Rethinking Social Simulation in Open-Population Systems: From Persistent Agents to Communication Processes

Large language models (LLMs) have enabled social simulation with persistent agents, where fixed computational individuals maintain internal states and interact over time. However, large-scale **social networks** are inherently **open-population**: potential participants are not fixed in advance, users enter and leave dynamically, and only a sparse subset becomes observable through communication at any given time. In such systems, persistent account identities do not provide continuously observable individual-state trajectories, making recursively maintained psychological states insufficiently grounded in empirical observations. We therefore shift the simulation target from persistent individual trajectories to the observable evolution of social communication, modeling who participates, what they communicate, and how these communications shape subsequent system dynamics. We instantiate this formulation with participant activation from a global user pool, collective state transition, and an LLM policy for communication generation, while explicitly separating environmental inputs from endogenous system evolution. Across five Qwen backbones, our framework reduces average KL divergence by **71.2%** over Direct LLM and **35.0%** over MF-LLM, with boundary and counterfactual experiments further validating the proposed modeling boundaries. Our results establish a principled formulation for faithful long-horizon simulation of open-population social systems.