Reducing Uncertainty in Effects-Based Operations
Reducing Uncertainty in Effects-Based Operations by Wilburn B. Mclamb, published by BiblioScholar on December 4, 2012, is a focused exploration of the challenges posed by uncertainty in military operations. This 112-page book delves into the concept known as the “fog of war,” emphasizing the importance of intelligence collection in mitigating uncertainty through effective information gathering and analysis.
Readers will find a detailed methodology that utilizes Shannon’s entropy to prioritize and allocate intelligence assets efficiently. The book presents a generalized state model for conducting adversary system-of-systems analysis, which serves as the foundation for entropy calculations and integer programming aimed at maximizing information gain. The content is particularly relevant for those interested in education and teaching within military contexts, as well as the broader implications of strategic operations.
Official synopsis Publisher
Known as the fog of war, uncertainty has been prevalent in the conduct of military operations throughout human history. Intelligence collection efforts are tasked to reduce this uncertainty through the collection of information. Utilizing Shannon’s entropy as a measure of the expected information gain due to an intelligence collection effort, a methodology is developed to prioritize and allocate intelligence assets in an efficient manner. Incorporated in this methodology are target priority and the requirement to reassess dynamic targets. The application area for the methodology is Effects-Based Operations. A generalized state model is developed to conduct adversary system-of-systems analysis. This model forms the basis for the entropy calculations and the resultant integer program to maximize the information gain.
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