Table of content

Case Study..............................2

1.0 Definition of Decision Tree.........3

2.0 The certainty and uncertainty in

decision-making environment.............5

3.0 The principal steps of decision

analysis................................7

4.0 Decision Tree step-by-step..........8

5.0 Benefits of using decision analysis 14

6.0 Summary............................17

7.0 References.........................18

8.0 Coursework.........................19

Page 1 of 32

Case Study

Patrick Oil Company has leased the drilling rights on a large

parcel of land in Tawke that may not contain an oil reserve. A

competitor has offered to lease the land for $200,000 cash in

return for drilling rights and all rights to any oil that

might be found. The offer will expire in three days. If

Patrick does not take the deal, it will be faced with the

decision of whether to drill for oil on its own. Drilling

costs are projected to be $400,000. The company feels that

there are four possible outcomes from drilling:

1. dry hole (no oil or natural gas)

2. natural gas

3. natural gas and some oil

4. oil only

Page 2 of 32

If drilling yields a dry hole, the land will be basically

worthless, because it is located in the badlands of Tawke. If

natural gas is discovered, Patrick will recover only its

drilling costs. If natural gas and some oil are discovered,

revenue is projected to be $800,000. Finally, if only oil is

discovered, revenues will be $1600, 000.

Assignment:

(a) Describe the certainty and uncertainty in decision-

making environments.

(b) Structure the decision problem as a decision tree.

(c) Solve for Patrick Oil Company’ optimal decision

strategy.

1.0 Definition of Decision Tree

Decision Trees are excellent tools for helping you to choose

between several courses of action. They provide a highly

Page 3 of 32

effective structure within which you can lay out options and

investigate the possible outcomes of choosing those options.

They also help you to form a balanced picture of the risks and

rewards associated with each possible course of action.

In another words, decision tree is a schematic tree-shaped

diagram used to determine a course of action or show a

statistical probability. Each branch of the decision tree

represents a possible decision or occurrence. The tree

structure shows how one choice leads to the next, and the use

of branches indicates that each option is mutually exclusive.

Decision tree learning uses a decision tree as a predictive

model which maps observations about an item to conclusions

about the item's target value. It is one of the predictive

modeling approaches used in statistics, data

mining and machine learning. More descriptive names for such

tree models are classification trees or regression trees. In

these tree structures, leaves represent class labels and

branches represent conjunctions of features that lead to those

class labels.

Page 4 of 32

In decision analysis, a decision tree can be used to visually

and explicitly represent decisions and decision making.

In data mining, a decision tree describes data but not

decisions; rather the resulting classification tree can be an

input for decision making.

In my opinion, decision trees provide an effective method of

Decision Making because they are clearly lay out the problem

so that all options can be challenged. Besides that, decision

trees allow us to analyze fully the possible consequences of a

decision. It also provides a framework to quantify the values

of outcomes and the probabilities of achieving them. Last but

not lease, decision tress help us to make the best decisions

on the basis of existing information and best guesses.

As with all Decision Making methods, decision tree analysis

should be used in conjunction with common sense. This is why

Page 5 of 32

decision trees are just one important part of your Decision

Making tool kit.

2.0 The certainty and uncertainty in

decision-making environment

Page 6 of 32

The decisions are taken in different types of environment. The

type of environment also influences the way the decision is

made.

There are two types of environment in which decisions are

made.

1. Certainty:

In this type of decision making environment, there is only one

type of event that can take place. It is very difficult to

find complete certainty in most of the business decisions.

However, in many routine type of decisions, almost complete

certainty can be noticed. These decisions, generally, are of

very little significance to the success of business.

Condition under certainty is which the decision maker has full

and needed information to make a decision. Decision is made

under the condition of certainty. The manager knows exactly

what the outcome will be, as he or she has enough clarity

about the situation and knows the resources, time available

for decision-making, the nature of the problem itself,

Page 7 of 32

possible alternatives to resolve the problem, and undoubtedly

clarify or certain with the result of alternatives.

In most situations, the solutions are already available from

the past experiences or incidents and are appropriate for the

problem at hand. The decision to restock food supply, for

example, when the goods in stock fall below a determined level

is a decision-making under circumstances of certainty.

2. Uncertainty:

In the environment of uncertainty, more than one type of event

can take place and the decision maker is completely in dark

regarding the event that is likely to take place. The decision

maker is not in a position, even to assign the probabilities

of happening of the events.

In a situation of uncertainty, on the other hand, people have

only a meager database, they do not know whether or not the

data are reliable, and they are very unsure about whether or

not the situation may change.

Page 8 of 32

Moreover, they cannot evaluate the interactions of the

different variables. For example, a corporation that decides

to expand its Operation to an unfamiliar country may know

little about the country’s culture, laws, economic

environment, and politics. The political situation may be

volatile that even experts cannot predict a possible change in

government.

Such situations generally arise in cases where happening of

the event is determined by external factors. For example,

demand for the product, moves of competitors and so on are the

factors that involve uncertainty.

Page 9 of 32

3.0 The principal steps of decision

analysis

To solve a decision tree problem, the following steps are

used.

Step 1: Grow a decision tree.

Arrange the decisions and events in the order in which they

will occur. This is often difficult with complex decision

problem with complex decision problems, but unless the

decision tree accurately represents the situation, the

decision made may not be the best decision.

Step 2: Assign the possibilities.

Make the necessary probability assessments and show them on

the event branches. These probabilities can be determined

using classical assessment, relative frequency of occurrence,

or subjective techniques. (Remember that probabilities are

associated with the uncertain events and not with the decision

alternatives.)

Page 10 of 32

Step 3: Assign cash flow

Assign cash flow by showing costs and payoffs on the branches

where they occur. Accumulate these cash flows and determine

the end value for each branch of the decision tree.

Step 4: Fold back the decision tree

At each decision fork, select the decision that maximizes

expected payoffs or minimize expected costs.

4.0 Decision Tree step-by-step

Step 1: Grow the decision tree.

The decision tree is developed by organizing the decisions and

events in chronological order. In this example, the initial

decision to be made is whether to accept the lease. The tree

is started as:

Page 11 of 32

If the land is leased, no further decisions are required.

However, if the land is not leased, Patrick faces the decision

of whether to drill on the property. The tree then grows to:

Now, if Patrick decides to drill, there are four possible

events that could occur. These are shown on the decision tree.

Page 12 of 32

Patrick Oil Company

lease out the land

do not lease out the land

Patrick Oil Company

lease out the land

do not lease out the land

drill

not drill

When finished, the decision tree should show all the decisions

and events.

Page 13 of 32

Patrtick oil Company

lease out the land

do not lease out the land

do not drill

drill

dry hole

natural gas

natural gas and some

oil

oil only

Step 2: Assign probabilities to the event outcomes on the

tree.

In this example, the only event deals with the production

result if Patrick decides to drill. The company has

subjectively assessed of probability of each of the four

possible outcomes as follow:

Outcomes Probabil

ity

Dry hole 0.2

Natural gas only 0.4

Natural gas and

some oil

0.3

Oil only 0.1

Page 14 of 32

Step 3: Assign the cash flows to the tree.

At each branch of the tree at which revenue or a cost occurs,

show the dollar value. These revenues and costs are then

totaled across the tree and the end values for each branch are

determined. These cash flows are placed on the tree as

follows.

Page 15 of 32

Patrtick oil Company

lease out the land

do not lease out the land

do not drill

drill

0.2 dry hole

0.4 natural gas

0.3 natural gas and some oil

0.1 oil only

END VALUE

Page 16 of 32

Patrtick oil

Company

$200,000lease

out the land

$200,000

do not lease

out the land

do not drill

$0

-$400,000 drill

$00.2 dry hole

-$400,000

$400,0000.4

natural gas

$0$800,00

00.3

natural gas and some oil

$400,000

$1600,000

0.1 oil only

$1200.000

Step 4: Fold back the decision tree and compute the expected

values for each decision.

We need to compute the expected value for each decision

alternative. This is done starting from the right side of the

tree and working back to the left. We first determine the

expected value for the Drill branch as follows:

E [Drill] = $400,000(0.20) + $0(0.40) + $400,000(0.30) +

$1,200,000(0.10)

= $160,000

As we fold back the tree, we block all decision alternatives

that do not have the highest expected value. This is shown in

the decision tree as follows.

END VALUE

Page 17 of 32

Patrtick oil

Company

$200,000lease

out the land

$200,000

do not lease

out the land

do not drill

$0

-$400,000 drill

$00.2 dry hole

-$400,000

$400,0000.4

natural gas

$0$800,00

00.3

natural gas and some oil

$400,000

$1600,000

0.1 oil only

$1200.000

Note that we always select decisions with the highest

expectation payoff. In this example, the best decision is

to lease the land and accept $200,000 payment because

it exceed $160,000 expected value of the not non-

lease option.

Page 18 of 32

Patrtick oil

Company

$200,000lease

out the land

$200,000

do not lease

out the land

do not drill

$0

-$400,000 drill

$00.2 dry hole

-$400,000

$400,0000.4

natural gas

$0$800,00

00.3

natural gas and some oil

$400,000

$1600,000

0.1 oil only

$1200.000

5.0 Benefits of using decision analysis

Page 19 of 32

There are couples of benefits of using a decision tree. First

of foremost, it is very easy to understand and interpret for

any reader. To better understand how decision trees work, it

is best to consider some examples. The decision tree below

provides its students are on a company trying to decide

whether or not to invest in an online training program.

The main decision to be made, which starts in the first box,

is whether or not to spend money on a new training program.

From that box, the two options are yes or no, so one branch is

Page 20 of 32

drawn from the first box with a "yes" on it and another with a

"no" written on it. Since the outcome from each is still

unknown, circles are drawn at the end of each branch

From the yes branch, there are then two options for different

training programs, system 1 and system 2. So, from the circle

at the end of the yes branch, two more branches are drawn: one

for system one and the other for system two. From there,

branches are drawn out of for how the system will be paid for,

either in full or monthly installments.

On the "no" side of the tree, branches are drawn to continue

using the existing training method or hire live training

experts. Since the existing plan is already in place, no more

branches are drawn out from that branch. From the hiring live

trainers branch, two more branches are drawn for those to be

either field technicians or sales engineers.

Once the tree is complete, the costs and probabilities are

assigned to each branch in order to calculate and evaluate

each option.

Page 21 of 32

By building the decision tree, small details that may have

been missed are taken into consideration. For the examples,

since there are a lot of calculations involved in creating

decision trees, many businesses use dedicated decision tree

software to help them with the process. Decision tree software

helps businesses draw out their trees, assigns value and

probabilities to each branch and analyzes each option.

An addition to that, decision trees require relatively little

effort from users for data preparation. To overcome scale

differences between parameters - for example if we have a

dataset which measures revenue in millions and loan age in

years, say; this will require some form of normalization and

scaling before we can fit a regression model and interpret the

coefficients.  Such variable transformations are not required

with decision trees because the tree structure will remain the

same with or without the transformation. Another feature which

saves data prep time: missing values will not prevent

splitting the data for building trees. This article describes

how decision trees are built. Decision trees are also not

Page 22 of 32

sensitive to outliers since the splitting happens based on

proportion of samples within the split ranges and not on

absolute values.

Decision trees help in brainstorming outcomes. Decision trees

help you think of all possible outcomes for an upcoming

choice. The consequences of each outcome must be fully

explored, so no details are missed. Taking the time to

brainstorm prevents overreactions to any one variable. The

graphical depiction of various alternatives makes them easier

to compare with each other. The decision tree also adds

transparency to the process. An independent party can see

exactly how a particular decision was made.

Another benefit of decision tree is displaying the sequencing

and interrelations of tasks and events. The schematic display

of starting events, secondary and terminating events allow for

insights into input / output relationship and start/stop

phasing as branching is extending into the future. Priorities

can be established from the difficulties, complexities, and

time requirements suggested by each path.

Page 23 of 32

6.0 Summary

Through this assignment, I am now know essentially all of the

important points related to learning decision trees as well as

many points seminal to learning in general.

First and foremost, I know that learning a classifier can be

useful in decision making. Next, I know what a decision tree

is. Besides that, I have learn that how to write a basic

decision tree analysis step by step. Last but not lease, I

Page 24 of 32

know how to make decision accurately by constructing a

decision tree when facing the conflicts.

In conclusion, decision tree analysis really guides me a lot

in term of making my decision. It is useful for me for the

future when I own a company.

Of course, my point of views is just the tip of the iceberg;

there are many other areas to investigate such as some fairly

well resolved, while others remain topics of very active

research, which will continue to lead to interesting, and

financially rewarding, results.

Page 25 of 32

7.0 References

http://www.investopedia.com/terms/d/decision-tree.asp

http://www.mindtools.com/dectree.html

http://en.wikipedia.org/wiki/Decision_tree_learning

http://www.businessnewsdaily.com/6147-decision-tree.html

http://www.simafore.com/blog/bid/62333/4-key-advantages-of-

using-decision-trees-for-predictive-analytics

http://smallbusiness.chron.com/advantages-decision-trees-

75226.html

http://decisiontreemodel.blogspot.com/

http://www.clt.astate.edu/asyamil/groebner6ed/ppt/chap18.pdf

http://webdocs.cs.ualberta.ca/~aixplore/learning/

DecisionTrees/InterArticle/9-DecisionTree.html

http://www.yourarticlelibrary.com/decision-making/decisions-

making-environments-certainty-uncertainty-and-risk/10269/

Page 26 of 32

http://www.studymode.com/essays/Decision-Making-Under-

Certainty-Uncertainty-And-153805.html

8.0 Coursework

NAME: CHOCK MEI YAN

NRIC: 930330-10-5616

H/P NUMBER: +6016-9827381

STUDENT ID: 200106

1. Decision Management Systems are differing from

traditional Decision Support System in five ways. Explain

carefully.

Decision Support Systems provide information that

describes the situation and perhaps historical

trends so that humans can decide what to do and

which actions to take. Decision Management Systems

Page 27 of 32

automate or recommend the actions that should be

taken based on the information that is available at

the time the decision is being made.

The policies, regulations, and best practices that

determine the best action are embedded, at least in

part, in a Decision- Management System where a

Decision Support System requires the user to

remember them or look them up separately.

The information and insight presented in a Decision

Support System is typically backward looking, and

Decision Support Systems are generally reactive—

helping human decision-makers react to a new or

changed situation by presenting information that

might help them make a decision. In contrast,

Decision Management Systems use information to make

predictions and aim to be proactive.

Learning is something that happens outside a

Decision Support System and inside a Decision

Management System. Users of Decision Support Systems

are expected to learn what works and what does not

Page 28 of 32

work and to apply what they learn to future

decisions. Decision Management Systems have

experimentation or test-and-Learn infrastructure

built in so that the system itself learns what works

and what does not.

Decision Management Systems are integrated into an

organization's runtime environment. They make

decisions for applications and services in the

organization's enterprise application architecture.

In contrast, Decision Support Systems are often

desktop or interactive applications that execute

outside the core application portfolio.

Page 29 of 32

2. A Business-centric rules management environment has a

number of characteristics. Explain carefully.

Each business expert sees only the business rules for

which he is responsible:

Multiple business experts might use the rule

management environment, and they should be able to

read those rules they have read permissions for and

change those rules they have read/write permissions

for. They should not have to navigate through a lot

of other rules or rule repository structures to find

them.

These business rules are presented in context

Business users are making these changes in a business

context—for instance, they are responding to new

regulations or trying to improve the performance of

the decision service. This context should be

reflected in the rule management environment so that

Page 30 of 32

changing the rules feels like just part of running

the business.

The business rules editing environment allows only

those changes that make sense:

Some rules can only be changed in certain ways

because of the underlying data—only certain values

can be set as a consequence of a rule, for

instance. Conditions and consequences that make no

business or technical sense should not be allowed;

if the overall decision service constrains the

specific rules in question to behave within a

range of allowed behaviour, then this should also

be enforced.

The business expert can rapidly see the impact of her

proposed changes: The rule management environment

should be linked to the impact analysis tools and

techniques described later.

No unnecessary technical information is presented

Page 31 of 32

Page 32 of 32