## Friday, April 3, 2009

### Decision Tree Analysis: Drawing Some of the Uncertainty Out of Decision Making

Decision Tree Analysis: Drawing Some of the Uncertainty Out of Decision Making
by William E. Marsh, PhD

Summary:

This article was written primarily to describe the benefits and how-to's of using decision trees (which Marsh commonly refers to as 'decision analysis') to make decisions in the livestock business - particularly swine veterinary practice. Although Marsh's target audience is obviously for those who either own swines or practice medicine on them, his article draws out the basics information needed to conduct a decision tree analysis; and he does so in a easy-to-understand and practical manner. For the purposes of this blog post, and out of respect of my targeted audience, I will leave out all swine references, examples, and jokes.

Marsh essentially defines a decision tree as a visual representation that logically depicts a time-sequenced flow of events with the purpose of informing a decision maker with the probability of various outcomes. It is a structured approach to making decisions when uncertainty exists that helps us to quantify and "consider the effects of chance on the outcome of a given decision." Marsh bluntly states that, "In using decision analysis, it is important to understand that the objective is not to make a prediction...[but rather, it] uses probabilities...to provide a guide for what should be done."

Steps to conducting a decision tree analysis:
1. Define the problem - what is it that we are trying to make a decision about. This will be represented visually using a rectangle (or box) around the decision to be made. Marsh refers to this as the "decision node."
2. Identify a "mutually exclusive, exhaustive list of all possible courses of action to address the problem." Each course of action should have a "branch" stemming out from the decision node.
3. Create a "chance node" (represented with circles) that represent the possible outcomes of a course of action. Different outcomes should stem out from this chance node.
4. Sometimes branches emanating from decision and chance nodes can lead to other decision nodes - repeat steps 2 & 3 if this occurs.
5. Indicate the associated probability (likelihood) that a particular outcome stemming from a chance node will occur. Probabilities are quantified with a value ranging from zero to 1. Therefore a probability of 0.6 would be the equivalent of a 60% chance. Use your experience and knowledge, as well as any conclusions from literature or other supporting data to assign a probability value.
6. The sum of the probabilities of all outcome branches stemming from a single chance node must equal 1.
Marsh does not clearly identify any cons to conducting a decision tree analysis, however, it is quite obvious that he is a strong proponent of using this technique to strengthen the decisions he makes.

#### 1 comment:

1. This seems to be an example of using DTs in qualitative analysis. That makes it an important example, in my mind.

In addition, I think you will have to come to grips as a group with using this method as an intel method. So far, I think only one paper mentioned it in this context but I am not sure that they really meant it...

Finally, one of the interesting things I noted in some other methods is factoring in a "blindside" possibility. Do any of the papers you have read suggest that, in addition to all of the reasonable COAs that there ought to be a blindside COA for those possibilities that you might not know about but might in fact exist? I thought this is what you were getting with when you were talking about the "chance node" but realize now I was mistaken. Still the possibility of a "blindside" node makes some sense to me.