因果涌现的神经信息挤压器
Neural Information Squeezer for Causal Emergence
摘要：
因果涌现的经典研究表明，在某些马尔可夫动力系统中，如果我们对系统状态进行适当的粗粒度处理，可以在高层次描述上发现比低层次描述更强的因果联系。然而，从数据中识别这种涌现的因果关系仍然是一个尚未解决的难题，因为很难找到正确的粗粒度策略。本文提出了一个通用的机器学习框架神经信息挤压自动提取有效的粗粒化策略和宏观状态动态，以及识别因果出现直接从时间序列数据。通过将粗粒化操作分解为信息转换和信息丢弃两个过程，不仅可以精确地控制信息通道的宽度，而且可以解析地得到一些重要的性质，包括宏观动力学有效信息的精确表达式.我们还展示了我们的框架如何提取不同层次上的动态，并从几个示例系统的数据中识别因果涌现。
英文摘要：
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.
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