Beyond integrated information: A taxonomy of information dynamics phenomena
Beyond integrated information: A taxonomy of information dynamics phenomena
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超越综合信息：信息动态现象的分类
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Barrett
中科院分区：
文献类型：
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作者：
P. Mediano;F. Rosas;R. Carhart;A. Seth;A. Barrett
大多数信息动力学和统计因果分析框架依赖于这样一个共同的直觉，即因果交互本质上是成对的--每个原因变量都有一个关联的效果变量，因此可以在它们之间画一个“因果箭头”。然而，将相互依赖描述为有向图的分析无法区分系统内可以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”概念实用化带来了问题。为了克服这一缺陷，我们将部分信息分解和综合信息的概念结合起来，得到了我们所称的综合信息分解，即$\Phi$ID。我们展示了$\Phi$ID是如何为更详细地分析多变量时间序列中的相互依赖关系铺平道路的，并揭示了以前没有报道的信息动力学的集体模式。此外，$\Phi$ID揭示了通常所说的‘整合’实际上是几种异类现象的集合。此外，$Phi$ID可用于制定新的、量身定做的综合信息措施，以及理解和减轻现有措施的局限性。
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 $\Phi$ID. We show how $\Phi$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, $\Phi$ID reveals that what is typically referred to as 'integration' is actually an aggregate of several heterogeneous phenomena. Furthermore, $\Phi$ID can be used to formulate new, tailored measures of integrated information, as well as to understand and alleviate the limitations of existing measures.