(MURI-FY22) LEARNING DYNAMICS AND DETECTING CAUSAL PATHWAYS IN COUPLED ONLINE-OFFLINE SYSTEMS
Abstract
Social instability is increasingly connected to a dynamic interplay between online and offline systems. Such instability is causally complex, multiscale in nature, and is traced to spillover, in both directions, between online and offline domains. Indeed, the complexity of offline and online systems on their own is amplified by the potential that problems or grievances incubated in one domain will spontaneously spread to the other. Threats that arise through coupled online-offline dynamics may reach a critical point faster, jump spatial or community boundaries with little constraint, and thrive on complex beliefs that readily mix fact and fiction. The inherent complexity of coupled online-offline systems motivates our proposed MURI. We will develop new data-driven methods for learning the dynamics of coupled online-offline systems and model-driven approaches for determining when spillover between systems is causally significant. Rapidly detecting patterns and knowing when those patterns are not spurious is central to maintaining agility and readiness in the face of heterogeneous threats.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 20, 2023
- Source ID
- FA95502210380
Entities
People
- P. Brantingham
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of California, Los Angeles