The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems

• URL: http://arxiv.org/abs/2502.16565v1
• Date: Sun, 23 Feb 2025 13:12:53 GMT
• Title: The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems
• Authors: Zengqing Wu, Takayuki Ito,
• Abstract summary: We argue that implicit consensus, where agents exchange information yet independently form decisions via in-context learning, can be more effective in dynamic environments.<n>We formalize a consensus-diversity tradeoff, showing conditions where implicit methods outperform explicit ones.<n>We highlight emergent coordination via in-context learning, underscoring the value of preserving diversity for resilient decision-making.
• Score: 1.137572571250676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Consensus formation is pivotal in multi-agent systems (MAS), balancing collective coherence with individual diversity. Conventional LLM-based MAS primarily rely on explicit coordination, e.g., prompts or voting, risking premature homogenization. We argue that implicit consensus, where agents exchange information yet independently form decisions via in-context learning, can be more effective in dynamic environments that require long-horizon adaptability. By retaining partial diversity, systems can better explore novel strategies and cope with external shocks. We formalize a consensus-diversity tradeoff, showing conditions where implicit methods outperform explicit ones. Experiments on three scenarios -- Dynamic Disaster Response, Information Spread and Manipulation, and Dynamic Public-Goods Provision -- confirm partial deviation from group norms boosts exploration, robustness, and performance. We highlight emergent coordination via in-context learning, underscoring the value of preserving diversity for resilient decision-making.

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