Friday, September 15, 2017

Decision Trees for Forecasting

Summary and Critique by Matthew Haines

Jakob Uvila discusses the use of decision trees as a forecasting model, and the evolution of traditional decision trees in business. Uvila starts by outlining the history of decision trees and how they have been in use since the 1950s. He also outlines that the reason decision trees are so useful is that the costs associated with them are low. This allows a company the ability to, at minimum, do a decision tree analysis no matter the costs allocated to strategic planning. Then Uvila begins to delve into his comparative case studies with the goal of showing a decision tree’s ability to combine assessments of judgement with data.
Uvila begins to describe the decision tree analysis that was developed by Honeywell Inc. This analysis is used in evaluating which products the company should invest in based on their projected success in the market compared to the option of doing nothing. Uvila is careful to stress that this process is done in two steps. First a decision tree on each individual prospective product, and then a decision tree the combines those trees accounting for interaction of those prospective products. This second step advances the company’s ability to meet future sales goals based on product projection. Uvila stated that the decision tree analysis revealed that the reason there were oddities in the statistical projection of sales in 1988 was the uncertainty of how many products would be in full production in 1988.
Uvila then highlights some of the problems that decision trees deal with and an example of these challenges in a separate case study. He states that the three problems are:
1. The decision maker must have only the information modelled to the left of the subsequent act and will choose among only those actions specified.
 2. The model following the subsequent act must be identical to that which would be specified at the time of the later decision.
3. The decision maker will choose to maximize expected value at the time of the subsequent act
To showcase these challenges, Uvila outlines the AIL Division of Culter-Hammer inc’s opportunity to buy a patent. Specifically, he examines the fact that if the patent was purchased a massive company effort would need to be launched into investigating the patent itself. To assume this information would be erroneous in the decision tree so Uvila states that
an assessment was made of the chances that different subsequent actions would be taken. This assessment took into account whatever relevant information might be known at the time. The mechanism of the analysis then proceeded as usual, treating the actas-event node as any other event node
            This article is a great tool to define how a decision tree analysis should be done and its limitations. It does not, however, offer much information as to how effective a forecasting tool it is. It would be good to see a more in-depth evaluation of the decision tree analysis, but the article does a good job of highlighting the strengths of the method. Specifically, that the method can be complex and sound in statistical projections, but it can also be simplified to give decision makers an easily explained visualization. This versatility alone makes the method valuable to the analyst.

Source: Ulvila, J. W. (1985). Decision Trees for Forecasting. Journal Of Forecasting, 4(4), 377-385.


  1. Thank you for sharing this. I had never thought about the cost associated with these methodologies, but he raises a good point. A cheap, accurate analysis is a practical solution for many situations.

  2. Decision trees can be very cost-effective for companies. They are practical and efficient when properly used. I like the example provided, when decision trees helped predict whether or not the company's products would do well in certain markets and how the use of decision trees were part of the reason there were oddities in the statistical prediction of sales in 1988.

  3. That was a good synopsis of the history of decision trees in business. I do agree there is not much explanation of how it helps with forecasting.

  4. Matt, good summary. It seems decision trees are highly dependent on previously gathered data. Regardless of their success rate, if the data utilized is incorrect or skewed, it's easy to see how that could throw off a final estimate.

  5. A decision tree is a visual model for decision making which represents consequences, including chance event outcomes, resource costs, and utility. It is also one way to display an algorithm that only contains conditional control statements. Making decision trees are super easy with a decision tree maker with free templates.