Name: Decision Analysis 3: Decision Trees
Uploaded: 2018-03-26T12:37:57.000Z
Duration: 3 min 6 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Welcome to this Decision Analysis tutorial for constructing decision trees.

We will be constructing a basic decision tree.

We will also be making decisions using the expected value approach.

We will be constructing a decision tree using this payoff table,

where payoffs are profits and the probabilities of the states of nature

A square or rectangle node called DECISION NODE

from which decision alternative branches will originate,

from which states of nature or outcome branches will emanate.

The CHANCE NODE is also referred to as an OUTCOME NODE or EVENT NODE

Now let's construct a decision tree for this PAYOFF table.

with branches coming out of the decision node representing decision alternatives.

Next we draw the chance node or outcome node

with respective states of nature or outcomes.

The payoffs are placed at the end of the branches as you can see (70 and -13 for Stocks).

We do the same for Mutual Funds and for Bonds.

Therefore, we really don't need to repeat 20,

Solving the decision tree is also known as

In essence, we are going to calculate the expected

For Stocks, the expected value is calculated as

.4 times 70 + .6 times -13 which is 20.2.

So we just usually write the 20.2 on the chance node for stocks.

We do the same for mutual funds. The expected

value is. 4 times 53 + .6 times -5 which is 18.2.

We also write that on the chance node for mutual funds.

We can only expect a payoff of 20 (for Bonds).

Now comparing the three values: 20.2, 18.2, and 20,

We usually just place that value close to the decision node.

Therefore, the decision is to invest in Stocks.