People reason about uncertainty with deliberately incomplete models. How do people hampered by different, incomplete views of the world learn from each other? We introduce a model of "model-based inference." Model-based reasoners partition an otherwise hopelessly complex state space into a manageable model. Unless the differences in agents' models are trivial, interactions will often not lead agents to have common beliefs or beliefs near the correct-model belief. If the agents' models have enough in common, then interacting will lead agents to similar beliefs, even if their models also exhibit some bizarre idiosyncrasies and their information is widely dispersed.

中文翻译：

---

在不同的世界观下学习：基于模型的推理

人们用故意不完整的模型来推理不确定性。受到不同，不完整的世界观阻碍的人们如何相互学习？我们介绍一个“基于模型的推断”模型。基于模型的推理器将原本毫无希望的复杂状态空间划分为可管理的模型。除非主体模型之间的差异很小，否则交互作用通常不会导致主体具有共同的信念或接近正确模型信念的信念。如果代理人的模型有足够的共同点，那么即使他们的模型也表现出一些奇怪的特质并且其信息被广泛散布，交互也会使代理人产生相似的信念。