Natural selection supplies an incredibly powerful way of pruning variation into effective solutions to the challenges of the environment. But it can’t explain where all that variation came from. As the biologist Hugo de Vries wrote in 1905, “natural selection may explain the survival of the fittest, but it cannot explain the arrival of the fittest.” Over the past several years, Wagner and a handful of others have been starting to understand the origins of evolutionary innovation. Thanks to their findings so far, we can now see not only how Darwinian evolution works but why it works: what makes it possible.
These ideas suggest that evolvability and openness to innovation are features not just of life but of information itself. That is a view long championed by Schuster’s sometime collaborator, Nobel laureate chemist Manfred Eigen, who insists that Darwinian evolution is not merely the organizing principle of biology but a “law of physics,” an inevitable result of how information is organized in complex systems. And if that’s right, it would seem that the appearance of life was not a fantastic fluke but almost a mathematical inevitability.
We study the two particle annihilation reaction A+B->Ø A+B→∅ on interconnected scale free networks. We show that the mixing of particles and the evolution of the process are influenced by the number of interconnecting links and by their functional properties, while surprisingly when the interconnecting links have the same function as the links within the networks, they are not affected by the interconnectivity strategies in use. Due to the better mixing, which suppresses the segregation effect, we show that the reaction rates are faster than what was observed in other topologies, in-line with previous studies performed on single scale free networks.
Conventional wisdom dictates that our genetic destiny is fixed at conception. But Dr. Moalem's groundbreaking book shows us that the human genome is far more fluid. By bringing us to the bedside of his unique and complex patients, he masterfully demonstrates what rare genetic conditions can teach us all about our own health and well-being. Drawing on bleeding-edge science and sometimes heartbreaking stories of individuals he’s treated for rare genetic anomalies, Moalem explains how your DNA’s constant shape-shifting is “mediated and orchestrated by how you live, where you live, the stresses you face, and the things you consume.”
If someone gave you a book filled with a stranger’s complete genetic code, could you predict everything about that stranger, from his or her appearance to his or her behavior? Of course, this would be an impossible task. The reason for this impossibility lies in a concept called integrative levels of organization, which describes the way units of matter are organized and integrated into levels of increasing complexity. At each level, new properties and rules emerge that cannot be predicted by full knowledge of a lower level. Such properties are called emergent properties. Because of emergent properties, knowledge of a lower level, such as a genome, cannot be used to predict everything about a higher level, such as an organism.
“Understanding the problems we face in the world today requires more than just data, it requires insight into radical changes that will take place in the future,” Bar-Yam stated. “We have to understand transitions that might happen in the behavior of systems, not just how they were behaving yesterday.”
The new science of complex systems will be at the heart of the future of the Worldwide Knowledge Society. It is providing radical new ways of understanding the physical, biological, ecological, and techno-social universe. Complex Systems are open, value-laden, multi-level, multi-component, reconfigurable systems of systems, situated in turbulent, unstable, and changing environments. They evolve, adapt and transform through internal and external dynamic interactions. They are the source of very difficult scientific challenges for observing, understanding, reconstructing and predicting their multi-scale dynamics. The challenges posed by the multi-scale modelling of both natural and artificial adaptive complex systems can only be met with radically new collective strategies for research and teaching (...)
Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties appear to universally emerge. We propose that an important class of complex systems can be modelled as a construction of potentially infinitely many levels of organization all following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance in the pyramid of production entities of the movie industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering or modularity, their hierarchy, their fractality and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of the hierarchical nature of our world.
Devourer of Encyclopedias: Stanislaw Lem's "Summa Technologiae" lareviewofbooks If evolution was charted in a form of code, and a simple Turing machine could be shown to be equivalent to the most complex computational machines imaginable, then...
Systems of many interacting components — be they species, integers or subatomic particles — kept producing the same statistical curve, which had become known as the Tracy-Widom distribution. This puzzling curve seemed to be the complex cousin of the familiar bell curve, or Gaussian distribution, which represents the natural variation of independent random variables like the heights of students in a classroom or their test scores. Like the Gaussian, the Tracy-Widom distribution exhibits “universality,” a mysterious phenomenon in which diverse microscopic effects give rise to the same collective behavior. “The surprise is it’s as universal as it is,” said Tracy, a professor at the University of California, Davis.
Using the effective complexity measure, proposed by M. Gell-Mann and S. Lloyd, we give a quantitative definition of an emergent property. We use several previous results and properties of this particular information measure closely related to the random features of the entity and its regularities.
Complexity and the Emergence of Physical Properties Miguel Angel Fuentes
This article is based on the keynote address presented to the European Meetings on Cybernetics and Systems Research (EMCSR) in 2012, on the occasion of Edgar Morin receiving the Bertalanffy Prize in Complexity Thinking, awarded by the Bertalanffy Centre for the Study of Systems Science (BCSSS). The following theses will be elaborated on: (a) The whole is at the same time more and less than its parts; (b) We must abandon the term "object" for systems because all the objects are systems and parts of systems; (c) System and organization are the two faces of the same reality; (d) Eco-systems illustrate self-organization.
Complex Thinking for a Complex World – About Reductionism, Disjunction and Systemism Edgar Morin
Systema: connecting matter, life, culture and technology Vol 2, No 1 (2014)
The modern world is complex beyond human understanding and control. The science of complex systems aims to find new ways of thinking about the many interconnected networks of interaction that defy traditional approaches. Thus far, research into networks has largely been restricted to pairwise relationships represented by links between two nodes. This volume marks a major extension of networks to multidimensional hypernetworks for modeling multi-element relationships, such as companies making up the stock market, the neighborhoods forming a city, people making up committees, divisions making up companies, computers making up the internet, men and machines making up armies, or robots working as teams.
This volume makes an important contribution to the science of complex systems by: (i) extending network theory to include dynamic relationships between many elements; (ii) providing a mathematical theory able to integrate multilevel dynamics in a coherent way; (iii) providing a new methodological approach to analyze complex systems; and (iv) illustrating the theory with practical examples in the design, management and control of complex systems taken from many areas of application.
Ever since humans first daubed works of art on rock faces and in caves, there has been evidence of mankind’s attempts to understand the world in which it finds itself. For millennia this was achieved...