Milan Paluš presents Causality, information and time

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.