Today the network of relationships linking the human race to itself and to the rest of the biosphere is so #complex that all aspects affect all others to an extraordinary degree. Someone should be studying the whole system, however crudely that has to be done, because no gluing together of partial studies of a complex nonlinear system can give a good idea of the behaviour of the whole.

Murray Gell-Mann in ISSS The Primer Project International Society for the Systems Sciences (ISSS) seminar (12 October - 10 November 1997).

The difference between #reversible and #irreversible events has particular explanatory value in #complex_systems (such as living organisms, or ecosystems). According to the biologists Humberto Maturana and Francisco Varela, living organisms are characterized by autopoiesis, which enables their continued existence. More primitive forms of self-organizing systems have been described by the physicist and chemist Ilya Prigogine. In the context of complex systems, events which lead to the end of certain #self-organising processes, like death, extinction of a species or the collapse of a meteorological system can be considered as irreversible. Even if a clone with the same organizational principle (e.g. identical DNA-structure) could be developed, this would not mean that the former distinct system comes back into being. Events to which the self-organizing capacities of organisms, species or other complex systems can adapt, like minor injuries or changes in the physical environment are reversible. However, #adaptation depends on import of negentropy into the organism, thereby increasing irreversible processes in its environment. Ecological principles, like those of sustainability and the precautionary principle can be defined with reference to the concept of reversibility.

https://en.wikipedia.org/wiki/Irreversible_process#Complex_systems

📄 Hidden geometric correlations in real multiplex networks

Kaj-Kolja Kleineberg, Marián Boguñá, M. Ángeles Serrano & Fragkiskos Papadopoulos

http://www.nature.com/nphys/journal/v12/n11/full/nphys3812.html

📌 ABSTRACT:
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.

Subject terms:

#Applied_physics
#Complex_networks
#Statistics

🎯 Samuel Arbesman on #Complex_Adaptive_Systems and the Difference between #Biological and #Physics Based Thinking


https://www.farnamstreetblog.com/2016/11/samuel-arbesman-biological-physics-thinking/?utm_source=twitter.com&utm_medium=social&utm_campaign=buffer&utm_content=bufferb2052