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Appendix B: Threat Fusion Models
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This dissertation considers the viability of applying Value of Information (VoI) methods in complex systems for policy analysis, concluding that these methods can be applied, but that different methods are appropriate in different cases. VoI is value to a decisionmaker of the difference information makes for a decision, or what a decisionmaker should pay for information. Compared to present practice, specific value of information techniques can potentially improve policy analysis and decision making. To that end, a methodology for policy applications of VoI is presented that can simplify practical application.
The dissertation applied computational methods for VoI to two case studies in disparate domains: biosurveillance and detection of violent extremists. Each of the case studies outlines how Bayesian methods can be used to evaluate VoI, including a novel formulation for considering additional information sources in the systems. The case studies are used both to consider the relative benefits and drawbacks of these approaches in policy decision making, and to identify potential challenges. Approaches for identifying and avoiding these challenges and for selecting appropriate methods are integrated into the overall methodology, which is presented in the final chapter.
Table of Contents
Chapter One
Introduction
Chapter Two
An Overview of Policy Decisions for VoI Questions
Chapter Three
Threat Fusion Case Study
Chapter Four
Influenza Case Study
Chapter Five
Methodology for Policy Applications of VoI
Appendix A
Mathematical Formulation of VoI Problem Types
Appendix B
Threat Model Bayesian Networks
Appendix C
Stan Influenza Model and Data Files
Research conducted by
This document was submitted as a dissertation in March 2018 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 Paul Davis (Chair), Steven Popper, and Kathryn Laskey.
This publication is part of the RAND Corporation Dissertation series. Pardee RAND dissertations are produced by graduate fellows of the Pardee RAND Graduate School, the world's leading producer of Ph.D.'s in policy analysis. The dissertations are supervised, reviewed, and approved by a Pardee RAND faculty committee overseeing each dissertation.
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