用大模型模拟市民投票，让交通政策更贴近真实民意。
Addressing the alignment problem in transportation policy making: an LLM approach
- 让大模型扮演不同社区居民，参与政策投票决策。
- 通过排序投票机制，模拟出接近真实的集体偏好结果。
- 适合城市规划者和政策研究者参考使用。
交通规划中，不同群体的出行偏好常与基于模型的政策建议不一致，导致执行延迟或失败。本文探讨大型语言模型（LLM）在模拟人类决策与推理方面的潜力，是否有助于缓解这一对齐问题。我们构建了一个多智能体仿真系统，让以GPT-4o和Claude-3.5为代表的LLM作为代表城市各社区居民的代理，参与针对一系列公共交通政策提案的公投。借助链式思维推理，这些代理提供排序选择或批准型偏好，并通过即时淘汰投票（IRV）机制聚合，模拟民主共识。该框架应用于芝加哥与休斯顿两地。结果显示，LLM代理能够近似生成合理的集体偏好并响应本地情境，但亦表现出模型特异性行为偏差，且与优化基准存在轻微差异。这表明，尽管大模型在解决交通决策对齐问题上展现出潜力，其局限性仍需关注。
原文摘要 · Abstract (English)
A key challenge in transportation planning is that the collective preferences of heterogeneous travelers often diverge from the policies produced by model-driven decision tools. This misalignment frequently results in implementation delays or failures. Here, we investigate whether large language models (LLMs), noted for their capabilities in reasoning and simulating human decision-making, can help inform and address this alignment problem. We develop a multi-agent simulation in which LLMs, acting as agents representing residents from different communities in a city, participate in a referendum on a set of transit policy proposals. Using chain-of-thought reasoning, LLM agents provide ranked-choice or approval-based preferences, which are aggregated using instant-runoff voting (IRV) to model democratic consensus. We implement this simulation framework with both GPT-4o and Claude-3.5, and apply it for Chicago and Houston. Our findings suggest that LLM agents are capable of approximating plausible collective preferences and responding to local context, while also displaying model-specific behavioral biases and modest divergences from optimization-based benchmarks. These capabilities underscore both the promise and limitations of LLMs as tools for solving the alignment problem in transportation decision-making.
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