From genes to memes - Algorithm may help scientists demystify complex networks
Jul 2021, phys.org
This is it.
I might be just now understanding that the reason we can't "do" memetics yet is because of computational power. Too many variables to model.
Keep in mind that fractals, one of the most ubiquitous phenomena there is, wasn't described mathematically until the 1980's, because that's when computers caught up to it. They needed to be able to run the same, simple algorithm hundreds of thousands of times in less than a lifetime in order to see any results, and this needed to wait for faster computers.
This time, the advancement comes in the form of Boolean networks. These networks aren't just about having lots of on/off switches, but about --networking-- all those switches. That's the hard part. Similar to fractals, which starts from a simple equation (Zn+1 = Zn2 + C), THIS is just a collection of nodes either on or off. Sounds simple, but it's what happens when it gets scaled-up to gives us the complexity of the Twittersphere, for example. A few nodes can generate millions of states.
And before we go any further, it should be noted that quantum computing will flip this entire paradigm upside down, and what today is impossible to even imagine will tomorrow be beamed to your brain via quantum cloud servers in outerspace faster than you can ask for it.
Until then, the news here is that these networks are now being analyzed by these two methods:
1. Parity - making a mirror image of the network where all ON nodes are switched to OFF to identify critical subnetworks, and2. Time Reversal - to identify which network configurations precede which outcomes.
Currently, they're working on networks of 16,000 genes, which is a lot more than we've ever done before. And currently, this work is to learn more about cancer cells, but soon enough we'll be modeling social uprisings and second-order psychological operations.
via Pennsylvania State University, Broad Institute, Dana-Farber Cancer Institute, Semmelweis University, and Center for Complex Network Research: "Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks" Science Advances (2021). https://advances.sciencemag.org/lookup/doi/10.1126/sciadv.abf8124
Post Script:
A new model enables the recreation of the family tree of complex networks
Jun 2021, phys.org
This new study analyzes the time evolution of the citation network in scientific journals and the international trade network over a 100-year period. According to M. Ángeles Serrano, ICREA researcher at UBICS, "What we observe in these real networks is that both grow in a self-similar way, that is, their connectivity properties remain invariable over time, so that the network structure is always the same, while the number of nodes increases."
-University of Barcelona: Muhua Zheng et al, Scaling up real networks by geometric branching growth, Proceedings of the National Academy of Sciences (2021). DOI: 10.1073/pnas.2018994118
Also, don't forget this one, which aged well, very, very well:
Cyber Swarming, Memetic Warfare and viral Insurgency: How Domestic Militants Organize on Memes to Incite Violent Insurrection and Terror Against Government and Law Enforcement; A Contagion and Ideology Report. Alex Goldberg from The Network Contagion Research Institute, Joel Finkelstein from The Network Contagion Research Institute and The James Madison Program in American Ideals and Institutions at Princeton University. Rutgers Miller Center for Community Protection and Resilience. Feb 7 2020. [pdf link]
^Published February 7, 2020, you know, almost a year before January 6, 2021.
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