Causality, Dynamical Systems and the Arrow of Time

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00491998" target="_blank" >RIV/67985807:_____/18:00491998 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1063/1.5019944" target="_blank" >http://dx.doi.org/10.1063/1.5019944</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/1.5019944" target="_blank" >10.1063/1.5019944</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Causality, Dynamical Systems and the Arrow of Time

Popis výsledku v původním jazyce

Using several methods for detection of causality in time series, we show in a numerical study that coupled chaotic dynamical systems violate the first principle of Granger causality that the cause precedes the effect. While such a violation can be observed in formal applications of time series analysis methods, it cannot occur in nature, due to the relation between entropy production and temporal irreversibility. The obtained knowledge, however, can help to understand the type of causal relations observed in experimental data, namely, it can help to distinguish linear transfer of time-delayed signals from nonlinear interactions. We illustrate these findings in causality detected in experimental time series from the climate system and mammalian cardio-respiratory interactions. Any scientific discipline strives to explain causes of observed phenomena. Studying phenomena evolving in time and providing measurable quantities which can be registered in consecutive instants of time and stored in datasets called time series brings researchers a possibility to apply modern mathematical methods which can detect possible causal relations between different datasets. Methods based on the so-called Granger causality have been applied in diverse scientific fields from economics and finance, through Earth and climate sciences to research trying to understand the human brain. Chaotic dynamical systems are mathematical models reflecting very complicated behaviour. Recently, cooperative phenomena have been observed in coupled chaotic systems due to their ability to synchronize. On the way to synchronization, the question which system influences other systems emerges. To answer this question, research works successfully applied the Granger causality methods. In this study, we demonstrate that chaotic dynamical systems do not respect the principle of the effect following the cause. We explain, however, that such principle violation cannot occur in nature, only in mathematical models which, on the other hand, can help us to understand the mechanisms behind the experimentally observed causalities.

Název v anglickém jazyce

Causality, Dynamical Systems and the Arrow of Time

Popis výsledku anglicky

Using several methods for detection of causality in time series, we show in a numerical study that coupled chaotic dynamical systems violate the first principle of Granger causality that the cause precedes the effect. While such a violation can be observed in formal applications of time series analysis methods, it cannot occur in nature, due to the relation between entropy production and temporal irreversibility. The obtained knowledge, however, can help to understand the type of causal relations observed in experimental data, namely, it can help to distinguish linear transfer of time-delayed signals from nonlinear interactions. We illustrate these findings in causality detected in experimental time series from the climate system and mammalian cardio-respiratory interactions. Any scientific discipline strives to explain causes of observed phenomena. Studying phenomena evolving in time and providing measurable quantities which can be registered in consecutive instants of time and stored in datasets called time series brings researchers a possibility to apply modern mathematical methods which can detect possible causal relations between different datasets. Methods based on the so-called Granger causality have been applied in diverse scientific fields from economics and finance, through Earth and climate sciences to research trying to understand the human brain. Chaotic dynamical systems are mathematical models reflecting very complicated behaviour. Recently, cooperative phenomena have been observed in coupled chaotic systems due to their ability to synchronize. On the way to synchronization, the question which system influences other systems emerges. To answer this question, research works successfully applied the Granger causality methods. In this study, we demonstrate that chaotic dynamical systems do not respect the principle of the effect following the cause. We explain, however, that such principle violation cannot occur in nature, only in mathematical models which, on the other hand, can help us to understand the mechanisms behind the experimentally observed causalities.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/NV15-33250A" target="_blank" >NV15-33250A: Predikce terapeutické odpovědi u pacientů s depresivním onemocněním pomocí nových metod EEG analýzy.</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Chaos

ISSN

1054-1500

e-ISSN

—

Svazek periodika

28

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

—

Kód UT WoS článku

000440606100024

EID výsledku v databázi Scopus

2-s2.0-85050176391