arXiv:2606.13696cs.CYcs.LG2026-06

通过模拟不同参会者组合,发现结构化讨论能缓解出行规划中的参与偏差。

AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning?

论文配图:AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning?
图 1 · 摘自论文原文
  • 用代理模拟不同参会者组合,结合讨论和治理规则控制变量。
  • 代表性的参会组合在公平性和尾部风险上表现更优。
  • 结构化讨论和治理规则可降低对参会者构成的依赖,适合政策设计者。

出行网络设计不仅依赖优化算法,还受公众听证会参与者影响。当前做法仅收集自选参会者的单向意见,导致参与者构成成为未控变量。本文提出AGORA框架,在固定网络、需求与求解器的前提下,通过利益相关者代理、结构化讨论与治理门限系统性地改变会议组成。在两个不同规模的标准基准网络上,我们发现:(i) 虽然总体结果变化不大,但在尾部风险和公平性差异方面,代表性样本仍优于分布倾斜的组合;(ii) 缺乏讨论时,组成变化不会带来任何结果差异,表明讨论是参与者影响结果的关键机制;(iii) 治理门限能压缩跨群体方差而不改变曼德爾网络的平均结果,但穆尔福德0网络显示接受阈值需实例化校准。研究将参与偏差从不可控输入重新定义为流程设计问题:即使无法保证代表性参会,良好的结构化讨论与治理标准也能显著降低结果对参会者构成的依赖。

原文摘要 · Abstract (English)

Transit network design depends not only on the optimization algorithm but also on who shows up to the public hearing. Current practice often collects one-directional comments from self-selected attendees, leaving participant mix as an uncontrolled source of outcome variation. We present AGORA, a framework that holds the network, demand, and solver fixed while systematically varying meeting composition through stakeholder agents, structured deliberation, and governance gates. Across two standard benchmark networks at different scales, we find that (i) aggregate outcomes vary little across compositions, but on tail risk and fairness disparity, representative sampling still tends to outperform skewed compositions; (ii) without deliberation, composition produces no variation at all, showing that deliberation is the mechanism through which who attends affects outcomes; and (iii) governance gates compress cross-profile variance without shifting the average outcome on Mandl, but low acceptance on Mumford0 shows thresholds require instance-specific calibration. These findings reframe participation bias from an uncontrollable input to a process-design problem: even without guaranteed representative attendance, well-structured deliberation and governance criteria can substantially reduce how much outcomes depend on who is in the room.

出行规划参与偏差治理机制多智能体

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