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

Key points

• Developing LLM-supported ABMs to simulate human collective behavior therefore requires navigating configuration sensitivity while identifying the limitations of the current model architecture.1
• In this paper, we introduce the Model Capacity Frontier (MCF), a framework that characterizes the empirical performance attainable by a specified architecture across its admissible configurations.1
• We demonstrate MCF in crowd simulation of university commencement events with approximately 12,000 agents, using empirical movement data on temporal occupancy, spatial distribution, and flow direction.1
• We first characterize the capacity of a conventional crowd model and then evaluate successive LLM-supported architectural extensions.1

Sources (1)

• [1]Model Capacity Frontier: A Development Framework for LLM-Supported Social Simulation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 07:45 PM

  Large language model (LLM) agents are increasingly incorporated into agent-based models (ABMs) on the premise that richer, context-sensitive human behavior can improve empirical fidelity.

  Developing LLM-supported ABMs to simulate human collective behavior therefore requires navigating configuration sensitivity while identifying the limitations of the current model architecture.

Extractive summary: sentences quoted from the sources.