MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework
This work addresses the problem of accurate social simulation for researchers and policymakers, offering a scalable foundation, though it is incremental as it builds on existing LLM methods by incorporating mean field theory.
The paper tackled the challenge of achieving quantitative alignment with real-world data in simulating collective decision-making using large language models (LLMs), and the result was the MF-LLM framework, which reduced KL divergence to human population distributions by 47% compared to baselines.
Simulating collective decision-making involves more than aggregating individual behaviors; it emerges from dynamic interactions among individuals. While large language models (LLMs) offer strong potential for social simulation, achieving quantitative alignment with real-world data remains a key challenge. To bridge this gap, we propose the Mean-Field LLM (MF-LLM) framework, the first to incorporate mean field theory into LLM-based social simulation. MF-LLM models bidirectional interactions between individuals and the population through an iterative process, generating population signals to guide individual decisions, which in turn update the signals. This interplay produces coherent trajectories of collective behavior. To improve alignment with real-world data, we introduce IB-Tune, a novel fine-tuning method inspired by the Information Bottleneck principle, which retains population signals most predictive of future actions while filtering redundant history. Evaluated on a real-world social dataset, MF-LLM reduces KL divergence to human population distributions by 47\% compared to non-mean-field baselines, enabling accurate trend forecasting and effective intervention planning. Generalizing across 7 domains and 4 LLM backbones, MF-LLM provides a scalable, high-fidelity foundation for social simulation.