用十亿智能体模拟全球社会演化，实现高保真与高效并存。
Modeling Earth-Scale Human-Like Societies with One Billion Agents
- 将社会行为建模为状态转移，用大模型驱动代理决策。
- 支持超十亿代理并行仿真，效率提升显著。
- 适合研究大规模社会现象的涌现机制与政策推演。
理解复杂社会现象的动态演化，既需高保真的人类行为建模，又需大规模仿真。传统基于代理的模型（ABMs）受限于简化的行为设定。近期大语言模型（LLMs）使代理具备复杂社会行为，但面临严重扩展挑战。本文提出 Light Society 框架，通过结构化状态转移和 LLM 驱动的仿真操作，统一建模社会过程。结合算法与系统优化，特别是混合模型引擎（融合全量 LLM 与蒸馏代理），实现对超过十亿代理的高效仿真。基于世界价值观调查的真实人口数据，对信任博弈与意见传播在十亿规模下的模拟验证了其高保真度与效率，为研究行星尺度集体行为与假设检验提供了实用工具。
原文摘要 · Abstract (English)
Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations. Traditional agent-based models (ABMs) have been employed to study these dynamics, but are constrained by simplified agent behaviors. Recent advances in large language models (LLMs) enable agents to exhibit sophisticated social behaviors, yet face significant scaling challenges. We present Light Society, an agent-based simulation framework that advances both fronts. Light Society formalizes social processes as structured transitions of agent and environment states, governed by a set of LLM-powered simulation operations. Joint algorithmic and system optimizations, particularly a mixture-of-models engine that combines full LLMs with distilled surrogates, enable Light Society to efficiently simulate societies with over one billion agents. Grounded in real-world demographic profiles from the World Values Survey, simulations of Trust Games and opinion diffusion at up to one billion agents demonstrate Light Society's high fidelity and efficiency in modeling diverse social phenomena, providing researchers with a practical foundation for hypothesis testing and the study of emergent collective behaviors at planetary scale.
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