Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundamental challenge in social science. Given LLMs' commonsense knowledge and social intelligence, we ask: Can LLMs model the dynamics of social beliefs following social events? In this work, we introduce the concept of the Social World Model (SWM), a general framework designed to capture how social beliefs evolve in response to major events. SWM learns state-transition functions for social beliefs by mining temporal patterns in social data and optimizing the evidence lower bound, without the need for explicit human annotations linking events to belief shifts, or for expensive census data. To evaluate SWM, we introduce a benchmark, SWM-bench, derived from real-world prediction markets, specifically Kalshi and Polymarket. SWM-bench includes over 12k data points for social belief prediction tasks spanning diverse domains such as politics, finance, and cryptocurrency. Our experimental results show that SWM significantly outperforms time-series foundation models, achieving state-of-the-art results on Kalshi data and demonstrating competitive performance on Polymarket data, while offering interpretable insights into the underlying mechanisms of social belief dynamics.

翻译：理解并预测社会信念如何因事件（从政策变化到科学突破）而演变，仍然是社会科学领域的一项基本挑战。鉴于大型语言模型（LLM）具备常识知识和社会智能，我们提出疑问：LLM能否建模社会事件后社会信念的动态变化？在本文中，我们引入社会世界模型（SWM）这一概念，它是一种通用框架，旨在捕捉社会信念如何因重大事件而演变。SWM通过挖掘社会数据中的时间模式并优化证据下界来学习社会信念的状态转移函数，无需依赖将事件与信念转变关联的显式人工标注，也无需昂贵的普查数据。为评估SWM，我们引入了一个基准数据集SWM-bench，该数据集源自真实世界的预测市场（具体为Kalshi和Polymarket）。SWM-bench包含超过12,000个数据点，用于涵盖政治、金融和加密货币等多个领域的社会信念预测任务。实验结果表明，SWM显著优于时间序列基础模型，在Kalshi数据上取得了最先进的结果，在Polymarket数据上展现出具有竞争力的性能，同时为社会信念动态的潜在机制提供了可解释的洞察。