Monday, March 11, 2013

An Introduction to Decision Tree Analysis


Craig Kirkwood, a member of the Department of Supply Chain Management of Arizona State University, published a primer on the use of decision tree analysis in 2002. The first chapter provides increasingly complex examples of decision trees and the various factors that can effect analysis.

Kirkwood begins by giving a simple example of a decision tree. This example involves the decision of a company on whether or not it should manufacture a temperature sensor, pressure sensor, or neither. The costs differ for the sensors, as does the potential profit. This is the most basic decision tree that he presents, and the examples become increasingly complex as more factors are added.

After the basic set-up of a decision tree is given, Kirkwood adds the first new "node": chance. What went from a relatively simple analysis of costs now include the probability that a product will sell or not. With the addition of this variable, a new concept is introduced: the expected value. Depending on the situation, the best expected value is either the highest number or the lowest number. By finding the expected value, it allows for a decision maker to know the best and worst decision with a quick glance at that number.

A second variable that Kirkwood introduces is dependent uncertainties   Dependent uncertainties further complicate decision trees, as they add a new degree of uncertainty. The example given discusses the uncertainty of a trading company in dealing with minerals from a nation that could face trade sanctions from the United States. This leads to the application of the term 'decision tree rollback', which is calculating the expected values from the endpoint of a decision tree to the root node, or beginning.

Sequential decisions is the last variable that Kirkwood discusses. These further complicate decision trees by adding a second layer (or more) of decisions that must be made after an initial decision has been decided. The example Kirkwood uses is a computer company bidding for a government contract. The company must first decide if it wants to bid for the contract or not, as there is a cost to creating a prototype to enter the bidding process. The company then must decide how much it will bid. Thirdly, the company then has to decide on whether or not to use a new, untested manufacturing process that will either save or lose money.

Kirkwood used diagrams of decision trees throughout the chapter. The most complex example (using all of the variables that he discussed) was presented at the end.
Figure 1: Final Dicission Tree Example 

Kirkwood gives a very good introduction to decision tree analysis. While his explanation of concepts can be vague, the examples he provides makes up for this. He also successfully demonstrated that decision trees can be used for increasingly complex scenarios. Though this is a good introduction to decision trees, there are two main criticisms that should be mentioned.

First, he does not explain sufficiently the formulas used to obtain the expected values that he bases decisions.  on. While he explains the first equation used to determine the expected value, this was done with only one variable (chance). With each new variable he introduces, the equation gets longer. Though there is no fundamental change in the equation, his discussion could have benefited from an explanation of each equation. As it was, it was difficult to remember how the equation he used came about.

Second, while his examples are very good at demonstrating the different variables that he discusses, they all have the same goal: profit. At no point did he discuss the application of decision trees to situations that did not have the endpoint of profit. Admittedly it would be much more difficult to give examples where the goal was something other than profit, but it would be interesting to see decision tree analysis applied to a problem that was not based on profit and costs.


Kirkwood, C.W. (2002). Decision Trees. Decision Tree Primer, 1-18. Retrieved from   


  1. When I was reading both the summary and critique it resonated with me how beneficial decision trees could be as one of many analytical tools used to examine both the current business environment or what the future trends in terms of probability might be in the future. I think that it would be interested to examine different aspects of the business environment that are not directly cost related. A good example could be specific changes in the business environment and how they could both your own company and ones competitors' market-share. The one aspect that I found to be very significant for the business environment was putting in an area for uncertainties within the decision trees. These uncertainties could be anything, potential new technologies, changes with regulations, or larger marco-enviornmental trends that will disrupt the functioning of a corporation.

  2. This article is a good starting point and gave a solid introduction to decision trees. By breaking down each step and gradually adding an element to the model I was able to apply what I learned to other articles about decision trees. The use of visuals was also very useful.

    As mentioned in the critique, it would have been beneficial to include at least one extra intended outcome to compare to the profit motivated outcome. Taking into account the study's intent to provide a foundation of decision trees I would have liked Kirkwood to elaborate on other outcomes, or provide a different data set for further clarification of their capabilities.

  3. The summary you provided of this article helped me understand a more direct production of decision trees than the article I approached. I do agree that more explanation of the equation could be beneficial, though I expect further explanation would have been hard to keep up with without a statistical background.

    I also liked the fact that his examples were profit-based, though I understand your desire for more variance within the article. One thing I did think was strange was that by using profit as the final outcome, it did not allow the author to address the ways or reasons to "prune" decision trees, as any amount of profit is still valuable to a company. I would consider this omission a weakness of the article.