The classic studies of causal emergence have revealed that in some Markovian dynamical systems, far stronger causal connections can be found on the higher-level descriptions than the lower-level of the same systems if we coarse-grain the system states in an appropriate way. However, identifying this emergent causality from the data is still a hard problem that has not been solved because the correct coarse-graining strategy can not be found easily. This paper proposes a general machine learning framework called Neural Information Squeezer to automatically extract the effective coarse-graining strategy and the macro-state dynamics, as well as identify causal emergence directly from the time series data. By decomposing a coarse-graining operation into two processes: information conversion and information dropping out, we can not only exactly control the width of the information channel, but also can derive some important properties analytically including the exact expression of the effective information of a macro-dynamics. We also show how our framework can extract the dynamics on different levels and identify causal emergence from the data on several exampled systems.
翻译:典型的因果出现研究显示,在某些马尔科维亚动态系统中,如果我们以适当的方式将同一系统的因果差异化,在较高层次的描述中可以找到远比较低层次的因果联系。然而,从数据中找出这种因果关联仍然是一个难题,由于无法轻易找到正确的因果差异化战略,因此尚未解决。本文提议了一个称为神经信息驱动器的一般性机器学习框架,以自动提取有效粗皮采集战略和宏观状态动态,并直接识别时间序列数据产生的因果。通过将粗皮采集操作分解成两个过程:信息转换和信息流出,我们不仅能够完全控制信息渠道的宽度,而且能够从分析中获取一些重要的属性,包括宏观动力学有效信息的确切表达。我们还展示了我们的框架如何从多个示例系统的数据中提取不同层次的动态并识别因果生成。