How can LLMs balance conflicting stakeholder preferences in decisions?
Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making
February 13, 2025
https://arxiv.org/pdf/2502.08542This paper introduces a framework for multi-stakeholder decision-making that goes beyond simply predicting outcomes. It considers the preferences of multiple actors (e.g., bank, applicant, regulator in a loan scenario) by using reward functions for each actor, then combines these with outcome predictions to recommend actions that balance everyone's interests. It uses compromise functions (e.g., maximizing total reward, ensuring equal reward distribution) to mediate between competing preferences.
Key points for LLM-based multi-agent systems:
- Reward Modeling: LLMs can be used to generate synthetic rewards representing different actor preferences, enabling experimentation with diverse stakeholder perspectives even without real-world data. This also makes it easier to tailor reward functions to highly specific actor profiles.
- Compromise Functions: The framework's concept of compromise functions allows for flexible integration of different LLM-driven agents, each potentially representing a different stakeholder or optimization strategy. These agents can collaborate through the framework to arrive at a balanced decision.
- Explainability: The framework's transparent design can be enhanced by LLMs to generate natural language explanations for the suggested actions, highlighting how different stakeholder interests are being considered and balanced. This is crucial for building trust and understanding in multi-agent systems.
- Scalability: The linear scalability of the framework, coupled with the ability of LLMs to handle complex relationships and model nuance, makes it promising for real-world applications with multiple agents and actions.
- Dynamic Interactions: The framework currently assumes non-adversarial interactions, but future work with LLMs could explore incorporating strategic behavior and adaptation within the decision-making process.