Reaching consensus in urban planning is a complex process often hindered by prolonged negotiations, trade-offs, power dynamics, and competing stakeholder interests, resulting in inefficiencies and inequities. Advances in large language models (LLMs), with their increasing capabilities in knowledge transfer, reasoning, and planning, have enabled the development of multi-generative agent systems, offering a promising approach to simulating discussions and interactions among diverse stakeholders on contentious topics. However, applying such systems also carries significant societal and ethical risks, including misrepresentation, privacy concerns, and biases stemming from opinion convergence among agents, hallucinations caused by insufficient or biased prompts, and the inherent limitations of foundation models. To evaluate the influence of these factors, we incorporate varying levels of real-world survey data and demographic detail to test agents' performance under two decision-making value frameworks: altruism-driven and interest-driven, using a real-world urban rezoning challenge. This approach evaluates the influence of demographic factors such as race, gender, and age on collective decision-making in the design of multi-generative agent systems. Our experimental results reveal that integrating demographic and life-value data enhances the diversity and stability of agent outputs. In addition, communication among generated agents improves the quality of collective reasoning. These findings provide a predictive framework for decision-makers to anticipate stakeholder reactions, including concerns, objections, and support. By enabling iterative refinement of proposals before public release, the simulated approach fosters more equitable and cost-effective decisions in urban planning.

翻译：城市规划中的共识达成是一个复杂过程，常因长期谈判、利益权衡、权力动态和利益相关者间的竞争关系而受阻，导致效率低下与公平缺失。随着大语言模型在知识迁移、推理和规划能力方面的持续进步，多生成智能体系统的开发为模拟多元利益相关者在争议性议题上的讨论与互动提供了可行路径。然而，此类系统的应用也伴随着显著的社会与伦理风险，包括：智能体因观点趋同导致的表征偏差、提示信息不足或偏误引发的幻觉问题、基础模型固有的局限性，以及隐私泄露等潜在隐患。为评估这些因素的影响，本研究结合不同层级的现实调查数据与人口统计细节，以实际城市区划调整案例为背景，在利他驱动与利益驱动两种决策价值框架下测试智能体表现。该方法能够评估种族、性别、年龄等人口统计因素对多生成智能体系统设计中集体决策的影响。实验结果表明：整合人口统计与生命价值数据能有效提升智能体输出的多样性与稳定性；同时，生成智能体间的交流可改善集体推理质量。这些发现为决策者预测利益相关者反应（包括担忧、反对与支持意见）提供了预测框架。通过在方案公开发布前进行迭代优化，这种模拟方法有助于在城市规划中形成更公平且更具成本效益的决策。