Naturalistic decision-making studies of intelligence analysis have generally focused on information search, collection, and synthesis processes, deemphasizing the initial "problem formulation" phase, in which analysts interpret and contextualize the information request to determine which information to collect. We present the results of two studies focusing on this phase. In the first study, we performed a cognitive task analysis via semistructured interviews with 22 active-duty U.S. Army intelligence analysts to uncover factors that arise in operational environments that complicate problem formulation. The factors discovered (e.g., vague and/or overly narrow intelligence requests) led to a second study probing 6 active-duty U.S. Army intelligence analysts' cognitive strategies with a "think-aloud" protocol as they interpreted and evaluated representative information requests. The study revealed that analysts actively interpret and contextualize an information request. The analysts reframed and broadened the request so that they could respond meaningfully to the underlying intent, then used contextual cues and metainformation to determine the most useful collectors and how effectively the request could be answered in the time allotted. We discuss these results and their implications for both the cognitive modeling of intelligence analysis and the development of training and decision aids for more effective framing and contextualization of information requests.

Current and future Joint Task Force stability and support operations (SASO) require intelligence and civic affairs analysis of the attributes of individuals and groups as well as the complex psychosocial and political relationships among these entities. To support analysis in this domain, we have been developing a tool, the Stability and Support Operations Visualization Aid (SASOVA), that combines visualizations (e.g., social network graphs, geo-referenced displays), hyperlinked navigation, and knowledge-based inferencing capabilities to enable analysts to: (1) rapidly profile individuals, groups, and events; (2) assess their inter-relationships; and (3) generate predictions of likely future behavior.A user evaluation of the SASOVA system was performed using military analysts with extensive SASO experience. Participants utilized the SASOVA system to assess entity characteristics, identify inter-relationships, analyze events, and predict future behavior in a simulated SASO scenario. The results of the evaluation pointed to the value of a multifaceted tool such as SASOVA in increasing speed and accuracy of intelligence analyses. At the same time, the evaluation pointed to the need for additional capabilities to improve observability and traceability of machine agent inferences and assessments, and reduce the potential for fixation effects and premature closure.