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Knowledge Models: Their History and Future
The History of Knowledge Models and a description of the current model the Network Model and/or the Fractal Model.

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ASC Staff

First Published

September 14, 2023

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Learning Blockchain

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The History of Communication Models has become my most popular post by orders of magnitude.

Sometimes it begins with one insight and grows into many branches; other times it grows as a complex and interconnected network.

Infographics expert Manuel Lima explores the thousand-year history of mapping data — from languages to dynasties — using trees and networks of information.

It’s a fascinating history of visualizations and a look into humanity’s urge to map what we know.

This mirrors the work I am doing on the History, and future, of Communication Models.

One of the main topics of scientific research is classification. Classification is the operation of distributing objects into classes or groups—which are, in general, less numerous than them. It has a long history that has developed during four periods: (1) Antiquity, where its lineaments may be found in the writings of Plato and Aristotle; (2) The Classical Age, with natural scientists from Linnaeus to Lavoisier; (3) The 19th century, with the growth of chemistry and information science; and (4) the 20th century, with the arrival of mathematical models and computer science. Since that time, and from an extensional viewpoint, mathematics, specifically, the theory of orders and the theory of graphs or hypergraphs, has facilitated the precise study of strong and weak forms of order in the world, and the computation of all the possible partitions, chains of partitions, covers, hypergraphs or systems of classes that we can construct on a domain. With the development of computer science, Artificial Intelligence, and new kinds of languages such as oriented-objected languages, an intensional approach has completed the previous one. Ancient discussions between Aristotle and Plato, Ramus and Pascal, Jevons and Joseph found some kind of revival via object-oriented modeling and programming, most of objected oriented languages being concerned with hierarchies, or partial orders: these structures reflect in fact the relations between classes in those languages, which generally admit single or multiple inheritance. In spite of these advances, most of classifications are still based on the evaluation of resemblances between objects that constitute the empirical data. This one is almost always computed by the means of some notion of distance and of some algorithms of aggregation of classes. So all these classifications remain, for technical and epistemological reasons that are detailed below, very unstable ones. A real algebra of classifications, which could explain their properties and the relations existing between them, is lacking. Though the aim of a general theory of classifications is surely a wishful thought, some recent conjecture gives the hope that the existence of a metaclassification (or classification of all classification schemes) is possible.

Over the past 10 years, I’ve been researching the way people organize and visualize information, and I’ve noticed an interesting shift. For a long period of time, we believed in a natural ranking order in the world around us, also known as the great chain of being orka naura in Latin, a top-down structure that normally starts with God at the very top, followed by angels, noble man, common people, animals, and so on. This idea was actually based on Aristotle’s ontology, which classified all things known to man in a set of opposing categories like the ones you see behind me.

But over time, interestingly enough, this concept adopted the branching schema of a tree in what became known as the porphyrin tree, also considered to be the oldest tree of knowledge. The branching scheme of the tree was in fact such a powerful metaphor for conveying information that it became over time an important communication tool to map a variety of systems of knowledge. We can see trees being used to map morality with its popular trio, virtues, and T tree of vices. As you can see here in this beautiful illustrations from medieval Europe, we can see trees being used to map consanguinity, the various blood ties between people. We can also see trees being used to map genealogy, perhaps the most famous archetype of the tree diagram. I think many of you in the audience have probably seen family trees. Many of you probably even have your own family trees drawn in such a way we can see trees, even mapping systems of law, the various decrees and rulings of kings and rulers.

And finally, of course, also very popular scientific metaphor. We can see trees being used to map all species known to man, and trees ultimately became such a powerful visual metaphor. ’cause in many ways, they really embodied this human desire for order, for balance, for unity, for symmetry. However, nowadays you’re really facing new complex, intricate challenges that cannot be understood by simply employing a simple tree diagram. And the new metaphor is currently emerging, and it’s currently replacing the tree in visualizing various systems of knowledge. It’s really providing us with a new lens to understand the world around us. And this new metaphor is the metaphor of the network, and we can see this shift from trees into networks in many domains of knowledge. We can see this shift in the way we try to understand the brain.

Well, before we used to think of the brain as a modular centralized organ where a given area was responsible for a set of actions and behaviors. The more we know about the brain, the more we think of it as a large music symphony played by hundreds and thousands of instruments. This is a beautiful snapshot created by the Blue Brain Project, where you can see 10,000 neurons and 30 million connections. And this is only mapping 10% of mammalian neocortex. We can also see this shift in the way we try to conceive human knowledge. These are some remarkable trees of knowledge or trees of science by Spanish schooler, Ram Lu. And Lule was actually the precursor, the very first one, who created a metaphor of science as a tree, a metaphor we use every single day When we say biology is a branch of science, when we say genetics is a branch of science, but perhaps the most beautiful of all trees of knowledge, at least for me, was created for the French encyclopedia by did and in 1751.

This was really the bastion of the French Enlightenment. And this gorgeous illustration was featured as a table of contents for the encyclopedia, and it actually illustrated maps out all domains of knowledge as separate branches of a tree. But knowledge is much more intricate than this. These are two maps of Wikipedia showing the interlinkage of articles related to history on the left and mathematics on the right. And I think by looking at these maps and the other ones that have been created of Wikipedia, arguably one of the largest rhizomatic structures ever created by man, we can really understand. Our human knowledge is much more intricate and interdependent just like a network. We can also see this interesting shift in the way we map social ties between people. This is the typical organizational chart. I’m assuming many of you have seen a similar chart as well in your own corporations of others.

It’s a top down structure that normally starts with the c e O at the very top and where you can drill down all the way to the individual workman on the bottom. But humans sometimes are, well, actually all humans are really unique in their own way, and sometimes we really don’t play well on this really rigid structure. I think the internet is really changing this paradigm quite a lot. This is a fantastic map of online social collaboration between Pearl developers. And Pearl is a famous programming language. And here you can see how different programmers are actually exchanging files and working together on a given project. And here you can notice that this is a completely decentralized process. There’s no leader in this organization. It’s a network. We can also see this interesting shift when we look at terrorism. One of the main challenges of understanding terrorism nowadays is that we are dealing with decentralized independent cells where there’s no leader leading the whole process.

And here you can actually see how visualization is being used. The diagram that you see behind me is showing all the terrorists involved in the Madrid attack in 2004. And what they did here is that they actually segmented the network into three different years represented by the vertical layers that you see behind me, and the blue lines tied together the people that were present in that network year after year. So even though there’s no leader per se, these people are probably the most influential ones in that organization, the ones that know more about the past and the future plans and goals of this particular cell. We can also see the shift from trees into networks in the way we classify and organize species.

The image on the right is the only illustration that Darwin included in the Origin of species, which Darwin called the Tree of Life. And there’s actually a letter from Darwin to the publisher expanding on the importance of this particular diagram. It was critical for Darwin’s theory on evolution, but recently scientists discovered that overlaying this tree of life is a dense network of bacteria. And this bacteria is actually tying together species that were completely separated before to what scientists are now calling not the tree of life, but the web of life, the network of life. And finally, we can really see this shift again when we look at ecosystems around our planet. No more do we have this simplified predator versus pray diagrams. We have all learned at school. This is a much more accurate depiction of an ecosystem. This is a diagram created by Professor David Levin mapping close to 100 pieces that interact with the codfish off the coast of Newfoundland in Canada. And I think here we can really understand the intricate and interdependent nature of most ecosystems that are bound to our planet.

But even though recent this metaphor, the metaphor of the network is really already adopting various shapes and forms, and it’s almost becoming a growing visual taxonomy. It’s almost becoming the syntax of a new language. And this is one aspect that truly fascinates me. And these are actually 15 different typologies I’ve been collecting over the time. And it really shows immense visual diversity of this new metaphor. And here’s an example. The first on the very top band, you have radio convergence, a model visualization model that has become really popular over the last five years. And the top left, the very first project is a gene network, followed by a network of IP addresses, machine servers, followed by a network of Facebook friends. You probably couldn’t find more dispar topics yet. They are using the same metaphor, the same visual model to map inherent complexities of its own subjects.

And here are a few more examples of the many I’ve been collecting of this growing visual taxonomy of networks. But networks are not just a scientific metaphor. As designers, researchers and scientists try to map a variety of complex systems, there are in many ways influencing traditional art fields like painting and sculpture, and influencing many different artists. And perhaps because networks have this huge aesthetical force to them, they’re immensely gorgeous. They are really becoming a cultural meme and driving a new art movement, which I’ve called naturalism. And we can see this influence in this movement in a variety of ways. This is just one of many examples where you can see this influence from science into art. The example on your left side is IP mapping, a computer generated map of IP addresses against servers, machines. And on your right side, you have tracing structures and unstable networks by Sharon Malloy using all an animal on canvas. And here are a few more paintings by Sharon Malloy, some gorgeous intricate paintings. And here’s another example of that, interesting cross-pollination between science and art. On your left side, you have Operation Smile. It is a computer generated map of a social network. And on your right side, you have Field four by Emma McNally, using only graphite on paper. Emma McNally is one of the main leaders of this movement, and she creates this striking imaginary landscapes where you can really notice the influence from traditional network visualization.

But network doesn’t happen only in two dimensions. This is perhaps one of my favorite projects of this new movement, and I think the title really says it all. It’s called Galaxies forming along filaments like droplets along the strands of a spider’s web. And I just find this particular project being immensely powerful. It was created by Thomas Ano, and he occupies these large spaces, creates this massive installations using only elastic ropes. As you actually navigate that space and bouncing around of those elastic ropes, the entire network kind of shifts almost like a real organic network wood. And here’s, and yet another example of naturalism taking to a whole different level. This was created by Japanese artists Shista in a piece called In Silence. And Chiara like Thomas Ana fills these rooms with this dense network, this dense web of elastic robes and black wool and thread sometimes including objects, as you can see here, sometimes even including people in many of our installations.

But networks are also not just a new trend, and it’s too easy for us to dismiss it. As such, networks really embody notions of decentralization, of interconnectedness, of interdependence. And this new way of thinking is critical for us to solve many of the complex problems we are facing nowadays, from decoding the human brain to understanding the vast universe. Out there on your left side, you have a snapshot of a neural network of a mouse, very similar to our own at this particular scale. And on your right side, you have the millennium simulation. It was the largest and most realistic simulation of the growth of cosmic structure. It was able to recreate the history of 20 million galaxies in approximately like 25 terabytes of output. And coincidentally or not, I just find this particular comparison between the smallest scale of knowledge, the brain, and the larger skill of knowledge, the universe itself to be really, really quite striking and fascinating. Because as Bruce Mao once said, when everything is connected to everything else, for better or worse, everything matters. Thank you so much.