Recent research has provided a wealth of evidence highlighting the pivotal role of high-order interdependencies in supporting the information-processing capabilities of distributed complex systems. These findings may suggest that high-order interdependencies constitute a powerful resource that is, however, challenging to harness and can be readily disrupted. In this paper we contest this perspective by demonstrating that high-order interdependencies can not only exhibit robustness to stochastic perturbations, but can in fact be enhanced by them. Using elementary cellular automata as a general testbed, our results unveil the capacity of dynamical noise to enhance the statistical regularities between agents and, intriguingly, even alter the prevailing character of their interdependencies. Furthermore, our results show that these effects are related to the high-order structure of the local rules, which affect the system's susceptibility to noise and characteristic times-scales. These results deepen our understanding of how high-order interdependencies may spontaneously emerge within distributed systems interacting with stochastic environments, thus providing an initial step towards elucidating their origin and function in complex systems like the human brain.

翻译：最近的研究提供了大量证据，凸显了高阶相互依赖在支撑分布式复杂系统信息处理能力中的关键作用。这些发现可能表明，高阶相互依赖构成了一种强大但难以利用且易被破坏的资源。本文通过证明高阶相互依赖不仅对随机扰动具有鲁棒性，甚至能被其增强而挑战了这一观点。以初等元胞自动机作为通用测试平台，我们的结果揭示了动态噪声能够增强主体间的统计规律性，且有趣的是，甚至能改变它们相互依赖的主导特征。此外，我们的结果表明，这些效应与局部规则的高阶结构有关，而后者会影响系统对噪声的敏感性和特征时间尺度。这些结果加深了我们对高阶相互依赖如何在随机环境中相互作用的分布式系统中自发涌现的理解，从而为阐明其在人脑等复杂系统中的起源与功能迈出了第一步。