Date of Award

6-2023

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Engineering Physics

First Advisor

Victoria R. Sieck, PhD

Abstract

An all-source intelligence analyst’s primary job is delivering timely, well-sourced assessments on relevant targets based on uncertain and incomplete information. Each assessment includes a likelihood that the assessment is true, and a confidence level based on the uncertainty of the sources used. Quantitative all-source intelligence analysis is not widely implemented despite the acknowledged limitations of qualitative intelligence assessments and the existence of proposed quantitative methods. This is due to the challenge of quantitatively representing uncertainty in text-based intelligence reporting (i.e., HUMINT, OSINT, SIGINT), which limits the effectiveness and usability of previously suggested methods. This research creates a novel framework for quantitatively assessing text-based intelligence source uncertainty by adapting quantitative decision models used in multi-objective decision analysis. This novel model allows analysts to easily identify and mathematically account for the underlying causes of a source’s uncertainty, weight the importance of these causes, and output a single value in between 0 and 1 representing the source’s overall uncertainty. The analyst can then use this numerical output as an input into the previously proposed quantitative intelligence analysis methods. Ultimately, this framework for quantifying source uncertainty facilitates the use of previously proposed methods and creates more traceable and defendable intelligence assessments.

AFIT Designator

AFIT-ENP-MS-23-M-097

Comments

A 12-month embargo was observed.

Approved for public release. PA case number on file.

Download

Share

COinS