Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs.

翻译：准确模拟人类舆论动态对理解极化现象和错误信息传播等社会现象至关重要。然而，当前普遍用于此类模拟的基于智能体的模型（ABMs）往往过度简化人类行为。我们提出一种基于大规模语言模型（LLM）群体进行舆论动态模拟的新方法。研究发现，LLM智能体存在内在的强偏差，倾向于生成准确信息，导致模拟智能体达成与科学事实一致的共识。这种偏差限制了其在理解气候变化等议题中共识抵制现象方面的应用价值。但通过提示工程引入确认偏向后，我们观察到与现有基于智能体的建模及舆论动态研究一致的舆论碎片化现象。这些发现揭示了LLM智能体在该领域的潜力与局限，并为后续研究指明了方向：通过真实世界话语改进LLM，以更精准模拟人类信念的演化过程。