Abdul Razak Fatimah, Jensen Henrik Jeldtoft
Complexity & Networks Group and Department of Mathematics, Imperial College London, London, United Kingdom; School of Mathematical Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
Complexity & Networks Group and Department of Mathematics, Imperial College London, London, United Kingdom.
PLoS One. 2014 Jun 23;9(6):e99462. doi: 10.1371/journal.pone.0099462. eCollection 2014.
'Causal' direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of 'causal' direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.
在处理复杂系统时，“因果”方向至关重要。通常可以获得大量时间序列形式的数据，开发能够揭示不同可观测变量之间可能因果关系的方法很重要。在此，我们研究转移熵度量识别嵌入在涌现相干关联中的因果关系的能力。我们首先将转移熵应用于修正的伊辛模型来做到这一点。此外，我们使用一个简单的随机转移模型来测试转移熵作为存在随机波动时“因果”方向度量的可靠性。特别是，我们系统地研究了数据集有限大小的影响。
