网络系统中的涌现受跨边界路径约束

中文摘要 AI 辅助

涌现，即整体具有而任何部分单独不具有的特征，是复杂系统的核心，但很少被测量以定位其来源。将系统结构性地视为由相互作用部分组成的网络，我们将涌现评估为观察耦合整体所揭示的、超出或抹去对各部分联合观察所得的信息。对于通过逐条路径构建其所见内容的观察，我们证明每个涌现特征都由其自身跨越部分间界面的路径产生，该路径将其追溯到负责的组件和交互。我们表明所有此类跨边界路径的数量限制了总涌现差异，并等于计数路径的测度的涌现。该界限在界面周围局部计算，通过网络示例验证，在神经系统中，这些路径表明中间神经元传递了大部分涌现。我们的工作将涌现与网络系统中可预测、可归因和可设计的路径结构联系起来。

英文摘要

Emergence, features of a whole that no part shows alone, is central to complex systems but rarely measured so as to locate its source. Viewing a system structurally, as a network of interacting parts, we evaluate emergence as what observing the coupled whole reveals beyond, or erases from, the combined observations of the parts. For observations that build what they see route by route, we show that every emergent feature is produced by its own route across the interface between parts, a route that traces it to the components and interactions responsible. We show the number of all such boundary-crossing paths bounds the total emergence discrepancy, and equals the emergence of measures that count routes. The bound is computed locally around the interface, validated through network examples, and in a nervous system the routes show that interneurons relay most of the emergence. Our work associates emergence with path structure in the network system that can be anticipated, attributed, and engineered.

发表机构

• UC San Diego（加州大学圣地亚哥分校）

机构由 AI 辅助整理，请以论文原文为准。