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Anderson, Sweeney, Williams, Camm, Cochran, Fry, and Ohlmann define a decision tree as something that provides a graphical representation of the decision-making process (p.107). The decision tree is best utilized with business decision models. The decision tree allows us to visualize the decision that is going to give us the greatest return with the least amount of risk. As someone who is constantly deciding between A and B, whether it be groceries, gas, or a utility company I can compare all my choices and clearly see where I am going to get the most from my investment.

According to Quantitative Methods for Business (2016), stimulation is a method that uses repeated random sampling of values to represent uncertainty in a model representing a real system and computes the values of model outputs (p.750). Stimulation models focus more on probability and prediction. Given a set of data what are the chances of something occurring and if so how frequently. The stimulation model can best be used effectively by organizations to prepare for future circumstances.

In my opinion I cannot say which model is more effective because it really depends on the situation and problem at hand. The decision tree may be better for one circumstance and the simulation model may be better for another. However, when it comes to making a decision in regards to what is going to give you the greatest return then I would most definitely use the decision tree. In situations where you are trying to figure out the probability of something happening then I would most definitely use the simulation model.