标题： 多变量信息动力学的简单物理系统参考 显示英文标题

标题： Simple physical systems as a reference for multivariate information dynamics

摘要： 理解一个复杂系统需要捕捉其不同部分之间相互作用所产生的非平凡集体现象。信息论是一个灵活且稳健的框架，用于研究此类行为，其中设计了几种度量来量化和表征系统组件之间的依赖关系。然而，由于这些估计器依赖于观测量的统计分布，因此必须检查信息论度量与系统底层机制结构之间的关系。为此，我们对一个受高斯噪声影响的交互式随机行走者的基本系统进行了信息论分析研究。聚焦于部分信息分解、因果涌现和整合信息，我们的结果帮助我们发展了一些关于它们与系统物理参数关系的直觉。例如，我们观察到解耦系统可以表现出涌现特性，我们认为这可能更好地描述为“统计自主”。总体而言，我们观察到在这个简单场景中，当在微观组件层面而非粗粒化对应物上计算时，信息度量与系统的机制属性更加可靠地一致，并且在与系统内在动力学相当的时间尺度上。此外，我们表明，那些将系统动力学和稳态分布贡献分开的方法（例如，通过因果扰动）可能有助于加强信息论分析的解释。 显示英文摘要

摘要： Understanding a complex system entails capturing the non-trivial collective phenomena that arise from interactions between its different parts. Information theory is a flexible and robust framework to study such behaviours, with several measures designed to quantify and characterise the interdependencies among the system's components. However, since these estimators rely on the statistical distributions of observed quantities, it is crucial to examine the relationships between information-theoretic measures and the system's underlying mechanistic structure. To this end, here we present an information-theoretic analytical investigation of an elementary system of interactive random walkers subject to Gaussian noise. Focusing on partial information decomposition, causal emergence, and integrated information, our results help us develop some intuitions on their relationship with the physical parameters of the system. For instance, we observe that uncoupled systems can exhibit emergent properties, in a way that we suggest may be better described as ''statistically autonomous''. Overall, we observe that in this simple scenario information measures align more reliably with the system's mechanistic properties when calculated at the level of microscopic components, rather than their coarse-grained counterparts, and over timescales comparable with the system's intrinsic dynamics. Moreover, we show that approaches that separate the contributions of the system's dynamics and steady-state distribution (e.g. via causal perturbations) may help strengthen the interpretation of information-theoretic analyses.