超越综合信息：信息动力学现象分类
Beyond integrated information: A taxonomy of information dynamics phenomena
摘要：
大多数信息动力学和统计因果分析框架依赖于共同的直觉，即因果相互作用本质上是成对的-每个“原因”变量都有一个相关的“效果”变量，因此可以在它们之间绘制“因果箭头”。然而，将相互依赖性描述为有向图的分析无法区分可以在系统内共存的丰富多样的信息流模式。这反过来又给试图将“动态复杂性”或“综合信息”的概念操作化带来了问题。为了解决这个缺点，我们结合部分信息分解和综合信息的概念，并获得我们所谓的综合信息分解，或$Φ$ ID。我们展示了如何$Φ$ ID铺平了道路，更详细的分析，在多元时间序列的相互依赖关系，并揭示了集体模式的信息动态，以前没有报道。此外，$Φ$ ID揭示了通常被称为“整合”的东西实际上是几种异质现象的集合。此外，$Φ$ ID可用于制定新的、定制的综合信息度量，以及理解和减轻现有度量的局限性。
英文摘要：
Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise -- every 'cause' variable has an associated 'effect' variable, so that a 'causal arrow' can be drawn between them. However, analyses that depict interdependencies as directed graphs fail to discriminate the rich variety of modes of information flow that can coexist within a system. This, in turn, creates problems with attempts to operationalise the concepts of 'dynamical complexity' or `integrated information.' To address this shortcoming, we combine concepts of partial information decomposition and integrated information, and obtain what we call Integrated Information Decomposition, or $Φ$ID. We show how $Φ$ID paves the way for more detailed analyses of interdependencies in multivariate time series, and sheds light on collective modes of information dynamics that have not been reported before. Additionally, $Φ$ID reveals that what is typically referred to as 'integration' is actually an aggregate of several heterogeneous phenomena. Furthermore, $Φ$ID can be used to formulate new, tailored measures of integrated information, as well as to understand and alleviate the limitations of existing measures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
