摘要翻译：
我们论证了系统的算法信息量与其潜在动力学密切相关，从而为系统在信息论空间中的运动和在相空间中的控制提供了途径。为此，我们在（1）一个非常大的小图集，(2)许多具有不同拓扑结构的较大网络，(3)来自广泛研究和验证的遗传网络（大肠杆菌）的生物网络，以及来自高质量数据库（哈佛的CellNet）的大量分化(Th17)和分化的人类细胞上进行了实验并验证了结果，结果符合实验验证的生物数据。基于这些结果，我们引入了一个概念框架，一个基于模型的介入演算和一个可重编程测度，用它来从局部和无序的观测中指导、操纵和重建非线性动力系统的动力学。该方法是在一个动态系统中寻找并应用一系列受控干预，以估计当其每个元素受到扰动时，该系统的算法信息量如何受到影响。该方法代表了一种替代数值模拟和统计方法，用于推断因果机制/生成模型和发现第一性原理。以离散动力系统（元胞自动机）的相空间重构和生成规则重构为例，证明了该框架的能力。因此，我们提出了在不了解或不了解系统的实际动力学方程或概率分布的情况下重新编程人工和生命系统的工具，产生了一套广泛适用于因果关系、降维、特征选择和模型生成的通用和无参数的算法。
---
英文标题：
《An Algorithmic Information Calculus for Causal Discovery and
  Reprogramming Systems》
---
作者：
Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng, Szabolcs
  Elias, Angelika Schmidt, Gordon Ball, Jesper Tegn\'er
---
最新提交年份：
2018
---
分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
--
一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
--
一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
--

---
英文摘要：
  We demonstrate that the algorithmic information content of a system is deeply connected to its potential dynamics, thus affording an avenue for moving systems in the information-theoretic space and controlling them in the phase space. To this end we performed experiments and validated the results on (1) a very large set of small graphs, (2) a number of larger networks with different topologies, and (3) biological networks from a widely studied and validated genetic network (e.coli) as well as on a significant number of differentiating (Th17) and differentiated human cells from high quality databases (Harvard's CellNet) with results conforming to experimentally validated biological data. Based on these results we introduce a conceptual framework, a model-based interventional calculus and a reprogrammability measure with which to steer, manipulate, and reconstruct the dynamics of non- linear dynamical systems from partial and disordered observations. The method consists in finding and applying a series of controlled interventions to a dynamical system to estimate how its algorithmic information content is affected when every one of its elements are perturbed. The approach represents an alternative to numerical simulation and statistical approaches for inferring causal mechanistic/generative models and finding first principles. We demonstrate the framework's capabilities by reconstructing the phase space of some discrete dynamical systems (cellular automata) as case study and reconstructing their generating rules. We thus advance tools for reprogramming artificial and living systems without full knowledge or access to the system's actual kinetic equations or probability distributions yielding a suite of universal and parameter-free algorithms of wide applicability ranging from causation, dimension reduction, feature selection and model generation.
---
PDF链接：
https://arxiv.org/pdf/1709.05429