How can I make distributed agents truthfully cooperate?
Ensuring Truthfulness in Distributed Aggregative Optimization
January 16, 2025
https://arxiv.org/pdf/2501.08512This paper addresses the problem of untruthful agents in distributed aggregative optimization, where agents collaborate to minimize a shared objective function that depends on both their individual decisions and the aggregate of all agents' decisions. The authors propose a novel algorithm that uses Laplace noise injection to guarantee η-truthfulness, meaning an agent's potential gain from lying is limited. This is the first such algorithm that works in a fully distributed manner, without a central authority to collect information or enforce truthfulness.
Key points for LLM-based multi-agent systems:
- Decentralized Truthfulness: The algorithm provides a way to incentivize truthfulness in decentralized, multi-agent systems without relying on a central server, making it suitable for peer-to-peer or other decentralized architectures.
- Noise Injection for Privacy & Truthfulness: The use of Laplace noise injection resembles techniques used in differential privacy, offering a potential avenue for integrating privacy-preserving mechanisms into LLM-based multi-agent communication.
- Robustness to Noise: The algorithm is designed to converge accurately even with noise injection, a critical feature for real-world applications where communication may be noisy or unreliable. This suggests potential robustness advantages for noisy LLM outputs in multi-agent settings.
- Trade-off Analysis: The paper analyzes the trade-off between the level of truthfulness and the optimization performance, providing insights into the cost of enforcing truthfulness in terms of convergence speed. This is an important consideration for practical LLM-based multi-agent system design.
- Relevance to Aggregative Tasks: The focus on aggregative optimization directly relates to scenarios where LLMs collaborate on tasks involving combined outputs or shared knowledge, such as collaborative writing or knowledge synthesis.