Model Capacity Frontier: A Development Framework for LLM-Supported Social Simulation

The paper introduces the Model Capacity Frontier (MCF), a framework that maps the empirical performance achievable by a given architecture across its possible configurations, particularly for large language model (LLM)-supported agent-based models (ABMs). Using a crowd simulation of university commencement events with about 12,000 agents, the authors demonstrate how MCF can identify which performance dimensions improve or remain limited as LLM layers are added and refined. The study shows that initial LLM integration enhances flow direction but worsens temporal occupancy and spatial distribution, while subsequent architectural tweaks recover these metrics and preserve flow gains.

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