发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用LLM驱动的多智能体模型探索气候变化立场的社会临界点，通过监测立场距离与话题模式，发现微小扰动可引发突变，并为现实干预提供方法论启示。
AI 中文摘要
由于应对全球变暖的重大行动需要大规模公众支持，理解气候问题上公众舆论的动态至关重要。尤其值得关注的是社会临界点，这些临界点通过个体行为中微小扰动的规模效应得以显现。基于智能体的模型（ABM）是研究这些问题的有效计算工具，因为它们允许对在现实社会系统中可能不可行的干预措施进行受控且系统的探索。大语言模型（LLMs）已被用于赋予模型智能体以自然语言交流的能力（而非交换预定义消息），以及个性（以叙事自我和情景记忆的形式）。我们利用LLM驱动的ABM来寻找一个微型社会中社会动态的临界点，在该社会中部分讨论涉及气候变化。我们智能体的立场由两个变量定义：对气候行动紧迫性信念的强度以及对现有机构的信任程度。我们通过多轮对话中监测（1）智能体在此二维立场空间中的相互距离，以及（2）由潜在狄利克雷分配（LDA）建模的讨论主题模式，来量化智能体“信念”的转变。我们迄今为止的发现表明，在这一简单模型中，气候变化立场的显著突变确实会发生。我们报告了本研究的若干方法论经验，特别是需要防止LLM偏见干扰对话动态，更普遍地说，在面对此类偏见时维持智能体个性和互动的情景记忆。解决这些问题可能有助于利用ABM衍生的见解来设计针对气候变化及其他重要社会挑战的现实干预措施。
英文摘要
Because significant action to counter global warming requires massive public support, it is important to understand the dynamics of public opinion on climate issues. Of special interest are social tipping points, as revealed by large-scale effects of small perturbations in individual behaviors. Agent-based models (ABM) are an effective computational tool for studying these matters, because they allow controlled and systematic exploration of the effects of interventions that may be infeasible in real-world social systems. Large language models (LLMs) have been used to endow model agents with the ability to communicate in natural language (rather than by exchanging predefined messages), as well as with personality (in the form of a narrative self and episodic memory). We leverage LLM-powered ABM to look for tipping points in the social dynamics of a micro-society in which some of the discussions are about climate change. Our agents' stance was defined by two variables: the strength of conviction about the urgency of climate action and the degree of trust in existing institutions. We quantified shifts in agents' "beliefs" by monitoring, across multiple rounds of conversations, (1) inter-agent distances in this two-dimensional stance space and (2) the patterns of discussion topics as modeled by Latent Dirichlet Allocation (LDA). Our findings to date suggest that significant abrupt changes in climate-change stance do occur in this simple model. We report a number of methodological lessons from this study, notably, the need to prevent LLM biases from interfering with the conversational dynamics and, more generally, to maintain agent personality and episodic memories of interactions in the face of such biases. Resolving these issues may allow for using ABM-derived insights in designing real-life interventions vis-a-vis climate change and other important societal challenges.
