Living organisms are characterized by a degree of hierarchical complexity that appears to be inaccessible to even the most complex inanimate objects. Routes and patterns of the evolution of complexity are poorly understood. We propose a general conceptual framework for emergence of complexity through competing interactions and frustrated states similar to those that yield patterns in striped glasses and cause self-organized criticality. We show that biological evolution is replete with competing interactions and frustration that, in particular, drive major transitions in evolution. The key distinction between biological and nonbiological systems seems to be the existence of long-term digital memory and phenotype-to-genotype feedback in living matter.
Yuri I. Wolf, Mikhail I. Katsnelson, and Eugene V. Koonin
The flagship conference of the Complex Systems Society will go to Latin America for the first time in 2017. The Mexican complex systems community is enthusiast to welcome colleagues to one of our richest destinations: Cancun.
The conference will include presentations by Mario Molina (Environment, Nobel Prize in Chemistry), Ranulfo Romo (neuroscience), Antonio Lazcano (origins of life), Marta González (human mobility), Dirk Brockmann (epidemiology), Stefano Battiston (economics) John Quackenbush (computational biology), and many more.
Abstract deadline March 10
Notifications of Acceptance April 21
Conference September 17-22
By Chengyi Tu, Joel Carr & Samir Suweis
The easy access to large data sets has allowed for leveraging methodology in network physics and complexity science to disentangle patterns and processes directly from the data, leading to key insights in the behavior of systems. Here we use country specific food production data to study binary and weighted topological properties of the bipartite country-food production matrix. This country-food production matrix can be: 1) transformed into overlap matrices which embed information regarding shared production of products among countries, and or shared countries for individual products, 2) identify subsets of countries which produce similar commodities or subsets of commodities shared by a given country allowing for visualization of correlations in large networks, and 3) used to rank country fitness (the ability to produce a diverse array of products weighted on the type of food commodities) and food specialization (quantified on the number of countries producing a specific food product weighted on their fitness). Our results show that, on average, countries with high fitness produce both low and high specializion food commodities, whereas nations with low fitness tend to produce a small basket of diverse food products, typically comprised of low specializion food commodities.