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

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

Abstract: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.

Computers and Society (cs.CY); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)

arXiv:2606.13696 [cs.CY]

(or arXiv:2606.13696v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2606.13696 Focus to learn more arXiv-issued DOI via DataCite

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