用大模型模拟法律社会，验证理论并揭示制度对弱势群体的保护效果
Law in Silico: Simulating Legal Society with LLM-Based Agents
- 构建基于大模型的法律代理人框架，模拟立法、审判与执行机制
- 模拟犯罪率与真实数据高度吻合，验证了系统有效性
- 揭示透明且灵活的法律体系更有效保护弱势群体权益
由于现实世界中的法律实验往往成本高昂或难以实施，利用人工智能系统模拟法律社会成为验证和发展法律理论以及支持法律治理的有效替代方案。大型语言模型（LLMs）凭借其世界知识和角色扮演能力，是构建法律社会模拟系统的有力候选。然而，将LLMs用于模拟法律体系的研究仍较为有限。本文提出Law in Silico，一个基于大模型的智能体框架，用于模拟具有个体决策与制度机制（立法、审判、执行）的法律场景。实验通过对比模拟犯罪率与真实世界数据，表明大模型代理能够较好再现宏观层面的犯罪趋势，并提供与现实观察一致的洞见。同时，微观层面的模拟显示，运行良好、透明且具备适应性的法律体系能更有效地保护弱势群体权利。
原文摘要 · Abstract (English)
Since real-world legal experiments are often costly or infeasible, simulating legal societies with Artificial Intelligence (AI) systems provides an effective alternative for verifying and developing legal theory, as well as supporting legal administration. Large Language Models (LLMs), with their world knowledge and role-playing capabilities, are strong candidates to serve as the foundation for legal society simulation. However, the application of LLMs to simulate legal systems remains underexplored. In this work, we introduce Law in Silico, an LLM-based agent framework for simulating legal scenarios with individual decision-making and institutional mechanisms of legislation, adjudication, and enforcement. Our experiments, which compare simulated crime rates with real-world data, demonstrate that LLM-based agents can largely reproduce macro-level crime trends and provide insights that align with real-world observations. At the same time, micro-level simulations reveal that a well-functioning, transparent, and adaptive legal system offers better protection of the rights of vulnerable individuals.
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