用最优条件传递熵定义因果关系的高阶定义。

IF 2.4 3区 物理与天体物理 Q1 Mathematics
Jakub Kořenek, Pavel Sanda, Jaroslav Hlinka
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引用次数: 0

摘要

对复杂系统动力学的描述,特别是对系统各元素之间的相互作用结构和因果关系的捕捉,是跨学科研究的核心问题之一。虽然两两因果相互作用的表征是一个相对成熟的领域,已有成熟的理论概念,目前的重点是对其有效估计的技术问题,但事实证明,诸如格兰杰因果关系或传递熵等标准概念可能无法忠实地反映高阶可能的协同作用或相互作用,而这些现象与许多现实世界的复杂系统高度相关。在本文中,我们提出了一种对因果推理的信息论方法的推广和改进,使描述真正的多元因果相互作用成为可能,而不是多重两两因果相互作用,从而从因果网络转向因果超网络。特别是,在保持控制中介变量或共同原因的能力的同时,在纯协同相互作用(如排他性分离)的情况下,它将因果作用归因于多变量因果集,而不是单个输入,从而将其与例如两个加性单变量原因的情况区分开来。我们通过应用说明性的理论例子以及最近报道的采用协同计算的生物神经元动力学的生物物理现实模拟来证明这一概念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Higher order definition of causality by optimally conditioned transfer entropy.

The description of the dynamics of complex systems, in particular the capture of the interaction structure and causal relationships between elements of the system, is one of the central questions of interdisciplinary research. While the characterization of pairwise causal interactions is a relatively ripe field with established theoretical concepts and the current focus is on technical issues of their efficient estimation, it turns out that the standard concepts such as Granger causality or transfer entropy may not faithfully reflect possible synergies or interactions of higher orders, phenomena highly relevant for many real-world complex systems. In this paper, we propose a generalization and refinement of the information-theoretic approach to causal inference, enabling the description of truly multivariate, rather than multiple pairwise, causal interactions, and moving thus from causal networks to causal hypernetworks. In particular, while keeping the ability to control for mediating variables or common causes, in case of purely synergistic interactions such as the exclusive disjunction, it ascribes the causal role to the multivariate causal set but not to individual inputs, distinguishing it thus from the case of, e.g., two additive univariate causes. We demonstrate this concept by application to illustrative theoretical examples as well as a biophysically realistic simulation of biological neuronal dynamics recently reported to employ synergistic computations.

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来源期刊
Physical review. E
Physical review. E 物理-物理:流体与等离子体
CiteScore
4.60
自引率
16.70%
发文量
0
审稿时长
3.3 months
期刊介绍: Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.
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