Execution and assessment of agentic influence operations in simulated social networks

Abstract

This article evaluates AI-enabled influence operations in synthetic social networks through controlled simulations of narrative release, amplification, and counter-messaging. We measure exposure and belief change in agentic audiences, showing that amplification maximizes reach, counter-messaging shifts opinions most, and narrative release requires larger attacker footprints.