Decision trees are decision science tools typically used to determine probabilities and/or expected values and to illustrate alternative system strategies. They can also be used to plan or document all possible paths through a series of nested decisions. Before creating a decision tree, the designer must understand the algorithm or procedure. The necessary information might be compiled from direct observation, extracted from existing documentation, or derived from the problem definition and/or analysis stages of the system development life cycle. When an algorithm involves more than two or three nested decisions, a decision tree gives a clear and concise picture of the logic. Decision trees can also be used to model nested decisions. Once a decision tree is drawn, probabilities can be associated with each branch and the expected values of the outcomes computed. The systems analyst can take advantage of this idea to improve the efficiency of an algorithm.