# Milan Paluš presents Causality, information and time

On 2018-02-22 16:00:00 at S5, MFF UK Malostranske nam. 25, Prague 1

33. Prague Computer Science Seminar

LECTURE ANNOTATION

Any scientific discipline strives to explain causes of observed phenomena.

Quantitative, mathematical description of causality is possible when studying

phenomena that evolve in time and provide measurable quantities which can be

registered in consecutive instants of time and stored in datasets called time

series. As examples we can mention long-term recordings of air temperature, or

recordings of the electrical activity of the human brain, known as the

electroencephalogram. In this talk we will follow ideas of the father of

cybernetics, Norbert Wiener, and Nobel prize winner Sir C.W.J. Granger. We will

explain how to detect causality using probability distribution functionals from

information theory and the interpretation of causal relations as information

transfer. We will study the information transfer in chaotic systems on the

route

to synchronization. The time and the arrow of time play a natural role in the

definition of causality: the cause precedes the effect. We will investigate

whether this principle is obeyed by chaotic dynamical systems. Another role of

time can be seen in complex systems evolving on multiple time scales. We will

show how to measure the information transfer across time scales. As an

application we will demonstrate a causal influence of climate oscillations with

a period about 7-8 years on the amplitude of the annual temperature cycle and

the inter-annual variability of the mean winter temperature in central Europe.

LECTURER

Milan Paluš studied mathematical physics at the Faculty of Mathematics and

Physics of the Charles University in Prague. At the Prague Psychiatric Centre

he

worked on applications of deterministic chaos in the analysis of brain waves

and

was awarded the CSc. degree (PhD equivalent) at the Czech Academy of Sciences.

Supported by the Fogarty research fellowship he worked as a postdoctoral fellow

at the University of Illinois and the Santa Fe Institute. He was a visiting

scholar at the School of Mathematical Sciences, Queensland University of

Technology, Brisbane, and participated in research programs at the Cambridge

University and the Max Planck Institute for the Physics of Complex Systems in

Dresden. At the Institute of Computer Science he studies complex systems and

their cooperative behaviour with focus on the detection of nonlinearity,

synchronization and causality in time series.

LECTURE ANNOTATION

Any scientific discipline strives to explain causes of observed phenomena.

Quantitative, mathematical description of causality is possible when studying

phenomena that evolve in time and provide measurable quantities which can be

registered in consecutive instants of time and stored in datasets called time

series. As examples we can mention long-term recordings of air temperature, or

recordings of the electrical activity of the human brain, known as the

electroencephalogram. In this talk we will follow ideas of the father of

cybernetics, Norbert Wiener, and Nobel prize winner Sir C.W.J. Granger. We will

explain how to detect causality using probability distribution functionals from

information theory and the interpretation of causal relations as information

transfer. We will study the information transfer in chaotic systems on the

route

to synchronization. The time and the arrow of time play a natural role in the

definition of causality: the cause precedes the effect. We will investigate

whether this principle is obeyed by chaotic dynamical systems. Another role of

time can be seen in complex systems evolving on multiple time scales. We will

show how to measure the information transfer across time scales. As an

application we will demonstrate a causal influence of climate oscillations with

a period about 7-8 years on the amplitude of the annual temperature cycle and

the inter-annual variability of the mean winter temperature in central Europe.

LECTURER

Milan Paluš studied mathematical physics at the Faculty of Mathematics and

Physics of the Charles University in Prague. At the Prague Psychiatric Centre

he

worked on applications of deterministic chaos in the analysis of brain waves

and

was awarded the CSc. degree (PhD equivalent) at the Czech Academy of Sciences.

Supported by the Fogarty research fellowship he worked as a postdoctoral fellow

at the University of Illinois and the Santa Fe Institute. He was a visiting

scholar at the School of Mathematical Sciences, Queensland University of

Technology, Brisbane, and participated in research programs at the Cambridge

University and the Max Planck Institute for the Physics of Complex Systems in

Dresden. At the Institute of Computer Science he studies complex systems and

their cooperative behaviour with focus on the detection of nonlinearity,

synchronization and causality in time series.

External www: http://praguecomputerscience.cz/index.php?l=en&p=33