Can diverse opinions improve AI urban planning?
Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes – Insights from Urban Studies
March 18, 2025
https://arxiv.org/pdf/2503.12613This paper proposes "negotiative alignment," a multi-agent AI framework for fairer decision-making in scenarios with diverse stakeholder preferences. It uses urban design evaluations as a case study, finding that averaging opinions obscures minority viewpoints.
Key points for LLM-based multi-agent systems:
- Preserve disagreement: Instead of seeking consensus, the framework treats disagreement as valuable input. This is relevant to LLMs by offering a way to represent and manage conflicting information or preferences generated by different agents or retrieved from external sources.
- Dynamically update preferences: Agent weights and preference distributions are updated iteratively through negotiation. This could be applied to LLMs by dynamically adjusting the importance given to different agent prompts or data sources based on evolving circumstances and feedback.
- Identity preservation: A metric is proposed to ensure that minority preferences aren't erased. LLMs could incorporate similar principles by prioritizing underrepresented viewpoints or fairness constraints during generation and decision-making.
- Negotiation operator: A core component of the framework is a negotiation operator that uses bargaining rules to find solutions. This could be implemented in LLMs by incorporating similar rules or mechanisms to resolve conflicts and generate compromises between competing agent goals.
- Iterative refinement: The iterative nature of the approach allows the system to adapt to changing needs and feedback. LLMs can benefit from similar principles by iteratively refining their outputs or strategies based on ongoing dialogue or feedback from multiple agents.