因果涌现：识别多元数据中因果涌现的信息论方法
Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data
摘要：
涌现的广义概念在各种最具挑战性的开放科学问题中是有用的-然而，很少有人提出什么构成涌现现象的定量理论。本文介绍了多元系统中因果涌现的形式理论，它研究了系统各部分的动力学与宏观特征之间的关系。我们的理论提供了一个向下因果关系的定量定义，并引入了一个补充的紧急行为模式-我们称之为因果脱钩。此外，该理论允许在大型系统中有效计算的实用标准，使我们的框架适用于一系列实际感兴趣的场景。我们在一些案例研究中说明了我们的发现，包括康威的生命游戏，雷诺兹的群集模型，和神经活动通过皮层电图测量。
英文摘要：
The broad concept of emergence is instrumental in various of the most challenging open scientific questions -- yet, few quantitative theories of what constitutes emergent phenomena have been proposed. This article introduces a formal theory of causal emergence in multivariate systems, which studies the relationship between the dynamics of parts of a system and macroscopic features of interest. Our theory provides a quantitative definition of downward causation, and introduces a complementary modality of emergent behaviour -- which we refer to as causal decoupling. Moreover, the theory allows practical criteria that can be efficiently calculated in large systems, making our framework applicable in a range of scenarios of practical interest. We illustrate our findings in a number of case studies, including Conway's Game of Life, Reynolds' flocking model, and neural activity as measured by electrocorticography.
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。
