  Multiagent social network simulations are an avenue that can bridge the
communication gap between the public and private platforms in order to develop
solutions to a complex array of issues relating to online safety. While there
are significant challenges relating to the scale of multiagent simulations,
efficient learning from observational and interventional data to accurately
model micro and macro-level emergent effects, there are equally promising
opportunities not least with the advent of large language models that provide
an expressive approximation of user behavior. In this position paper, we review
prior art relating to social network simulation, highlighting challenges and
opportunities for future work exploring multiagent security using agent-based
models of social networks
