arXiv:2606.13696v1 Announce Type: cross 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 th
The increasing complexity of urban planning and the growing capabilities of AI agents are converging, necessitating new frameworks for decision-making that account for human biases.
This framework addresses a core challenge in public policy and urban development by using AI agents to simulate and mitigate participation bias, leading to more equitable and effective outcomes.
Traditional public hearing processes, often skewed by self-selected participants, could be augmented or replaced by AI-driven simulations that proactively model and correct for demographic biases in planning.
- · Urban planners
- · Public transport authorities
- · AI ethics researchers
- · Citizens in underserved areas
- · Lobbyist groups relying on traditional public input
- · Inefficient manual public consultation processes
- · Transit planning agencies ignoring participation bias
Improved, more equitable public transport networks designed with reduced participation bias.
Expansion of AI-driven deliberation and governance models to other areas of public policy beyond transit planning.
Increased public trust in AI-assisted governance as mechanisms for fairness become more transparent and demonstrable.
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