title: Directed dynamical influence is more detectable with noise.
abstract: Successful identification of directed dynamical influence in complex systems is relevant to significant problems of current interest. Traditional methods based on Granger causality and transfer entropy have issues such as difficulty with nonlinearity and large data requirement. Recently a framework based on nonlinear dynamical analysis was proposed to overcome these difficulties. We find, surprisingly, that noise can counterintuitively enhance the detectability of directed dynamical influence. In fact, intentionally injecting a proper amount of asymmetric noise into the available time series has the unexpected benefit of dramatically increasing confidence in ascertaining the directed dynamical influence in the underlying system. This result is established based on both real data and model time series from nonlinear ecosystems. We develop a physical understanding of the beneficial role of noise in enhancing detection of directed dynamical influence.
authors.affiliation: School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA.|School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China.
authors.forename: Jun-Jie
authors.initials: JJ
authors.lastname: Jiang
authors.affiliation: School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA.|School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China.
authors.forename: Zi-Gang
authors.initials: ZG
authors.lastname: Huang
authors.affiliation: School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China.
authors.forename: Liang
authors.initials: L
authors.lastname: Huang
authors.affiliation: School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USA.
authors.forename: Huan
authors.initials: H
authors.lastname: Liu
authors.affiliation: School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA.|Department of Physics, Arizona State University, Tempe, Arizona 85287, USA.
authors.forename: Ying-Cheng
authors.initials: YC
authors.lastname: Lai
mesh_terms: D015984:Causality; D017753:Ecosystem; D015233:Models, Statistical