Quantifying 'causality' in complex systems: understanding transfer entropy
File(s)
Author(s)
Razak, FA
Jensen, HJ
Type
Journal Article
Abstract
‘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.
Date Issued
2014-06-23
Date Acceptance
2014-04-30
Citation
PLOS One, 2014, 9 (6)
ISSN
1932-6203
Publisher
Public Library of Science
Journal / Book Title
PLOS One
Volume
9
Issue
6
Copyright Statement
© 2014 Abdul Razak, Jensen. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Entropy
Complex systems
Covariance
Markov models
Dynamical systems
Nonlinear dynamics
Probability distribution
Collective animal behaviour
Publication Status
Published
Article Number
e99462