2025/02/23 by Zengqing Wu, Wu, Zengqing, Takayuki Itō +1 · 1 citation
Decision Sciences · #Artificial Intelligence (cs.AI) #Auction Theory and Applications #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Game Theory and Applications #Multiagent Systems (cs.MA)
paper · pdf · doi:10.48550/arxiv.2502.16565
openalex publication_date 2025/02/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.