White Paper: Understanding and Realizing the New GEOINT Vision Through the Use of Analytic Decision Games
The future users of GEOINT, from combatant commands down to dismounted soldiers, will confront new challenges as the battlefield shifts along multiple dimensions. Operating environments will include both densely populated urban settings as well as open, ungoverned spaces. Increasingly mobile adversaries operating in small, semi-autonomous command and control structures intermixed with friendly locals will result in shorter operational windows of opportunity for U.S. troops and compressed timelines for GEOINT analysts. Intelligence data will require greater context, the reuse and repurposing of multi-source data will become commonplace, and increased automation could free up a significant amount of analysts' time to perform more complex tasks and produce high-value assessments. GEOINT analysts will be able to think critically and more deeply about the implications of what they find on an image rather than simply marking objects and features. They will be able to ponder over larger volumes of data from multiple sources using specially developed, highly individualized applications. The difficulty in formulating unambiguous judgments and clear conclusions is not reduced by the introduction of more information; rather, more information often adds complexity and compounds analytic challenges. Therefore, developers of new analytic tools and applications must take cognitive factors into account when addressing the next generation of ISR needs. To be effective, the development of new analytic capabilities for the future GEOINT environment must be driven by community collaboration and joint innovation.
Raytheon is working with its mission partners to incorporate an approach to development based on a more complete understanding of how data is used and how analysis is performed. Guided by recent U.S. government reports and expert studies that evaluated analytic tradecraft, we are developing a series of analytic multipliers that increase the effectiveness of the analytic workforce without growing its size. Key multipliers will allow analysts to reduce the search space, increase "thinking time", and evaluate alternative explanations and predictions while avoiding common errors in judgment and decision making. To evaluate the effectiveness of these multipliers compared to current tools and techniques, we have developed an analytic exercise protocol based on the Pennsylvania State University's Analytic Decision Games and Raytheon's new Analytic Test Bed. Intelligence Community analysts will participate in these exercises, and their analytic process and judgments will be observed and recorded. From these measurements, a statistical analysis will assess the impact of the multipliers in increasing analytic quality.
By studying cognitive biases in this fashion, we will be able to demonstrate the effectiveness of analytic multipliers and their corresponding impact on analytic quality. Our cooperative approach to developing and evaluating analytic improvements will ensure that we achieve our joint vision, and that we can demonstrate solutions that offer high analytic confidence.