Extending Heuer’s Analysis of Competing Hypotheses Method to Support Complex Decision Analysis
Marco Valtorta, Michael Huhns, Jiangbo Dang, Hrishikesh Goradia, and Jingshan Huang of the University of South Carolina used Bayesian Networks to extend the Analysis of Competing Hypotheses. They first explained what ACH is, followed by an explanation of Bayesian Networks. After the initial explanation of each matrix, they used a fictional situation to formulate an ACH matrix into a Bayesian matrix.
Before examining ACH and Bayesian Networks more in-depth, they give a brief explanation of another methodologies: Porter’s 5 Forces, Win-Loss Analysis, and Scenario Planning as possible rivals to the ACH matrix. Porter’s 5 Forces is an intelligence model designed for businesses as a method of examining threats in the industry: threat of entry, threat of substitution, buyer bargaining power, supplier bargaining power, rivalry between current competitors. Win-Loss analysis examines sales from a historical point of view to give feedback and possibilities for future sales. Scenario Planning is more strategic, as it uses multiple possible scenarios as a method of achieving a specific goal.
The researchers explained how ACH aids the intelligence process first by examining what the intelligence process is. Their explanation of the process divided it into three phases: the collection phase, analysis phase, and reporting phase. They went on to explain ACH as a methodology which weighs varying hypotheses using evidence for and against each hypothesis. This method gives reasoning for the validation or invalidation of the conclusions as well as explaining how the analysts arrived at their conclusions.
Bayesian Networks are acyclic, graphical models that shows probabilistic relationships between variables as well as combining data with prior knowledge. The complex nature of the model allows for a more in-depth analysis of ACH. In the Bayesian Network, the hypotheses of ACH are shown as modules connecting to the pieces of evidence.
They examined the limitations of using a Bayesian Network to model ACH hypotheses with the evidence. Basic Bayesian models cannot fully show all the variables without the context of the evidence without using a more complex model. The researchers got around this by adding more nodes to their model that provide the context for the evidence and giving lines between the hypothesis node and the evidence nodes if they were weighted for or against the hypothesis.
While the use of Bayesian Networks to extend ACH into a visual model would be a good way to show hypotheses and their evidence for more visual decision makers, it has not been tested in the field. Furthermore, unless the audience knows how Bayesian networks work, the visual could be more confusing than a normal ACH matrix. A simpler explanation of Bayesian Networks would be useful when presenting it as a method of extending the ACH matrix. On a different note, the examination of rival methodologies i.e. Porter’s 5 Forces etc., was useful for the comparison with ACH, but not necessarily relevant to the paper.
Citation: Valtorta, Marco, Huhns, Michael, Dang Jiangbo, Goradia, Hrishikesh, and Huang, Jingshan. “Extending Heuer’s Analysis of Competing Hypotheses Method To Support Complex Decision Making.” 10 February 2005. University of South Carolina. Accessed 29 August 2017. https://cse.sc.edu/~MGV/reports/TR2005-01.pdf.