论文标题
动态目录，动态类别：从深度学习到预测市场
Dynamic Operads, Dynamic Categories: From Deep Learning to Prediction Markets
论文作者
论文摘要
天然有组织的系统适应内部和外部压力，这在抽象层次结构的各个级别都会发生。想清楚地思考这个想法会激发我们的论文，因此在引言中广泛阐述了这个想法，哲学上有利的受众应该可以广泛地使用。在其余部分中，我们转向更加压缩的类别理论。我们定义了动态组织的单体双重类别组织，我们提供了富含或动态的分类结构的定义 - 例如动态类别，攻击和单型类别 - 我们展示了它们如何实例化激励的哲学思想。我们给出了两个动态分类结构的示例：作为动态奥尔特的预测市场和作为动态单体类别的深度学习。
Natural organized systems adapt to internal and external pressures and this happens at all levels of the abstraction hierarchy. Wanting to think clearly about this idea motivates our paper, and so the idea is elaborated extensively in the introduction, which should be broadly accessible to a philosophically-interested audience. In the remaining sections, we turn to more compressed category theory. We define the monoidal double category Org of dynamic organizations, we provide definitions of Org-enriched, or dynamic, categorical structures -- e.g. dynamic categories, operads, and monoidal categories -- and we show how they instantiate the motivating philosophical ideas. We give two examples of dynamic categorical structures: prediction markets as a dynamic operad and deep learning as a dynamic monoidal category.
