Supporting Representation Management in Intelligence Analysis through Automated Decision Aids

Abstract

Intelligence analysts—overloaded with complex and disparate data—must incorporate information into cohesive and convincing narratives and explanations. For analysts, data overload can result in developing premature conclusions and limit their ability to effectively conduct comprehensive analyses. Historically, automated decision aids designed to help with these processes have largely failed analysts in managing a fundamental work tradeoff between analytic narrowing and broadening because software tools too often attempt to supplant analyst reasoning, rather than support the iterative process as a whole. Instead of such brittle approaches, previous research suggests analysts would benefit from automation that supports a collaborative partnership throughout the analysis process. The research and development described in this work began with a cognitive analysis of practicing analysts, entailing literature review, interviews, and walkthroughs with a software prototype. Findings from the cognitive analysis were translated into design requirements for decision aids that support representation management across multiple intelligence products. The findings provide insight into under-supported cognitive and collaborative work in modern intelligence analysis with implications for the future design of useful automated decision aids.