Information Flows? A Critique of Transfer Entropies
Information Flows? A Critique of Transfer Entropies
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DOI：
10.1103/physrevlett.116.238701
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发表时间：
2016-06-09
影响因子：
8.6
通讯作者：
Crutchfield, James P.
中科院分区：
文献类型：
--
作者：
James, Ryan G.;Barnett, Nix;Crutchfield, James P.
分析复杂动力学的中心任务是确定系统内信息存储的轨迹和信息流的通信拓扑。在过去的15年中，对后者的诊断已经被转移熵所主导。通过简单的例子，我们表明，它和一个衍生量，因果熵，不，事实上，量化的信息流。与此同时，他们可能高估心流或低估影响力。我们隔离为什么是这种情况下，并提出了几种途径，以替代措施的信息流。我们还解决了一个辅助的后果：网络的扩散作为一个现在常见的大规模系统的理论模型，与使用transferlike熵，硬塞进我们的结构解释的组织和行为的复杂系统的二元关系。因此，这种解释未能包括多元依赖的影响。最终的结果是，复杂系统的许多复杂组织可能无法被发现。
A central task in analyzing complex dynamics is to determine the loci of information storage and the communication topology of information flows within a system. Over the last decade and a half, diagnostics for the latter have come to be dominated by the transfer entropy. Via straightforward examples, we show that it and a derivative quantity, the causation entropy, do not, in fact, quantify the flow of information. At one and the same time they can overestimate flow or underestimate influence. We isolate why this is the case and propose several avenues to alternate measures for information flow. We also address an auxiliary consequence: The proliferation of networks as a now-common theoretical model for large-scale systems, in concert with the use of transferlike entropies, has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems. This interpretation thus fails to include the effects of polyadic dependencies. The net result is that much of the sophisticated organization of complex systems may go undetected.