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

arXiv: https://arxiv.org/abs/2606.13696 · HTML: https://mlsanity.com/paper/2606.13696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 31 May 2026.

## Builds on (most cited first, 23 of 23)

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- [The Effects of Demographic Instructions on LLM Personas](https://mlsanity.com/paper/2505.11795/) (2025)
- [Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning](https://mlsanity.com/paper/2404.05894/) (2024)
- [Synthetic Participatory Planning of Shard Automated Electric Mobility Systems](https://mlsanity.com/paper/2404.12317/) (2024)
- [Addressing the alignment problem in transportation policy making: an LLM approach](https://mlsanity.com/paper/2510.13139/) (2025)
- [AgentSUMO: An Agentic Framework for Interactive Simulation Scenario Generation in SUMO via Large Language Models](https://mlsanity.com/paper/2511.06804/) (2025)
- [LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling](https://mlsanity.com/paper/2511.06260/) (2025)
- [GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior](https://mlsanity.com/paper/2601.16778/) (2026)

## Abstract (arXiv)

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.
