How can I build fair multi-agent systems?
MAFE: Multi-Agent Fair Environments for Decision-Making Systems
February 27, 2025
https://arxiv.org/pdf/2502.18534This paper introduces Multi-Agent Fair Environments (MAFEs) to study long-term fairness in multi-agent AI systems. MAFEs simulate realistic social scenarios (loans, healthcare, education) where multiple AI agents interact, allowing researchers to observe and mitigate biases that emerge over time.
Key points for LLM-based multi-agent systems:
- Sequential decision-making and long-term fairness: MAFEs address the limitations of static fairness metrics by focusing on how biases evolve over a series of decisions, crucial for LLM agents interacting over extended periods.
- Modular social systems: The MAFE framework provides modular, adaptable environments, relevant for testing LLM agents in complex scenarios with multiple interacting actors (e.g., users, businesses, institutions).
- Cooperative and competitive settings: MAFEs support both cooperative (agents working towards shared goals) and competitive scenarios, valuable for developing LLM agents for various applications, including negotiations, resource allocation, and market simulations.
- Flexible reward and fairness metrics: The use of component functions allows customization of reward and fairness criteria, essential for aligning LLM agent behavior with specific application requirements and ethical considerations.
- Heterogeneous agents: MAFEs support agents with different capabilities and information access, mirroring real-world interactions and allowing the study of how LLM agents with varying levels of expertise or access to data can cooperate and compete fairly.
- Testbed for multi-agent RL algorithms: MAFEs are designed to be easily integrated with existing multi-agent reinforcement learning libraries, enabling researchers to develop and test algorithms for training LLM-based agents to achieve long-term fairness in interactive settings.