AI 中文总结
多户住宅重建政策设计面临集体行动难题，CoRenew平台基于大语言模型智能体，整合地理人口数据，能模拟多利益相关者谈判，评估政策组合效果，支持多种输入与可视化导出，经案例验证，可用于不同背景政策评估。
AI 中文摘要
集体行动的困难仍是多户住宅重建政策设计的核心挑战。利益相关者会根据谈判环境和他人反应不断调整决策，政策干预及其目标对象会重塑集体结果。事前评估这些适应性反应很难，因现有模拟模型常依赖预定义行为规则。我们展示了CoRenew，一个开源平台，用基于大语言模型的智能体模拟多利益相关者间谈判并评估替代政策组合的效果。该平台整合开源地理和人口数据，能生成合成居民，模拟不同政策设置下的谈判动态并比较政策绩效。它支持数值和语义政策输入，有可视化和结果导出工具。我们根据324名居民的调查反馈和一个真实重建案例的九个月观察谈判过程验证了其行为现实性。凭借模块化和适应性架构，CoRenew可用于评估不同制度和文化背景下的政策。
英文摘要
The difficulty of collective action remains a central challenge in the design of policies for multifamily residential redevelopment. Stakeholders continually adjust their decisions in response to evolving negotiation contexts and the reactions of others, meaning that when a policy intervenes and which stakeholders it targets can substantially reshape collective outcomes. Assessing these adaptive responses ex ante remains difficult because existing simulation models often rely on predefined behavioral rules. Here, we present CoRenew, an open-source platform that uses LLM-based agents to simulate negotiations among multiple stakeholders and evaluate the effects of alternative policy combinations. Integrating open source geographic and demographic data, the platform can generate synthetic residents, simulate negotiation dynamics under alternative policy settings and compares policy performance across competing objectives. It supports both numerical and semantic policy inputs and includes built-in tools for visualization and result export. We validate its behavioral realism against survey responses from 324 residents and a nine-month observed negotiation process from a real redevelopment case. With its modular and adaptable architecture, CoRenew can be used to assess policies across different institutional and cultural contexts.
