Proposed is an approach based on collaborative visual sensemaking that is at the convergence of people, visualization, collaboration and analytics. Working at this apex, our sensemaking methodology seeks to leverage the strengths of each of these four components, while overcoming their deficiencies especially those due to multiple, interacting views. The novel approach enables a recursive dialectical sensemaking process that involves"discourse"between multiple participants each holding different perspectives about issues of interest and who are collaborating to better understand the issues guided by argumentation. In this case, the arguments are visualizations. Exploiting these multiple perspectives is critical to gaining insights about complex emergent situations and avoiding unintended consequences from action-effect decision-making in the PMESII/DIME domain. In the user-driven process, hidden contextual information is captured as a by-product of visualization construction and is used to enable the reification of tacit knowledge that implicitly contains interpretations and insights that have not heretofore been captured, exploited and shared. Tesseract Analytics and our subcontractor SRI International is not a team of convenience - the principals have been working together on project teams since mid 1990s on closely related research in the PMESII/DIME domain involving collaborative analytics and visualization.
Benefit: The proposed new capabilities and associated technology have great dual use application potential, unlike many DoD developed technologies. One of the key needs to address in this effort is enhancing user understanding of causality as it pertains to PMESII effects and DIME actions. The underlying premise is that we need to understand the cause so we can influence outcomes. These problems and associated capability gaps fit into a broad category of issues faced by most decision-makers dealing in dynamic high complexity environments. Complexity science is emerging as a dominant paradigm in the 21st century and is revolutionizing how we perceive the domains in which we must operate. Traditional views of cause-effect assume a linearity in which the output of a system is proportional to its input. Leap-ahead innovation is hindered because most current applications use the same linear underlying mathematical models and representations (i.e., linear algebra, systems theory, differential equations, partial differential equations, probability and statistics). Such a reductionist paradigm has dominated thought and is the basis for most of our research and statistical methods. But complexity science says that such Newtonian characteristics are rare in systems composed of diverse, interconnected, adaptive agents as characterized by the PMESII/DIME domain. Reality in such a complex world is dynamic and unpredictable, exhibiting nonlinear patterns. Problems in this domain are also characterized by large volumes of heterogeneous data (big data) where collaborative visual analytics can make sense of these data. We believe that highly complex phenomenon warrants technical approaches that are underpinned by non-conventional mathematics. That said, we recognize that user communities are unaware of this inherent complexity and its technical implications. US Air Force Doctrine Document 3-0 (05 June 2013) takes a strong position in favor of"the effects-based approach to operations"which the proposed capability would support. Additionally, the importance and anticipated benefit of applications such as those proposed has been highlighted in multiple recent Defense Science Board (DSB) studies. They found that enhanced capabilities in big data analysis for robust tracking and defense of WMD is not only relevant to DoD, but is also transformative and underattended (DSB Technology and Innovation Enabler for Superiority in 2030, Oct 2013). Improved information sharing was identified as a vital enabler of effective threat management (Final Report of the Defense Science Board Task Force on Predicting Violent Behavior (14 August 2012). Citing work by MG Flynn, the Report of the DSB Task Force on Defense Intelligence, Counterinsurgency (COIN), Intelligence, Surveillance and Reconnaissance (ISR) Operations (Feb 2011) stated that such technology would aid in the problem of the US remaining unable to answer fundamental questions about the complex environments in which we must operate, underscored by the need to exploit knowledge about the localized contexts of operation. Detailed information about the enemy is emphasized at the expense of the political, economic, and cultural environment. Intelligence shops on the ground lack the resources to collect, analyze, and disseminate the amount of information that flows in on a daily basis. The market for commercial applications of the proposed capability is large. It fits in the category of"actionable analytics,"one of Gartner"s top 10 technology trends in 2013. Gartner"s description is mirrors the SBIR solicitation objective:"To make analytics more pervasive and actionable, business intelligence and analytics professionals must make analytics more transparent, in context, and more embedded in real-time applications accessible by nontraditional analytics users."Recent Forbes experts expect the analytics market to track with"big data,"which has a projected market size of $16.1B in 2014 and is growing 6X faster that the IT market in general.
Keywords: Collaborative sensem
