用大模型打造可对话的社会数字孪生，模拟政策效果
Towards an LLM-powered Social Digital Twinning Platform
- 用大模型驱动的智能体模拟真实社会系统行为
- 支持自然语言交互，实时测试干预措施效果
- 适合政策制定者与社会科学家进行协同决策
我们提出 Social Digital Twinner，一种创新的社会仿真工具，用于探索复杂自适应社会系统中“假如”情景的潜在影响。系统由三部分无缝集成：包含真实世界数据和多维度代表性合成人口的数据基础设施；基于大模型的智能体仿真引擎；支持自然语言交互的用户界面，可与虚拟公民直接对话。该平台实现实时参与，使利益相关方能协作设计、测试并优化干预措施，推动基于数据与证据的社会问题解决。我们以挪威克拉格雷罗地区青少年辍学问题为例，展示了通过自然语言创建并执行专用社会数字孪生的能力。
原文摘要 · Abstract (English)
We present Social Digital Twinner, an innovative social simulation tool for exploring plausible effects of what-if scenarios in complex adaptive social systems. The architecture is composed of three seamlessly integrated parts: a data infrastructure featuring real-world data and a multi-dimensionally representative synthetic population of citizens, an LLM-enabled agent-based simulation engine, and a user interface that enable intuitive, natural language interactions with the simulation engine and the artificial agents (i.e. citizens). Social Digital Twinner facilitates real-time engagement and empowers stakeholders to collaboratively design, test, and refine intervention measures. The approach is promoting a data-driven and evidence-based approach to societal problem-solving. We demonstrate the tool's interactive capabilities by addressing the critical issue of youth school dropouts in Kragero, Norway, showcasing its ability to create and execute a dedicated social digital twin using natural language.
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