This document discusses decision trees and provides an example of using a decision tree to analyze a company's options regarding purchasing a patent. The document begins by defining decision trees as graphical representations of decisions and outcomes that make complex problems simple to understand. It then provides more details on what constitutes a decision tree, with branches representing possible decisions and statistical probabilities. The document goes on to give a specific example of a company facing the decision of whether to purchase a patent for $200,000, or spend an additional $50,000 on market research first. It lays out the probabilities and potential monetary outcomes for each option in a decision tree diagram. After calculating the expected values at each branch, it determines that purchasing the patent without

1. DECISION
TREES
MOHAMED ASHIQ P M
MES MAMPAD COLLEGE
(AUTONOMOUS)

2. INTRODU
CTION
Decision tree is a graphical picture of decision &
outcome.
It make complex to simple.
Simply decision trees give people an effective & easy
way to understand the potential options of decision &
its range of possible outcomes.

3. DECISION
TREES
Decision Tree is a tree shaped diagram used to
determine a course of action or show a statistical
probability.
Each branch of the decision tree represent a possible
decision, occurrence or reactions.
The tree is structured to show how and why one choice
may lead to the next, with the use of the branches
indicating each option is mutually exclusive.

4. Explanation of Decision Tree
 A company has an opportunity to purchase a patent
for the manufacture of a new product of $200000/-
 It has three possible choices.
1. It does not purchase the patent.
2. It purchase the patent at $200000/-.
3. It spends an additional $50000/- on a feasibility study
before purchasing the patent.

5. Estimates.
 1) Probability that the additional research will find the product to have good
potential is 60 %.
 If the research results are favorable, there is an 80% probability that the product
net the company $10,00000/-.
 20%  income will be only $1,50,000/-.
 Unfavorable  90% prblty  $1,00000/-.
 10%  $8,00,000/-.
 2) If the company purchases the
patent without further research
 The income estimates are,
1. 30% prblty  $10,00000/-
2. 40%  $5,00,000/-
3. 30%  $150,000/-

7.  Set up all the branches.
 Left to right
 Faced with decision points & with chances of events with circles.
 When the entire tree is completed, the procedure to move back from right to
left, calculate the value of each branch, & where appropriate combine or
eliminate branches.
 Starting with the highest branch
 Which represents the situation in which research is conducted before
purchasing the patent & the researches are favorable, the expected income is
$8,30,000/- (0.8*10,00,000+0.2*1,50,000)
 Subtract the cost of the patent & research 2,50,000 then result is 5,80,000.
 Bcz not buying the patent , if the market research is favorable would result
in a net loss of 50,000/-.
 Eliminate the particular decision.

8.  Now, if the result of the research is unfavorable, the company would not
proceed with the purchase of patent bcz the loss is only 50,000/- compared
with a loss of 80,000/- , if the company does purchase it.
 Thus, if the company does the research, the resulting NPV(net present
value) is 3,28,000/-, bcz there is a 60% prblty of favorable results & 40%
unfavorable(0.6*580000+0.4*50000=328000)
 The result of not doing research proceeding the same way as we did
previously, if the company buys the patent the expected NPV will be
345000/-.
 Whereas if it does not go ahead, the result is of course on NPV of $0/-.
 Thus, purchasing the patent without additional research appears to be the
better alternative.
 The preceding solution is not quite complete, bcz the decision is being made
on the basis of expected NPV alone.
 There has been no calculation of SD; so we have in, in effect, ignored the
differences in risk between purchasing the patent with or without additional
research.

9. CONCLUSI
ON
•Decision Tree is used to clarify & find an answer to a complex problem.
•Easy to understand
•Shows r/s b/w different events or decisions.
•The furthest branches on the tree represent the end results.
•Each end result should be assigned a risk and reward weight or number.
•A user analyzing a decision tree looks at each final outcome & assesses the
benefits and drawbacks.
•The result of highest total value is chosen and a decision is made.

11. Reference
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12. THANK YOU….!