Business – Decision Trees
Its YOUR Decision –>
Value x Chance = Expected Value
can get to the root of the problem….
Chapter 54 Hall and Jones, p328
Decision Trees
• Definition: A quantitative method to analyse the different outcomes
of business decisions, and select the highest expected value
• Simple example:
Flip a fair coin – win £1 if heads, lose 50p if tails. Would you take the
bet? How would you write this down?
Fair Coin – heads win £1 / tails lose a £1
Quick probability reminder
Probability (the chance of something happening) can be written:
50% = 0.5 = ½ …..usually decimals are used
Remember probability ALWAYS ADDS UP TO 1….so flip a fair coin once
Probability: 0.5 (Heads) + 0.5 (Tails) = 1
A bigger picture – let’s start with Expected Monetary
Value (EMV) – Hall and Jones p329
A pub chain seeks to decide when to run its happy hour?
Expected
Happy Hour Probability Profit if
Probability Loss if
Monetary
Period
of success successful of failure
unsuccessful Value
3-4pm
0.5
£1,300
0.5
-£200
£550
4-5pm
0.5
£1,700
-£400
5-6pm
0.7
£400
-£1,200
6-7pm
0.6
£1,000
-£800
7-8pm
0.6
£1,100
-£400
Handout
Using the previous table, sketch a tree diagram
Happy Hour Period
Probability of success
Profit if successful
Probability of failure
Loss if unsuccessful
Expected Monetary Value
3-4pm
0.5
£1,300
0.5
-£200
£550
4-5pm
0.5
£1,700
0.5
-£400
£650
5-6pm
0.7
£400
0.3
-£1,200
-£80
6-7pm
0.6
£1,000
0.4
-£800
£280
7-8pm
0.6
£1,100
0.4
-£400
£500
Handout
Answer: P329 Hall and Jones – example
Calculating the
Decision Tree
Advantages of this approach
Problems with this approach?
Homework
Have a go at the following Tree Diagram (Q2 (a)
and (b) p331 Hall & Jones)
Where’s the wally? Look at figure 4 on p332 – the
best result is to choose node C (£2.46m), then
chance node G from Choice node E….but the
figures don’t add up on these nodes…what’s gone
wrong (There is a typo on these nodes – can you
find it?)