Finding needles in a haystack
Subtitle: a resource allocation methodology to design strategies to detect terrorist weapon development
By: David R. Howell
Contributors: Pardee Rand Graduate School
Series: Dissertation / Rand Pardee Graduate School -- 247; Dissertation (Pardee Rand Graduate School) -- RGSD-247.
Publisher: RAND
Published: 2009
Place of publication: Santa Monica, CA
Language: eng
Format: [electronic resource] :
Number of pages: 105
Subjects: Terrorism; Prevention; Resource allocation; Mathematical models; Bayesian statistical decision theory; Arms control; Military Explosives; Weapons of mass destruction; Military intelligence
Description: This thesis presents a methodology to design strategies for detecting terrorist weapon development and shows how it might be used to detect development of improvised explosive devices and radiological dispersal devices. First, a framework to approach the problem of detection of terrorist weapon development is introduced. Then, weapon pathways are generated, which define the target set of potential evidence the intelligence and law enforcement communities could pursue to discover terrorist weapon development. Finally, Bayesian networks are used to create a logical structure for how potential observations would affect our belief a weapon is being developed. Information entropy measures how much uncertainty is present in a system and can be used to assess the relative information content of potential evidence in the Bayesian networks. Resource allocations can be guided by these information-theoretic measures. The dissertation then shows how these methods might be used to detect terrorist development of improvised explosive devices (IEDs) and radiological dispersal devices (RDDs). This method is an example of how expert judgments made prior to observations can guide collection and analytic resource allocations.
Notes: "This document was submitted as a dissertation in June 2009 in partial fulfillment of the requirements of the doctoral degree in public policy analysis at the Pardee RAND Graduate School. The faculty committee that supervised and approved the dissertation consisted of Gregory F. Treverton (Chair), Lynn E. Davis, David E. Mosher, and Walter L. Perry. Professor Kathryn Blackmond Laskey (George Mason University) was the external reader. Financial support for this dissertation was provided by RAND's National Defense Research Institute"--Cover.

Title from title screen (viewed Aug. 24, 2009).

Thesis (Ph. D.)--RAND Graduate School, 2009.

Includes bibliographical references (p. 100-105).

Mode of access: World Wide Web.

System requirements: Adobe Reader.
Table of contents: title: Introduction; title: A Framework to Detect Terrorist Weapon Development; title: Weapon Pathway Analysis; title: Bayesian Networks; title: Bayesian Network Analysis of Weapon Pathways; title: Conclusions.