Gray-zone activities―behaviors and/or actions potentially leading to, but below the threshold of armed conflict― executed across actors’ instruments of national power present significant national security and global stability challenges. Successful gray-zone maneuver depends on an actor’s ability to model, then implement effective strategies whilst managing the associated risks and chaos of possibly destabilizing activities. One approach to modeling the evolving nature of global competition and conflict is examining the history of international relations encoded in multiple Conflict and Mediation Event Observations (CAMEO) open-source databases. An exemplar – the Global Database of Events, Language and Tone (GDELT) project is 55TB of events and related data from public news sources accumulated for four decades. This paper examines the feasibility and suitability of this data as a means for decision-makers to explore complex, dynamic gray-zone phenomena, anticipate competing incentives, and assess consequences of choices. There are four facets to this proposed approach. First, it will be shown gray-zone news events fall on a Pareto distribution in terms of the number of mentions each gets in the media. Second, Reflective Thematic Analysis (RTA) is used to extract relevant data from GDELT to train statistical topic models for actor behaviors. Thirdly, results―including newsfeeds and thematic signatures―are generated for two actors over the first four months of 2023. Regarding data quality it will be shown that filtering events with low mention counts can be used for data conditioning, but unfiltered and filtered topics appear statistically similar so that strong filtering is not usually worth the information loss. Finally, we discuss utilizing open-source intelligence (OSINT) for potential model generation for wargaming capabilities. In this, emphasis will be placed on the usefulness of mention counts for cost-benefit-risk analysis to aid decision-making as well as the power of RTA to adapt OSINT to alternate analyst frameworks
Keywords
BIG DATA, DECISION, MODELING
Additional Keywords
Grey Zone