在即将于新加坡召开的 WWW 2024 会议上，我们将举办主题为《基于大模型智能体的社会模拟仿真》（Simulating Human Society with LLM Agents: City, Social Media, and Economic System）的 Tutorial 报告，欢迎参加！
时间：2024 年 5 月 13 日 下午 1:30-5:00地点：Virgo 3 会议室，Resorts World Convention Centre located at 8 Sentosa Gateway, Singapore 本次Tutorial报告围绕基于大模型智能体（LLM Agent）的社会模拟，阐述基于 Agent 模拟的必要性，探讨大模型 Agent 用于社会模拟的优势，介绍大模型 Agent 社会模拟的最新研究，覆盖社会系统、经济系统、城市系统等，最后对该领域面临的挑战和未来的潜力方向进行讨论。
报告流程
参考文献：[1] Gao et al. "Large language models empowered agent-based modeling and simulation: A survey and perspectives." arXiv preprint arXiv:2312.11970 (2023).[2] Li et al. "Large language model-empowered agents for simulating macroeconomic activities." arXiv preprint arXiv:2310.10436 (2023).[3] Gao et al. "S3: Social-network Simulation System with Large Language Model-Empowered Agents." arXiv preprint arXiv:2307.14984 (2023).[4] Lan et al. "Stance detection with collaborative role-infused llm-based agents." in ICWSM 2024.[5] Shao et al. "Beyond Imitation: Generating Human Mobility from Context-aware Reasoning with Large Language Models." arXiv preprint arXiv:2402.09836 (2024).[6] Xu et al. "Urban generative intelligence (ugi): A foundational platform for agents in embodied city environment." arXiv preprint arXiv:2312.11813 (2023).[7] Wang et al. "Recagent: A novel simulation paradigm for recommender systems." arXiv preprint arXiv:2306.02552 (2023).[8] Wang et al. "A survey on large language model based autonomous agents." Frontiers of Computer Science 18, no. 6 (2024): 1-26.[9] Zhang et al. "A Survey on the Memory Mechanism of Large Language Model based Agents." arXiv preprint arXiv: 2404.13501 (2024).[10] Zhang et al. "On generative agents in recommendation." arXiv preprint arXiv:2310.10108 (2023).
