统计暗物质：协同作用无处不在，但难以捕捉

中文摘要 AI 辅助

信息论中的协同作用这一概念，指的是存在于三个或更多变量中、但不存在于其中任何子集中的统计结构。在此，我们利用多变量信息论的最新进展来证明，协同作用在复杂系统中的普遍程度远超以往的认识。特别是，我们表明，随着子组件数量的增加，多体系统往往会变得强烈地由协同作用主导。同时，我们的结果也揭示，常用的统计建模工具严重低估了协同结构。这些发现意味着，协同作用构成了复杂系统中一种普遍存在却往往不可见的信息组成部分。带着一定的诗意许可，我们将这些结果解读为：协同作用是统计学的暗物质——我们知道其存在，但标准工具却无法检测到的相互依赖关系。

英文摘要

The information-theoretic construct of synergy refers to the statistical structure that is contained in three or more variables, but not in any subset of them. Here we leverage recent advances in multivariate information theory to show that synergy is far more prevalent in complex systems than previously thought. In particular, we show that many-body systems tend to become strongly dominated by synergy as the number of subcomponents grows. At the same time, our results also reveal that commonly used tools for statistical modelling severely underestimate synergistic structures. These findings imply that synergy constitutes a prevalent, yet often invisible, informational component of complex systems. With a certain poetic licence, we interpret these results as suggesting that synergy is the dark matter of statistics - interdependencies that we know exist, but standard instruments fail to detect.

发表机构

• Imperial College London（帝国理工学院）
• University of Sussex（萨塞克斯大学）
• University of Oxford（牛津大学）
• Ghent University（根特大学）
• University of Vermont（佛蒙特大学）
• EPFL（洛桑联邦理工学院）
• University College London（伦敦大学学院）

机构由 AI 辅助整理，请以论文原文为准。