Home > Open Access Journals > JSS > Vol. 6 > No. 3 (2013)
Author Biography
Mr. Singh holds a Bachelor of Science degree in Mechanical Engineering, a Master of Science degree in Biomedical Engineering from the University of Southern California and a Master of Arts degree (with honors) in Intelligence Studies with a focus on Terrorism Studies from the American Public University. The focus of his thesis was on the strategic, theater and operational characteristics of the Haqqani network. He has previously published his work in the Small War Journal and is currently engaged in the development of quantitative methods for modeling the development and promulgation of insurgency.
DOI
http://dx.doi.org/10.5038/1944-0472.6.3.8
Subject Area Keywords
Intelligence analysis, Intelligence studies/education, Methodology
Abstract
A critical aspect of the role of intelligence, within the context of conflict situations involving national level actors, is the reduction in uncertainty associated with ascertaining information relevant to policy makers. Structured techniques for intelligence analysis seek to reduce this uncertainty by the implementation and use of stepwise methods in which each step within the process is transparent and through which the uncertainty generated by cognitive bias is limited. One such method, which serves as the contextual basis for this study, is the Lockwood Analytic Method for Prediction (LAMP). The focus of the study is the recasting of traditional implementation of this specific structured method for intelligence analysis within a simplified probabilistic framework using basic definitions and Bayes’ theorem. The resultant is shown to one in which the original twelve steps are reduced to four and through which the metrics for uncertainty, focal events and event transposition are inherently encoded.
Recommended Citation
Singh, Jai. "The Lockwood Analytical Method for Prediction within a Probabilistic Framework." Journal of Strategic Security 6, no. 3 (2013)
: 83-99.
DOI: http://dx.doi.org/10.5038/1944-0472.6.3.8
Available at:
https://digitalcommons.usf.edu/jss/vol6/iss3/8