In this study, we develop a multiple-generative agent system to simulate community decision-making for the redevelopment of Kendall Square's Volpe building. Drawing on interviews with local stakeholders, our simulations incorporated varying degrees of communication, demographic data, and life values in the agent prompts. The results revealed that communication among agents improved collective reasoning, while the inclusion of demographic and life values led to more distinct opinions. These findings highlight the potential application of AI in understanding complex social interactions and decision-making processes, offering valuable insights for urban planning and community engagement in diverse settings like Kendall Square.

翻译：摘要：本研究构建了一个多生成式智能体系统，用于模拟肯德尔广场沃尔普大楼重建过程中的社区决策过程。基于对当地利益相关者的访谈，我们在智能体提示中融入了不同程度的沟通机制、人口统计数据及生活价值观。实验结果表明：智能体之间的沟通增强了集体推理能力，而引入人口统计学特征与生活价值观则产生了更加差异化的意见。这些发现揭示了人工智能在解析复杂社会互动与决策过程中的潜在应用价值，为肯德尔广场等多元化场景下的城市规划与社区参与提供了重要启示。