Tuesday, March 12, 2013

Decision support system for risk management: A case study

Summary:


Prasanta Kumar Dey uses an analytical hierarchy process (AHP) in conjunction with decision trees to evaluate the risk of a cross-country petroleum pipeline construction project with a quantitative aspect. He states that a traditional and informal approach to project management does not assess risk efficiently and proposes a new method that is more effective through analyzing a particular case.

Dey states that the process in risk management takes three steps: identifying, analyzing and responding to the risk. He takes the approach of first using an analytical hierarchy process, a multi-criteria decision-making methodology, to quantitatively and therefore subjectively measure both the likelihood of a particular risk and its severity. This allows for what he believes to be a more accurate and less subjective decision tree because the factors have been given a quantitative measurement.

He then conducts the decision tree using the possible courses of action and subsequent outcomes including the probability and severity of each. His decision trees focus primarily on monetary risk for the business and the length of time it would take to for that decision to come to fruition. This final outcome then allows the decision-maker to respond to any risks present, knowing their likelihood and severity, and evaluating alternatives.


Critique:

Although this study was conducted evaluating a pipeline construction project, I believe that the methodology can to some extent be applied to the intelligence field. The main theory behind the article is reducing risk for a decision-maker, which is the essential function of decision trees and is what intelligence analysts strive to achieve. The extent to which they can be useful is questionable though. An implicit limitation in the study was the operationalization of risks in the analytical hierarchy process. Dey did not explain how he applied quantities to specific risks, but only said that the numbers were produced through brainstorming sessions with professionals who have been in the field for 15 years. It may be less feasible to objectively quantify the likelihood of a revolt in a foreign country, for instance.

An explicit limitation the author stated was that his methodology and technique is limited to smaller projects. However, he does not state what constitutes a large or a small project and therefore leaves that open to interpretation. It seems as though this methodology would have great applicability for business intelligence because it did prove to decrease monetary risk for the company Dey studied, but may not be as useful for national security intelligence. Although this article is quite dated, Dey took an interesting route in conducting a decision tree after first conducting an AHP to decrease subjectivity in his decision tree which I was curious to evaluate. If this particular dual method could be applied to the intelligence field, it would certainly decrease uncertainty in the formulation of the decision tree and could yield interesting results.



Dey, P. K. (2001). Decision support system for risk management: A case study. Management Decision,39(8), 634-649. Retrieved from http://portal.uni-freiburg.de/empiwifo/lehre-teaching-     1/summer-term-09/materials-seminar-in-risk-management/emeraldinsight-com_dey.pdf

3 comments:

  1. I agree with in you in that Dey’s methodology consists of gaps that need to be explained. Dey’s study seems prone to biases based on how quantities are applied to specific risks. This can risk the validity of his methodology as wells as the results.

    I think decision trees can be a useful tool for intelligence analysis even for national security related intelligence. However, I am skeptical as to the amount of information that can be managed by decision trees. They may not be able to handle a large volume of data without creating complex decision trees that are ineffective. In addition, some decision trees require algorithms to determine probabilities for certain outcomes making decision trees even more complicated. Perhaps, decision trees are better suited for keeping track of recent events where a large volume of data may not be available.

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  2. Ana, I really like how Dey's study explicitly used multiple methodologies to analyze the issue. It makes it easier to apply the process in conjunction with something we have previously studied. This study contains undertones with apply to the Intel field -- specifically the assessment of risk and the mapping of potential outcomes. With intelligence, it is necessary to analyze a number of possibilities; the use of decision trees has the potential to eliminate some bias or the elimination of potential outcomes.

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