信息处理特征可以检测动态系统的行为机制
Information processing features can detect behavioral regimes of dynamical systems
摘要：
在动力系统中，动力单元之间的局部相互作用产生相关性，这些相关性在整个系统中存储和传输，从而产生宏观行为。然而，缺乏一个在微观尺度上量化和研究这一点的框架。在这里，我们提出了一个“信息处理”框架的基础上香农互信息量之间的初始和未来的状态。我们将其应用到256个基本元胞自动机（ECA），这是最简单的可能的动力系统表现出从简单到复杂的行为。我们对ECA的主要发现是，完全可预测性只需要几个功能，并且“信息集成”（协同）功能始终是最具预测性的。最后，我们将形式主义应用于外汇（FX）和利率互换（IRS）时间序列数据，发现2008年金融危机标志着一个突然而持续的制度转变（FX和欧元IRS）类似于临界点行为。相反，美元IRS市场表现出缓慢而稳定的发展，这似乎与该市场是危机背后驱动力的假设一致。我们的工作表明，建议的框架是一个有前途的方式来预测紧急复杂的系统行为的本地信息处理的单位。
英文摘要：
In dynamical systems, local interactions between dynamical units generate correlations which are stored and transmitted throughout the system, generating the macroscopic behavior. However a framework to quantify and study this at the microscopic scale is missing. Here we propose an 'information processing' framework based on Shannon mutual information quantities between the initial and future states. We apply it to the 256 elementary cellular automata (ECA), which are the simplest possible dynamical systems exhibiting behaviors ranging from simple to complex. Our main finding for ECA is that only a few features are needed for full predictability and that the 'information integration' (synergy) feature is always most predictive. Finally we apply the formalism to foreign exchange (FX) and interest-rate swap (IRS) time series data and find that the 2008 financial crisis marks a sudden and sustained regime shift (FX and EUR IRS) resembling tipping point behavior. The USD IRS market exhibits instead a slow and steady progression which appears consistent with the hypothesis that this market is (part of) the driving force behind the crisis. Our work suggests that the proposed framework is a promising way of predicting emergent complex systemic behaviors in terms of the local information processing of units.
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