Two quite different types of causal effects are given by (i) changes in near future states of a driven system under changes in a current state of a driving system and (ii) changes in statistical characteristics of a driven system dynamics under changes in coupling parameters, e.g., under switching the coupling off. The former can be called transient causal effects and can be estimated from a time series within the well established framework of the Wiener–Granger causality, while the latter represent equilibrium (or stationary) causal effects which are often most interesting but generally inaccessible to estimation from an observed time series recorded at fixed coupling parameters. In this work, relationships between the two kinds of causal effects are found for unidirectionally coupled stochastic linear oscillators depending on their frequencies and damping factors. Approximate closed-form expressions for these relationships are derived. Their limitations and possible extensions are discussed, and their practical applicability to extracting equilibrium causal effects from time series is argued.
Skip Nav Destination
Article navigation
July 2018
Research Article|
July 06 2018
Transient and equilibrium causal effects in coupled oscillators
Dmitry A. Smirnov
Dmitry A. Smirnov
a)
Saratov Branch, V.A. Kotel’nikov Institute of Radio Engineering and Electronics, Russian Academy of Sciences
, 38 Zelyonaya Street, Saratov 410019, Russia
a)Electronic mail: smirnovda@yandex.ru
Search for other works by this author on:
a)Electronic mail: smirnovda@yandex.ru
Chaos 28, 075303 (2018)
Article history
Received:
November 30 2017
Accepted:
March 21 2018
Citation
Dmitry A. Smirnov; Transient and equilibrium causal effects in coupled oscillators. Chaos 1 July 2018; 28 (7): 075303. https://doi.org/10.1063/1.5017821
Download citation file:
Sign in
Don't already have an account? Register
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Pay-Per-View Access
$40.00
Citing articles via
Nonlinear model reduction from equations and data
Cecilia Pagliantini, Shobhit Jain
Sex, ducks, and rock “n” roll: Mathematical model of sexual response
K. B. Blyuss, Y. N. Kyrychko
Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology
Eugene Tan, Shannon Algar, et al.
Related Content
Causality, dynamical systems and the arrow of time
Chaos (July 2018)
Granger causality using Jacobian in neural networks
Chaos (February 2023)
Detecting directional couplings from multivariate flows by the joint distance distribution
Chaos (July 2018)