Complex Systems Studies

What are the gender differences in scientific productivity and impact? We reconstructed 1.5 million careers with fascinating findings at https://t.co/5VuiWpxXxo. 1. Paradoxically, as the fraction of women has increased in academia, so did the productivity and impact gender gaps. 2. The good news: there are no systematic differences between the annual productivity of male and female scientists! Annual productivity is a key gender-invariant. 3. There is, however, a persistent higher dropout rate for women at all stages of their careers. 4. Once we correct for the different dropout rate, the productivity and the impact differences between female and male scientists are reduced by roughly two-thirds. 5. We also discover a second gender invariant quantity: men and women have equivalent career-wise impact for the same size body of work (total number of publications). The bottom line: https://twitter.com/barabasi/status/1149210323724984321?s=19

This research fascinates me, and there's a bunch of directions to go with it. Feel free to send feedback or questions, and stay tuned for the release of some tutorials / open python code. https://t.co/Til7g7LCEk

It's a question of zoom: what's the right scale to represent brain networks, given what we want from our model? What's the best scale to model economic systems? What counts as a "node" in a genome? They're rich, tough, fun problems. And there's a long way to go.

...which is to say, we find that compressed or coarse-grained or macroscale descriptions of networks often have more *effective information* than the original microscale network (e.g. your raw network data). This noise-minimizing process is known as causal emergence. So what?

Networks are such powerful objects. They've changed how we study complex systems. But I’ve always been struck by how nontrivial the “what is a node?” question can be. We provide a framework for identifying the most informative *scale* to describe interdependencies in a system...

terrific paper on causal emergence: https://t.co/ZNBVvq0dhy Since then, we’ve been building a formalism to study causal emergence in networks. Today we posted our first paper on it https://t.co/Til7g7LCEk

🎞 Taha Yasseri course on "Research Design in Social Data Science" is finally live! Here is a sneak preview! If you like it, enrol to join the next cohort, starting 10 Oct, here: https://t.co/xkiREH7yk3

Calling all quantitative life scientists! Deadline to apply to this postdoc is July 10, 2019! https://t.co/1utmJVxueR

🎞 We are biased but the Internet could help us measure it & possibly fix it. Also, let's save the Internet instead of blaming it for everything! https://t.co/eN9hRTrrhZ

Spectral properties and the accuracy of mean-field approaches for epidemics on correlated networks “comparison between stochastic simulations and mean-field theories of the susceptible-infected-susceptible (SIS) model on correlated networks” https://t.co/h6h6o7KPqy

🖤 Recollecting Mitchell Feigenbaum— a chaos pioneer by SFI External Professor Fred Cooper https://medium.com/@sfiscience/recollecting-mitchell-feigenbaum-a-chaos-pioneer-206a73a91a42 Feigenbaum’s constants are universal ratios … that relate to phenomena with oscillatory (cyclic) behavior, such as swinging pendulums or heart rhythms. The most well-known one, Feigenbaum’s Delta, refers to the spacing between parameter values required to double the cycle’s length, which decreases exponentially by a factor approaching approximately 4.669. Among all my friends, Mitchell was the most unusual and brilliant. He viewed the world through the lens of a scientist. When he walked through the forest he wondered “at what distance do the trees merge and become inseparable?” When he looked at the moon he wondered “why does the moon appear larger when it is on the horizon?” He then needed to develop a theory to explain these phenomena “from scratch.” This led him to study how vision evolved from fish to humans and why optical illusions occur as a result of “mistakes” made by our sensory cognition. When he was asked by Pete Carruthers, “what is the origin of turbulence?” Mitchell looked at the simplest nonlinear system — the logistic map where bifurcations took place. This led to the famous Feigenbaum numbers, which were an essential part in understanding the onset of chaos. Mitchell had a great love of music and again wondered how can one improve on digital technology so that the sound of the onset of a bow string could be captured. His interest in photography led him to write computer codes to undo the mistakes made by existing copying machines so that a perfect image could be printed. I found it fascinating that not only did he ask these questions, which were unusual to me, but then he dropped everything to figure out the answer. This also led to the production of maps with minimal distortion and computer codes for figuring out how to label maps in the clearest fashion. Mitchell was a dear friend and he will be missed.

geoplot looks like a nice tool for plotting geo data in Python. Seaborn but for spatial data ;) You can try there quick start tutorial here: https://t.co/2JjhfsvFBV no install needed

🎞 Is Complexity a Science? Is it a possibly useful new way of engineering? In this video narrated by Maxi San Miguel it will be argued that Complexity is a new way of thinking, necessary for a scientific renaissance that can transform society. 📺 https://t.co/OR41RZjRCd

Modeling cities “short review with a discussion about the possibility of constructing a science of cities” https://t.co/4M5Sga7q4s

David Tong: Lectures on Statistical Field Theory These lecture notes provide a detailed introduction to phase transitions and the renormalisation group, aimed at "Part III" (i.e. masters level) students. The lecture notes come in around 130 pages and can be downloaded below. http://www.damtp.cam.ac.uk/user/tong/sft.html

🔸 Critical Phenomena: field theoretical approach http://www.scholarpedia.org/article/Critical_Phenomena:_field_theoretical_approach

From Random Walks to Random Matrices Jean Zinn-Justin Introduces major topics in modern theoretical physics Features short introductions in self-contained chapters Covers renormalization group, fixed points, universality, continuum limit Authoritative overviews by an experienced author and teacher

Phase Transitions and Renormalization Group Jean Zinn-Justin Oxford Graduate Texts Elementary, authoratative introduction by experienced teacher and author Central topic in theoretical physics today Covers mean-field theory, critical phenomena, renormalization group, continuum limit, perturbative methods Based on many years of teaching experience