A rapidly increasing percentage of the world’s population is connected to the global information environment. At the same time, the information environment is enabling social interactions that are radically changing how and at what rate information spreads. Both nation-states and nonstate actors have increasingly drawn upon this global information environment to promote their beliefs and further related goals.
A simulation of the spread and evolution of online information, if accurate and at-scale, could enable a deeper and more quantitative understanding of adversaries’ use of the global information environment than is currently possible using existing approaches. At present, the U.S. Government employs small teams of experts to speculate how information may spread online. While these activities provide some insight, they take considerable time to orchestrate and execute, the accuracy with which they represent real-world online behavior is unknown, and their scale (in terms of the size and granularity with which populations are represented) is such that they can represent only a fraction of the real world. High-fidelity (i.e., accurate, at-scale) computational simulation of the spread and evolution of online information would support efforts to analyze strategic disinformation campaigns by adversaries, deliver critical information to local populations during disaster relief operations, and could potentially contribute to other critical missions in the online information domain.
The goal of Computational Simulation of Online Social Behavior (SocialSim) is to develop innovative technologies for high-fidelity computational simulation of online social behavior. SocialSim will focus specifically on information spread and evolution. Current computational approaches to social and behavioral simulation are limited in this regard. Top-down simulation approaches focus on the dynamics of a population as a whole, and model behavioral phenomena by assuming uniform or mostly-uniform behavior across that population. Such methods can easily scale to simulate massive populations, but can be inaccurate if there are specific, distinct variations in the characteristics of the population. In contrast, bottom-up simulation approaches treat population dynamics as an emergent property of the activities and interactions taking place within a diverse population. While such approaches can enable more accurate simulation of information spread, they do not readily scale to represent large populations. SocialSim aims to develop novel approaches to address these challenges.
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