Summary of Findings: Decision Trees (3 out of 5 stars)

Definition:
A visual representation exploring all possible courses of action and the resulting consequences to aid in the decision making process. Decision trees are comprised of nodes (decisions/consequences), branches (links between nodes), and probabilities. The resulting form resembles a tree.

How to:

1. Begin from the top or the left-hand side as outcomes may flow from either left-to-right, or from top-to-bottom.
2. Define the problem/original decision visually represent this by using a rectangle (or box) around the decision to be made. This original decision is referred to as the "decision node."
3. Identify all possible courses of action that stem from that decision. The courses of action must be mutually exclusive and exhaustive. Each course of action should have a "branch" stemming out from the decision node.
4. Identify "chance nodes" (represented with circles) that represent the possible outcomes of the courses of action. Different outcomes should stem out from this chance node.
5. Sometimes branches emanating from decision and chance nodes can lead to other decision nodes - repeat steps 2 & 3 if this occurs, this will effectively “overgrow” the tree.
6. 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.
7. The sum of the probabilities of all outcome branches stemming from a single chance node must equal 1.
8. When final consequences are identified, use a filled-in circle to represent that consequence.
9. Start “pruning” the decision tree by eliminating leaves and branches that are not (or are less) probabilistic.
   

Strengths:

• Structured, allowing for transparent recognition and interpretation of the constructed model
• Visual representation – good tool for presenters and audiences
• Provides an audit trail for the decision maker
• Applicable to multiple disciplines
• Produces quantifiable estimates
• More organized than a mind map
  

Weaknesses:

• High volume of quantitative data requires high level of mathematics capability
• Risky when choosing the "correct" variable to subdivide data
• Susceptible to "blind spots"
• Assigning probabilities may entail guesswork
• Susceptible to bias
  

Experience:
In order to get a feel for decision tree analysis, the group, while sitting in the bar across from the intel building, decided to conduct a decision tree analysis on whether or not we should do a decision tree experience exercise. This was the general consensus of decision tree analysis based on that exercise.

• It was difficult to assign probabilities without prior data that specifies what the likelihood is
  
• It is subjective and open to analysts' bias
• Can get messy if using pen and paper
• Open to wild cards and cognitive blindspots
• Does make the analyst think through the decisions and think outside of the box
• The visual representation makes the choices and consequences clear
• It does produce an estimation