👥 https://theconversation.com/amp/game-theory-can-help-prevent-disease-outbreaks-102934?__twitter_impression=true

🎊 Great summary of Python basics for scientific computing.

http://cs231n.github.io/python-numpy-tutorial/

🌄 Are mountains fractal? Actually there are two scaling regimes (same on earth and venus!)
http://necsi.edu/research/multiscale/earth-venus

Smooth at fine scale, rough at large scales with an exponent satisfying the KPZ (Kardar Parisi Zhang) prediction 0.4.

📝Turn off your e-mail and social media to get more done
Distractions are a fundamental aspect of the modern world, but we don’t have to become hermits to avoid them.

🌐 https://www.nature.com/articles/d41586-018-06213-7

🎲 @ComplexSys

🌀 https://arogozhnikov.github.io/2016/12/19/markov_chain_monte_carlo.html

💡 https://towardsdatascience.com/when-bayes-ockham-and-shannon-come-together-to-define-machine-learning-96422729a1ad

🔖 Triangulating War: Network Structure and the Democratic Peace

Benjamin Campbell, Skyler Cranmer, Bruce Desmarais

🔗 https://arxiv.org/pdf/1809.04141

📌 ABSTRACT
Decades of research has found that democratic dyads rarely exhibit violent tendencies, making the democratic peace arguably the principal finding of Peace Science. However, the democratic peace rests upon a dyadic understanding of conflict. Conflict rarely reflects a purely dyadic phenomena---even if a conflict is not multi-party, multiple states may be engaged in distinct disputes with the same enemy. We postulate a network theory of conflict that treats the democratic peace as a function of the competing interests of mixed-regime dyads and the strategic inefficiencies of fighting with enemies' enemies. Specifically, we find that a state's decision to engage in conflict with a target state is conditioned by the other states in which the target state is in conflict. When accounting for this network effect, we are unable to find support for the democratic peace. This suggests that the major finding of three decades worth of conflict research is spurious.

📝Is together better? Examining scientific collaborations across multiple authors, institutions, and departments

🌐 https://arxiv.org/abs/1809.04093

🎲 @ComplexSys

💡 https://www.nature.com/articles/d41586-018-06185-8

🔅 https://comdig.unam.mx/2018/09/16/physical-foundations-of-biological-complexity/

🎓 The Institute for Advanced Study IAS, Universiteit van Amsterdam, offers 10 outstanding MSc. students the opportunity to join the IAS research community for their MSc. thesis. This will form an inspiring working environment for your #complexity/#interdisciplinary project. Apply by Nov. 1! https://t.co/bWalkVynz1

IFISC offers several Ph.D. positions to start by the end of 2018. Applications are welcome immediately until September 21.
t.co/LFxe2EgggI

🔖 Balance in signed networks

Alec Kirkley, George T. Cantwell, M. E. J. Newman

🔗 Download

📌 ABSTRACT
We consider signed networks in which connections or edges can be either positive (friendship, trust, alliance) or negative (dislike, distrust, conflict). Early literature in graph theory theorized that such networks should display "structural balance," meaning that certain configurations of positive and negative edges are favored and others are disfavored. Here we propose two measures of balance in signed networks based on the established notions of weak and strong balance, and compare their performance on a range of tasks with each other and with previously proposed measures. In particular, we ask whether real-world signed networks are significantly balanced by these measures compared to an appropriate null model, finding that indeed they are, by all the measures studied. We also test our ability to predict unknown signs in otherwise known networks by maximizing balance. In a series of cross-validation tests we find that our measures are able to predict signs substantially better than chance.

💡 Five book recommendations on a complex financial systems, curated by SFI External Prof AndrewWLo
https://fivebooks.com/best-books/finance-andrew-lo/

🎬 The Math That Predicted the Arab Spring - VICE Video: Documentaries, Films, News Videos

https://video.vice.com/en_us/video/motherboard-math-physics-predicted-arab-spring/58ffac792539226a16ed65a6

📝 “it wasn’t until the past two decades that we finally had enough data, powerful computers and sophisticated mathematical algorithms to develop quantitative theories of human social interactions”


🌐 https://blogs.wsj.com/cio/2018/09/14/social-physics-reinventing-analytics-to-better-predict-human-behaviors/

🎲 @ComplexSys

💡 https://blog.frontiersin.org/2018/09/18/how-physics-can-improve-our-society-matjaz-perc-leads-new-specialty-on-social-physics-in-frontiers-in-physics/

💡 https://petterhol.me/2018/09/19/community-detection-a-consumers-voice/

📝 Even in the age of the internet, distance matters immensely in determining whom — and, as a result, what — we know.

🌐 https://www.nytimes.com/interactive/2018/09/19/upshot/facebook-county-friendships.html


🎲 @ComplexSys

🎈 https://mystudentvoices.com/emergence-of-simplicity-and-complexity-d6e89003f7ad?gi=c4a76804a2a3

💡 https://www.quantamagazine.org/universal-method-to-sort-complex-information-found-20180813