Decision Making

by Ian Moore

Decision trees

If there are two courses of action, you should take the third.

Jewish proverb

A decision tree is a visual tool for analysing decisions. In using it, you generate a tree-like graph of decisions and their consequences. In the simplest form of this technique

  • Squares represent decisions
  • Triangles represent end points.

When the graph is completed, you can then add probabilities for each of the individual branches and from the overall probabilities of the end points.

Using the technique

As a simple example, let’s suppose that I decide that I want to travel from my home to a hotel in town A. So let’s draw the options in a decision tree:

We can now add some percentages to reflect either our preferences or an estimate of some factor that we would like to consider (for example cost, estimated likelihood and so on). In this case, I will use personal preferences:

I am assuming here that the train station is close to my home. For ‘walk’ I have added 0 per cent because it is a long distance to town A. The bus takes a long time, but it is quite cheap, so I have given it ten per cent.

Notice that I have also added end points to ‘walk’ and ‘drive’ as they both get me to my destination. ‘Train’ and ‘bus’ however do not get me to my final destination, so I now extend my decision tree by adding more decision points, options and estimates:

We can now see what the final end point percentages are. To do this, we work out, for example, that 50 per cent (the train) of 90 per cent (taxi) is 45 per cent and so on:

So our conclusion should be that we should take the train and a taxi (45 per cent).

Extending the percentages

In this example, I have used personal preferences only. You could also add percentages based on cost (or any other factor): simply use another colour for another percentage on each branch.


This is a very simple example, but even here I have simplified the decision tree. I have not considered, for instance, how I would get from my home to the train station. Decision trees can rapidly become very complex; indeed, this is one of their strengths, as they allow us to consider options that we might normally ignore. Try using a large wall space covered with flip-chart paper (or a large white board) for your decision trees.

If you are doing this as a group exercise and people can’t agree over the percentages, you can weight them.


Before starting your decision tree, decide on the level of detail you wish to consider and the number of percentages you want to take into account. For example, it can get very complicated if you use more that two types of percentage. So, when you have decided on the level of detail that you require, try to stick to this and don’t be dragged off into too much detail. In fact, deciding the detail you should go down to in each decision tree that you do is one of the skills that you will develop by using this technique. In some cases, a very high degree of detail is needed; in others, all but the major option branches can be ignored.

Adding chance events

You can make your decision trees even more elaborate by adding circles, representing chance events. For example:

Making your decision

The percentages at the end points should be all that you need to make a decision. If you feel that this is the wrong decision, you should consider:

  • Whether the percentages you have allocated are correct
  • Whether the level of detail in the branches is too high or too low
  • Whether you are using the correct estimate (for example, your percentages may be ‘increased sales’, when they should be ‘increased profits’).