# Decision Theory: Decision Trees

This is part 4 of our series on Decision Theory, previous segments are available in our archives.

Few decisions in life, or in business, stand alone. One decision leads to more decisions which, in turn, lead to even more decisions. When making a decision you need to consider those later decisions if you are to make an optimal choice.

A decision tree is an extension of the decision model we discussed previously, which allows you to understand the relationships and eventual outcomes from connected decisions. The only difference between a decision model and a decision tree is that in a decision tree, the outcome of a choice can be another decision!

As an example, let us revisit our decision about whether to hire a salesperson. Instead of thinking about it as a decision to hire a salesperson, let us think about it as a decision whether to hire a salesperson now or later. This changes the decision into a tree as you can see below:

If we decide not to hire the salesperson now, we open ourselves to the option to hire them later. This has both costs and benefits that you can begin to analyze now that you have the decision tree mapped out.

This simple example captures many of the characteristics of real world decisions and things you should consider when building decision trees:

• Just because there are a large number of decisions, there might only be a small number of possible outcomes. In many real world circumstances there are only a few possible outcomes to any line of decision making and this can greatly simplify your process. By working backwards from the preferred outcomes you can identify the decision paths most likely to help you reach it.
• Depending on the choice we make in the first decision, various other decisions either become available or are lost. This is an important factor to consider in decision making, as some choices may preserve more choices in the future and hence increase the flexibility you’ll have for future decisions.
• Many of the branches of your decision tree will also introduce uncertainty, as some decisions and outcomes will rely on certain probabilities. Here, using expected values is very powerful as you can calculate the expected value of a choice even if the true outcome is many decisions removed!

The larger your decision tree the harder it will get to deal with the growing number of probabilities it may introduce. Tomorrow we’ll review a useful tool to cut through that complexity and make those decisions a little easier.

Quote of the Day: “Ask again later” – One of the possible answers from the Magic 8-Ball, a decision support tool/child’s toy.