How does agent disagreement improve LLM MAS adaptability?
The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems
This paper explores how independent decision-making in multi-agent systems, even with information sharing, can be more effective than forced consensus, especially in dynamic environments. This "implicit consensus" allows agents to retain diverse perspectives and adapt better to change. For LLM-based multi-agent systems, this means letting agents discuss and learn from each other via in-context learning, but ultimately make their own choices based on their individual interpretations, rather than forcing them to agree on a single action through voting or strict prompts. This approach leads to more robust performance in scenarios tested, including disaster response, misinformation control, and resource allocation, particularly when facing volatility or unexpected events.