Method to Support Complex Decision Analysis
Marco Valtorta (email@example.com), Michael Huhns, Jiangbo Dang, Hrishikesh Goradia, and Jingshan Huang Center for Information Technology TR-CIT05-03 Department of Computer Science and Engineering TR-2005-001 University of South Carolina
Marco Valtorta (et al.) looks into how the analysis of competing hypotheses can be combined with Bayesian networks. The authors break down the article into eight sections. The first section following the introduction compares ACH to other related work but indicate that the related work mostly applies to industrial problems. The related work detailed includes: Porter’s 5 Forces, Win-Loss Analysis, and Scenario Planning.
The body of the research begins with the third section. The third section contains a detailed breakdown on how an individual can utilize analysis of competing hypothesis in a simulated example involving a terrorist attack on the Iran oil industry. The example Hypotheses are H1: Terrorists will bomb the oil refineries in Abadan, H2: Terrorists will bomb the oil pipelines in Abadan, H3: Terrorists will bomb the oil wells in Abadan, H4: Terrorists will bomb the oil facilities in Shiraz, in Shiraz. H5: Terrorists will not launch an attack. The hypotheses are displayed with corresponding evidence in an ACH table.
The fourth section, continuing with the example used with the ACH method, uses Bayesian networks analyze the problem. ACH tables are represented by Valtorta (et al.) via bipartite graphs creating sets and respective nodes for the hypotheses and evidence.
The authors compare ACH and Bayesian Networks in the fifth section and specifically note that evidence can be refined more through BN using probability tables instead of the “diagnosticity” in ACH. The article cites Heuer stating that simple linear additive scoring mechanism to derive a probability for an indicated hypothesis.
Sections 6 and 7 detail how Bayesian networks enhance ACH and how the two models can be integrated. Integrating the two requires addressing three limitations with BN, showing dependency between hypotheses, showing dependency between evidence nodes, and modeling context for hypotheses. The authors enhance dependency and context by making their model more complex through the introduction of intermediate nodes. Utilizing the terrorist attack example, they introduce “Terrorist action” and “threat level” as intermediate nodes linking they hypotheses and evidence.
Overall, I believe the authors did a decent job explaining ACH and the example was relevant to the intelligence community. The explanation of Bayesian networks was somewhat confusing however the use of visuals assisted, particularly in displaying how intermediate variables are utilized when ACH and BN are integrated. The related work section, although interesting, was irrelevant considering they did not attempt to tie the work into the example problem regarding the terrorist attack, although in some instances it may not be possible with the given example. The authors note that their research has not been tested operationally, therefore I don’t know if it adds much to the community. In order to test their research new tools need to be created to generate a BN fragment from an ACH and address inadequacies in the bipartite BN. I think their research could have benefited from a more detailed explanation of the tools required.
Valtorta, M., Huhn, M., Dang, J., Goradia, H., Huang, J. (2005). Extending Heuer’s Analysis of Competing Hypotheses. Center for Information Technology TR-CIT05-03 Department of Computer Science and Engineering TR-2005-001 University of South Carolina. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122.3422&rep=rep1&type=pdf