World models have garnered substantial interest in the AI community. These are internal representations that simulate aspects of the external world, track entities and states, capture causal relationships, and enable prediction of consequences. This contrasts with representations based solely on statistical correlations. A key motivation behind this research direction is that humans possess such mental world models, and finding evidence of similar representations in AI models might indicate that these models "understand" the world in a human-like way. In this paper, we use case studies from the philosophy of science literature to critically examine whether the world model framework adequately characterizes human-level understanding. We focus on specific philosophical analyses where the distinction between world model capabilities and human understanding is most pronounced. While these represent particular views of understanding rather than universal definitions, they help us explore the limits of world models.

翻译：世界模型在人工智能领域引起了广泛关注。这些内部表征能够模拟外部世界的某些方面，追踪实体与状态，捕捉因果关系，并支持对后果的预测。这与仅基于统计相关性的表征形成鲜明对比。该研究方向的核心动机在于：人类拥有此类心理世界模型，若在人工智能模型中发现类似表征，可能意味着这些模型以类人的方式“理解”世界。本文借助科学哲学文献中的案例研究，批判性地审视世界模型框架是否足以刻画人类水平的理解。我们聚焦于哲学分析中世界模型能力与人类理解差异最为显著的特定场景。尽管这些案例代表了对理解的特定视角而非普适定义，但它们有助于我们探索世界模型的局限性。