How to select high-performing agents for decentralized systems?
Merit-Based Sortition in Decentralized Systems
November 13, 2024
https://arxiv.org/pdf/2411.07302This paper introduces "merit-based sortition," a method for selecting a smaller "active" group from a larger pool of participants in a decentralized system based on their performance (merit). It aims to improve efficiency by limiting active participation while maintaining representativeness and boosting overall performance. The method uses an exponential moving average (EMA) of a quality metric to smooth out performance fluctuations and a percentile-based system to manage the flow between active and inactive participants.
Key points for LLM-based multi-agent systems:
- Dynamic Agent Selection: Merit-based sortition can be applied to select the most performant LLMs for specific tasks at any given time, dynamically optimizing the system's overall intelligence.
- Robustness and Adaptability: The EMA smoothing and percentile system allows the multi-agent system to adapt to changes in individual LLM performance over time.
- Scalability and Efficiency: This method is designed for large decentralized networks and addresses computational limitations by limiting active LLM participation without sacrificing overall performance.
- Fairness and Representation: While prioritizing performance, the system maintains a degree of fairness by giving all LLMs a chance to become active based on improved performance. This is especially relevant in scenarios where diverse perspectives are valued.
- Tunable Performance: The percentile parameter (P) allows developers to fine-tune the balance between performance optimization and participation churn. This allows customization for various applications and LLM capabilities.