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UID:/NewsandEvents/Archives/2021/newsitem/12803/10
 -June-2021-DIEP-Seminars-Casper-van-Elteren
DTSTAMP:20210603T161516
SUMMARY:DIEP Seminars, Casper van Elteren
ATTENDEE;ROLE=Speaker:Casper van Elteren (UvA)
DTSTART;TZID=Europe/Amsterdam:20210610T110000
LOCATION:Zoom
DESCRIPTION:Understanding dynamical systems is a f
 undamental problem for the 21st century. Despite t
 he prima facie differences and purposes of many re
 al-world networks, previous research shows several
  universal characteristics in networks properties 
 such as the small-world phenomenon, fat-tail degre
 e and feedback loops. This has led to the common b
 ut often implicit assumption that the connectednes
 s of a node in the network is proportional to its 
 dynamic importance. For example in epidemic resear
 ch, high degree nodes or "super-spreaders" are ass
 ociated to dominant epidemic risk and therefore de
 serve special attention. Yet prior research shows 
 that the shared universality in network characteri
 stics is not shared in the dynamic or functional p
 roperties of many real-world systems.  In this tal
 k I will explore the relation between local intera
 ctions and macroscopic properties of a system thro
 ugh the lens of statistical physics and informatio
 n theory. In particular, I will show novel methods
  on determining the so-called driver node in compl
 ex systems, and how tipping points can be studied 
 from an information theoretical perspective. (webs
 ite + slides: https://cvanelteren.github.io/talk/d
 iep2021/)
X-ALT-DESC;FMTTYPE=text/html:\n  <p>Understanding 
 dynamical systems is a fundamental problem for the
  21st century. Despite the <em>prima facie</em> di
 fferences and purposes of many real-world networks
 , previous research shows several universal charac
 teristics in networks properties such as the small
 -world phenomenon, fat-tail degree and feedback lo
 ops. This has led to the common but often implicit
  assumption that the connectedness of a node in th
 e network is proportional to its dynamic importanc
 e. For example in epidemic research, high degree n
 odes or &quot;super-spreaders&quot; are associated
  to dominant epidemic risk and therefore deserve s
 pecial attention. Yet prior research shows that th
 e shared universality in network characteristics i
 s not shared in the dynamic or functional properti
 es of many real-world systems.</p>\n\n  <p>In this
  talk I will explore the relation between local in
 teractions and macroscopic properties of a system 
 through the lens of statistical physics and inform
 ation theory. In particular, I will show novel met
 hods on determining the so-called driver node in c
 omplex systems, and how tipping points can be stud
 ied from an information theoretical perspective. (
 website + slides: <a href="https://cvanelteren.git
 hub.io/talk/diep2021/" target="_blank">https://cva
 nelteren.github.io/talk/diep2021/</a>)</p>\n
URL:https://www.d-iep.org/diep-seminars
CONTACT:Soroush Rafiee Rad at soroush.r.rad at gma
 il.com
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