corresponding to the maximum of these payoffs is selected. Or the maximum costs are determined and the minimum of those is selected.

the minimax regret approach.

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Example: Optimistic Approach

An optimistic decision maker would use the optimistic (maximax) approach. We choose the decision that has the largest single value in the payoff table.

Decision Maximum Payoff
8
14
20

Maximax

decision

Maximax

payoff

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Example: Conservative Approach

A conservative decision maker would use the conservative (maximin) approach. List the minimum payoff for each decision. Choose the decision with the maximum of these minimum payoffs.

 

Decision Minimum Payoff
7
5
–9

Maximin

decision

Maximin

payoff

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Minimax Regret Approach

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.

 

 

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Example: Minimax Regret Approach (1 of 2)

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 8, 14, and 20 from 20; etc.

 

 

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Example: Minimax Regret Approach (2 of 2)

For each decision list the maximum regret. Choose the decision with the minimum of these values.

 

Decision Maximum Regret
12
6
16

Minimax

decision

Minimax

regret

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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.

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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:

 

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Decision Tree (1 of 2)

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.

 

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Decision Tree (2 of 2)

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Expected Value for Each Decision

Choose the decision alternative with the largest EV. Build the large complex.

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Expected Value of Perfect Information (1 of 2)

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.

EVPI = |EVwPI – EVwoPI|

 

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Expected Value of Perfect Information (2 of 2)

Expected Value with Perfect Information (EVwPI)

 

 

Expected Value without Perfect Information (EVwoPI)

 

 

Expected Value of Perfect Information (EVPI)

 

EVwPI = 0.8(20 mil) + 0.2(7 mil) = $17.4 mil

EVwoPI = 0.8(20 mil) + 0.2(-9 mil) = $14.2 mil

EVPI = |EVwPI – EVwoPI| = |17.4 – 14.2| = $3.2 mil

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Risk Analysis

Risk analysis helps the decision maker recognize the difference between:

the expected value of a decision alternative, and

the payoff that might actually occur

The risk profile for a decision alternative shows the possible payoffs for the decision alternative along with their associated probabilities.

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Risk Profile

Large Complex Decision Alternative

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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.

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Decision Analysis with Sample Information

Frequently, decision makers have preliminary or prior probability assessments for the states of nature that are the best probability values available at that time.

To make the best possible decision, the decision maker may want to seek additional information about the states of nature.

This new information, often obtained through sampling, can be used to revise the prior probabilities so that the final decision is based on more accurate probabilities for the states of nature.

These revised probabilities are called posterior probabilities.

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Example: Pittsburgh Development Corp.

Let us return to the PDC problem and assume that management is considering a 6-month market research study designed to learn more about potential market acceptance of the PDC condominium project. Management anticipates that the market research study will provide one of the following two results:

Favorable report: A significant number of the individuals contacted express interest in purchasing a PDC condominium.

Unfavorable report: Very few of the individuals contacted express interest in purchasing a PDC condominium.

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Influence Diagram

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Sample Information (1 of 2)

PDC has developed the following branch probabilities.

 

If the market research study is undertaken:

 

P(Favorable report) = P(F) = 0.77

P(Unfavorable report) = P(U) = 0.23

If the market research report is favorable:

P(Strong demand | favorable report) = P(s1|F) = 0.94

P(Weak demand | favorable report) = P(s2|F) = 0.06

 

 

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Sample Information (2 of 2)

If the market research report is unfavorable:

 

P(Strong demand | unfavorable report) = P(s1|U) = 0.35

P(Weak demand | unfavorable report) = P(s2|U) = 0.65

If the market research study is not undertaken, the prior

probabilities are applicable:

P(Favorable report) = P(F) = 0.80

P(Unfavorable report) = P(U) = 0.20

 

 

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Decision Tree 1

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Decision Strategy

A decision strategy is a sequence of decisions and chance outcomes where the decisions chosen depend on the yet-to-be-determined outcomes of chance events.

The approach used to determine the optimal decision strategy is based on a backward pass through the decision tree using the following steps:

At chance nodes, compute the expected value by multiplying the payoff at the end of each branch by the corresponding branch probabilities.

At decision nodes, select the decision branch that leads to the best expected value. This expected value becomes the expected value at the decision node.

 

 

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Decision Tree 2

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PDC’s Decision Strategy

PDC’s optimal decision strategy is:

Conduct the market research study.

If the market research report is favorable, construct the large condominium complex.

If the market research report is unfavorable, construct the medium condominium complex.

 

 

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PDC’s Risk Profile

 

Risk Profile

 

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Expected Value of Sample Information

The expected value of sample information (EVSI) is the additional expected profit possible through knowledge of the sample or survey information.

The expected value associated with the market research study is $15.93.

The best expected value if the market research study is not undertaken is $14.20.

We can conclude that the difference, $15.93  $14.20 = $1.73, is the expected value of sample information.

Conducting the market research study adds $1.73 million to the PDC expected value.

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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.

The efficiency of the survey:

 

E = (EVSI/EVPI) X 100

= [($1.73 mil)/($3.20 mil)] X 100

= 54.1%

 

The information from the market research study is 54.1% as efficient as perfect information.

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Computing Branch Probabilities (1 of 2)

We will need conditional probabilities for all sample outcomes given all states of nature, that is, P(F | s1), P(F | s2), P(U | s1), and P(U | s2).

Market Research
State of Nature Favorable, F Unfavorable, U
Strong demand, s1 P(F| s1) = 0.90 P(U| s1) = 0.10
Weak demand, s1 P(F| s2) = 0.25 P(U| s2) = 0.75

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Computing Branch Probabilities (2 of 2)

Branch (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.

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Bayes’ Theorem and Posterior Probabilities

Knowledge of sample (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 or branch probabilities for decision trees.

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Posterior Probabilities (1 of 2)

Favorable
State of Nature sj Prior Probability P(sj) Conditional Probability P(F|sj) Joint Probability P(F I sj) Posterior Probability P(sj |F)
s1 0.8 0.90 0.72 0.94
s2 0.2 0.25 0.05 0.06
P(favorable) = P(F) = 0.77 1.00

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Posterior Probabilities (2 of 2)

Unfavorable
State of Nature sj Prior Probability P(sj) Conditional Probability P(U|sj) Joint Probability P(U I sj) Posterior Probability P(sj |U)
s1 0.8 0.10 0.08 0.35
s2 0.2 0.75 0.15 0.65
P(unfavorable) = P(U) = 0.23 1.00

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End of Presentation: Chapter 13

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