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.
- Begin from the top or the left-hand side as outcomes may flow from either left-to-right, or from top-to-bottom.
- 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."
- 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.
- 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.
- 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.
- 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.
- The sum of the probabilities of all outcome branches stemming from a single chance node must equal 1.
- When final consequences are identified, use a filled-in circle to represent that consequence.
- Start “pruning” the decision tree by eliminating leaves and branches that are not (or are less) probabilistic.
- 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
- 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
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