Are complex systems predictable? Is the butterfly effect real? Here's a seminal 1979 paper by David Ruelle on how fluctuations on the smallest scale propagate to the large, chaotic fluid-scale tmosphere dynamics

https://www.sciencedirect.com/science/article/abs/pii/0375960179906534

There are 2 openings for #PhD positions in my research group mds.univie.ac.at , see tinyurl.com/2p93hzsn and tinyurl.com/bddaf3cf for the announcments. Come to #Vienna to advance the Mathematics of Data Science!

Published exactly 50 years ago.

Continues to be a thought-provoking read.

https://t.co/j0AzAgSje2

Information theory: A foundation for complexity science
https://t.co/lpgm7eHRJ7

School on Information, Noise, and Physics of Life
19-30 Sep 2022, Nis - Serbia

https://indico.ictp.it/event/9826/

We are looking for two postdoctoral fellows in neural data science at the Department of Mathematical Sciences.
https://www.jobbnorge.no/en/available-jobs/job/229369/2-postdoctoral-fellows-in-neural-data-science

#postdoc

Swimming in Complex Fluids
Saverio E. Spagnolie, Patrick T. Underhill
https://arxiv.org/abs/2208.03537

We review the literature on swimming in complex fluids. A classification is proposed by comparing the length and time scales of a swimmer with those of nearby obstacles, interpreted broadly, extending from rigid or soft confining boundaries to molecules which confer the bulk fluid with complex stresses. A third dimension in the classification is the concentration of swimmers, which incorporates fluids whose complexity arises purely by the collective motion of swimming organisms. For each of the eight system classes which we identify we provide a background and describe modern research findings. While some classes have seen a great deal of attention for decades, others remain uncharted waters still open and awaiting exploration.

Complexity matters: the fiftieth anniversary of Philip Anderson’s landmark essay ‘More is Different’.

https://www.nature.com/articles/s41567-022-01734-5

The point of More is Different is that a reductionist approach does not imply a ‘constructionist’ one. One cannot use the laws learned at a certain scale as building blocks to directly explain the emergent properties at higher scales. Reality is a collection of layers of emergence, and all the laws and frameworks needed to understand them share the same universal and fundamental quality. From this perspective, chemistry is not just applied physics, biology is not just applied chemistry — all the way up to sociology, which is not just applied psychology.

کتاب‌هایی برای مردم درباره پیچیدگی به انتخاب پتر
(به همراه جستاری در مورد آثار عامه‌پسند)

این کتاب‌ها برای مردم عادی نوشته شده‌اند و تغییر قابل توجهی در نگاه مردم به انگاره پیچیدگی ایجاد کرده‌اند.

🔗 sitpor.org/2022/08/complexity-science-books
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#سیتپـــــور به خاطر روایتگری در علم

#Postdoc researcher in Network Science (3 years)

The position is for 3 years, in a friendly academic atmosphere, and offers quite a bit of freedom for the candidate. See the details below, or the university job ad page: https://www.universiteitleiden.nl/en/vacancies/2022/kwartaal-3/22-503postdoctoral-researcher-in-network-science-3-years-0.8-1.0-fte

"Northwestern University research team has discovered a ubiquitous property of a complex network and developed a novel computational method that... exploit[s] that property to control the whole network using only local information."
https://techxplore.com/news/2022-07-failures-large-networks-local-focus.html

ICTP-KIAS School on Statistical Physics for Life Sciences

https://indico.ictp.it/event/9836/

Information theory: A foundation for complexity science
Amos Golan and John Harte

https://www.pnas.org/doi/abs/10.1073/pnas.2119089119?af=R

Abstract
Modeling and inference are central to most areas of science and especially to evolving and complex systems. Critically, the information we have is often uncertain and insufficient, resulting in an underdetermined inference problem; multiple inferences, models, and theories are consistent with available information. Information theory (in particular, the maximum information entropy formalism) provides a way to deal with such complexity. It has been applied to numerous problems, within and across many disciplines, over the last few decades. In this perspective, we review the historical development of this procedure, provide an overview of the many applications of maximum entropy and its extensions to complex systems, and discuss in more detail some recent advances in constructing comprehensive theory based on this inference procedure. We also discuss efforts at the frontier of information-theoretic inference: application to complex dynamic systems with time-varying constraints, such as highly disturbed ecosystems or rapidly changing economies.

Fully funded scholarship for #PhD students "with a background in computational social science, digital humanities, and/or data science". Great opportunity to study and do research in our team.

https://t.co/xd60k00RwY

#PhD position in Mathematical Foundations of Large Evolving Networks

https://www.uu.nl/en/organisation/working-at-utrecht-university/jobs/phd-position-in-mathematical-foundations-of-large-evolving-networks-10-fte

So you think you know the slope of your power-law?
Yasser Roudi, Kavli Institute for Systems Neuroscience, Norway

Online Talk | Complex Systems | Aalto University
Tuesday, Aug 30, 2022
11:15 Finland Time (EEST)
Link: bit.ly/3AgmOmr

My aim in this talk is to discuss the potential pitfalls that one faces in such efforts and to promote a Bayesian interpolation framework for this purpose. I assume "power law scaling" is indeed present in the data, and discuss the difficulties and pitfalls in estimating the exponent. I do this both for synthetic and real data. Establishing that a "power law scaling" is indeed present is much more difficult. This talk is based on Davidovich & Roudi, Bayesian interpolation for power laws in neural data analysis, arXiv:2204.08525

Our work about multilayer network science is finally out!

This is something like a short technical book, part of the Cambridge Elements series on structure and dynamics of networks.

Free pdf for 2 weeks here

https://t.co/IbDe0pYR7i

New BCCN Pre-Doc internship program is now open for applications. https://www.bccn-berlin.de/call-for-application-pre-doc-internships.html

We accept four interns per year for a six-month internship - preferably from the global south countries. Internships are paid with 1468.- €/month plus travel.