Decision trees provide a formal structure in which decisions and chance events are linked in sequence from left to right. Decisions, chance events, and end results are represented by nodes and connected by branches. The result is a tree structure with the "root" on the left and various payoffs on the right. Probabilities of events occurring and payoffs for events and decisions are added to each node in the tree. With PrecisionTree, you’ll see the payoff and probability of each possible path through a tree.
PrecisionTree functions may be added to any cell in a spreadsheet and can include arguments that are cell references and expressions - allowing great flexibility in defining decision models. You can also collapse and restore branches to the right of any given node for simplicity and easier navigation through the tree, and insert nodes at any point in a tree. You can even append symmetric subtrees to particular nodes, greatly speeding up the building of large models.
Advanced Features
Bayesian Revision – This lets you to “flip” one or more chance nodes in a model in order to show probabilities calculated using Bayes’ Rule. This is valuable when the probabilities of a model are not available in a directly useful form. For example, you may need to know the probability of an outcome occurring given the results of a particular test. The test’s accuracy may be known, but the only way to determine the probability you seek is to “reverse” a traditional tree using Bayes Rule.
Logic nodes – A special type of node where the optimum branch is selected according to conditions the user defines. A logic node behaves like a decision node, but it selects the branch whose branch logic formula evaluates to TRUE as the logical (optimum) decision.
Reference nodes – Are used to reference a sub-tree. The sub-tree can be on any sheet in the workbook. Use reference nodes to simplify a tree, to reference the same sub-tree many times in a tree, or to build a tree
