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Decision Analysis (Decision Trees ). Y. İlker TOPCU , Ph .D. www.ilkertopcu. net www. ilkertopcu .org www. ilkertopcu . info www. facebook .com/ yitopcu twitter .com/ yitopcu. Decision Trees. A decision tree is a diagram consisting of decision nodes (squares)

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Decision Analysis (Decision Trees )

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## Decision Analysis(Decision Trees)

Y. İlker TOPCU, Ph.D.

www.ilkertopcu.net www.ilkertopcu.org www.ilkertopcu.info

### Decision Trees

• A decision tree is a diagram consisting of

• decision nodes (squares)

• chance nodes (circles)

• decision branches (alternatives)

• chance branches (state of natures)

• terminal nodes (payoffsorutilities)

q1

x11

a1

qn

x1n

a2

am

q1

xm1

qn

xmn

### Decision Tree Method

• Define the problem

• Structure / draw the decision tree

• Assign probabilities to the states of nature

• Calculate expected payoff (or utility) for the corresponding chance node – backward, computation

• Assign expected payoff (or utility) for the corresponding decision node – backward, comparison

• Represent the recommendation

### Example 1

A chancenode

Favorable market(0.6)

\$200,000

1

Unfav. market(0.4)

Constructlargeplant

-\$180,000

A decisonnode

Favorable market(0.6)

\$100,000

Constructsmallplant

2

Unfav. market

(0.4)

-\$20,000

Do nothing

\$0

A chancenode

Favorable market(0.6)

\$200,000

1

Unfav. market(0.4)

EV =

\$48,000

Constructlargeplant

-\$180,000

A decisonnode

Favorable market(0.6)

\$100,000

Constructsmallplant

2

Unfav. market

(0.4)

EV =

\$52,000

-\$20,000

Do nothing

\$0

184

220

130

%60

%60

%60

186

210

%40

%40

%40

150

170

162

150

### Sequential Decision Tree

• A sequential decision tree is used to illustrate a situation requiring a series of decisions (multi-stage decision making) and it is used where a payoff matrix (limited to a single-stage decision) cannot be used

### Example 3

• Let’s say that DM has two decisions to make, with the second decision dependent on the outcome of the first.

• Before deciding about building a new plant, DM has the option of conducting his own marketing research survey, at a cost of \$10,000.

• The information from his survey could help him decide whether to construct a large plant, a small plant, or not to build at all.

• Before survey, DM believes that the probability of a favorable market is exactly the same as the probability of an unfavorable market: each state of nature has a 50% probability

• There is a 45% chance that the survey results will indicate a favorable market

• Such a market survey will not provide DM with perfect information, but it may help quite a bit nevertheless by conditional (posterior) probabilities:

• 78% is the probability of a favorable market given a favorable result from the market survey

• 27% is the probability of a favorable market given a negative result from the market survey

### Example 4

• A manager has to decide whether to market a new product nationally and whether to test market the product prior to the national campaign.

• The costs of test marketing and national campaign are respectively \$20,000 and \$100,000.

• Their payoffs are respectively \$40,000 and \$400,000.

• A priori, the probability of the new product's success is 50%.

• If the test market succeeds, the probability of the national campaign's success is improved to 80%.

• If the test marketing fails, the success probability of the national campaign decreases to 10%.

[240]

S(.8)

320

C

[240]

F(.2)

~C

S(.5)

-80

20

[110]

S(.1)

[-80]

280

F(.5)

T

[-20]

F(.9)

C

[110]

-120

~C

~T

-20

[100]

[100]

C

S(.5)

300

~C

F(.5)

-100

0

### Expected Value of Sample Information

EVSI

= EV of best decision withsample information, assuming no cost to gather it

– EV of best decision without sample information

= EV with sample info. + cost – EV without sample info.

DM could pay up to EVSI for a survey.

If the cost of the survey is less than EVSI, it is indeed worthwhile.

In the example:

EVSI = \$49,200 + \$10,000 – \$40,000 = \$19,200

### Estimating Probability Values by Bayesian Analysis

Bayes Theorem

Posterior

probabilities

Prior

probabilities

New data

• Management experience or intuition

• History

• Existing data

• Need to be able to reviseprobabilities based upon new data

### Bayesian Analysis

Example:

• Market research specialists have told DM that, statistically, of all new products with a favorable market, market surveys were positive and predicted success correctly 70% of the time.

• 30% of the time the surveys falsely predicted negative result

• On the other hand, when there was actually an unfavorable market for a new product, 80% of the surveys correctly predicted the negative results.

• The surveys incorrectly predicted positive results the remaining 20% of the time.

### Market Survey Reliability

Actual States of Nature

Result of Survey

Favorable

Unfavorable

Market (FM)

Market (UM)

(survey positive|FM)

(survey positive|UM)

Positive (predicts

P

P

= 0.70

=

0.20

favorable market

for product)

(survey

(survey negative|UM)

Negative (predicts

P

P

negative|FM) = 0.30

= 0.80

unfavorable

market for

product)

### Calculating Posterior Probabilities

P(BA) P(A)

P(AB) =

P(BA) P(A) + P(BA’) P(A’)

where A and B are any two events, A’ is the complement of A

P(FMsurvey positive) =

[P(survey positiveFM)P(FM)] /

[P(survey positiveFM)P(FM) + P(survey positiveUM)P(UM)]

P(UMsurvey positive) =

[P(survey positiveUM)P(UM)] /

[P(survey positiveFM)P(FM) + P(survey positiveUM)P(UM)]

Probability Revisions Given a Positive Survey

Conditional

Probability

State

P(Survey positive|State of Nature

Prior Probability

Joint Probability

Posterior Probability

of

Nature

0.35

= 0.78

FM

0.70

* 0.50

0.35

0.45

0.10

= 0.22

0.20

0.10

* 0.50

UM

0.45

1.00

0.45

Probability Revisions Given a Negative Survey

Conditional

Probability

State

P(Survey

Prior Probability

Joint Probability

Posterior Probability

of

negative|State

Nature

of Nature)

0.15

= 0.27

0.15

0.30

* 0.50

FM

0.55

0.40

= 0.73

0.40

UM

0.80

* 0.50

0.55

0.55

1.00