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If You're So Free, Why Do You Follow Others? The Sociological Science Behind Social Networks and Social Influence. Nicholas Christakis, Professor of Medical ...
Via luiy, Ashish Umre
This video explains our research on autonomous unmanned aerial vehicles (UAVs). The research team at the AlpenAdria University and Lakeside Labs developing a multiUAV system by four key components:  the multiple UAV platforms, http://youtu.be/QX2UPkd6yIc
Via Complexity Digest
The "smallworld effect" is the observation that one can find a short chain of acquaintances, often of no more than a handful of individuals, connecting almost any two people on the planet. It is often expressed in the language of networks, where it is equivalent to the statement that most pairs of individuals are connected by a short path through the acquaintance network. Although the smallworld effect is wellestablished empirically for contemporary social networks, we argue here that it is a relatively recent phenomenon, arising only in the last few hundred years: for most of mankind's tenure on Earth the social world was large, with most pairs of individuals connected by relatively long chains of acquaintances, if at all. Our conclusions are based on observations about the spread of diseases, which travel over contact networks between individuals and whose dynamics can give us clues to the structure of those networks even when direct network measurements are not available. As an example we consider the spread of the Black Death in 14thcentury Europe, which is known to have traveled across the continent in welldefined waves of infection over the course of several years. Using established epidemiological models, we show that such wavelike behavior can occur only if contacts between individuals living far apart are exponentially rare. We further show that if longdistance contacts are exponentially rare, then the shortest chain of contacts between distant individuals is on average a long one. The observation of the wavelike spread of a disease like the Black Death thus implies a network without the smallworld effect.
Via Claudia Mihai
Artur Avila’s solutions to ubiquitous problems in chaos theory have “changed the face of the field,” earning him Brazil’s first Fields Medal.
Via Claudia Mihai
Researchers from the Santa Fe Institute and the Smithsonian Institution have pieced together a highly detailed picture of feeding relationships among 700 mammal, bird, reptile, fish, insect, and plant species from a 48 million year old lake and forest ecosystem. Their analysis of fossilized remains from the Messel deposit near Frankfurt, Germany, provides the most compelling evidence to date that ancient food webs were organized much like modern food webs. Their paper describing the research appears online and open access this week in Proceedings of the Royal Society B: Biological Sciences.
Via Claudia Mihai
The “study of complexity” refers to the attempt to find common principles underlying the behavior of complex systems—systems in which large collections of components interact in nonlinear ways. Here, the term nonlinear implies that the system can’t be understood simply by understanding its individual components; nonlinear interactions cause the whole to be “more than the sum of its parts.” How Can the Study of Complexity Transform Our Understanding of the World? Melanie Mitchell https://www.bigquestionsonline.com/content/howcanstudycomplexitytransformourunderstandingworld
Via Complexity Digest
(...) complex systems are characterized by the interactions between their numerous elements. The word ‘complex’ comes from the Latin plexus which means entwined. In other words, it is difficult to correlate global properties of complex systems with the properties of the individual constituent components. This is primarily because the interactions between these individual elements partly determine the future states of the system (Gershenson 2013). If these interactions are not included in the developed models, the models would not be an accurate reflection of the modelled phenomenon. Gershenson, C. & M. A. Niazi (2013). Multidisciplinary applications of complex networks modeling, simulation, visualization, and analysis. Complex Adaptive Systems Modeling 1:17 http://dx.doi.org/10.1186/21943206117
Via Complexity Digest, Ashish Umre
In the 1960s Schelling devised a simple model in which a mixed group of people spontaneously segregates by race even though no one in the population desires that outcome. Initially, black and white families are randomly distributed. At each step in the modeling process the families examine their immediate neighborhood and either stay put or move elsewhere depending on whether the local racial composition suits their preferences. The procedure is repeated until everyone finds a satisfactory home (or until the simulator’s patience is exhausted).
Via Bernard Ryefield, Jorge Louçã, NESS

We study the conditions for persistent cooperation in an offlattice model of mobile agents playing the Prisoner's Dilemma game with pure, unconditional strategies. Each agent has an exclusion radius ${r}_{P}$, which accounts for the population viscosity, and an interaction radius ${r}_{\mathrm{int}}$, which defines the instantaneous contact network for the game dynamics. We show that, differently from the ${r}_{P}=0$ case, the model with finitesized agents presents a coexistence phase with both cooperators and defectors, besides the two absorbing phases, in which either cooperators or defectors dominate. We provide, in addition, a geometric interpretation of the transitions between phases. In analogy with lattice models, the geometric percolation of the contact network (i.e., irrespective of the strategy) enhances cooperation. More importantly, we show that the percolation of defectors is an essential condition for their survival. Differently from compact clusters of cooperators, isolated groups of defectors will eventually become extinct if not percolating, independently of their size.
Via Claudia Mihai
The Matthew effect describes the phenomenon that in societies the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in network science, where the more connected nodes are destined to acquire many more links in the future than the auxiliary nodes. Cumulative advantage and successbreadssuccess also both describe the fact that advantage tends to beget further advantage. The concept is behind the many power laws and scaling behaviour in empirical data, and it is at the heart of selforganization across social and natural sciences. Here we review the methodology for measuring preferential attachment in empirical data, as well as the observations of the Matthew effect in patterns of scientific collaboration, sociotechnical and biological networks, the propagation of citations, the emergence of scientific progress and impact, career longevity, the evolution of common English words and phrases, as well as in education and brain development. We also discuss whether the Matthew effect is due to chance or optimisation, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research. The Matthew effect in empirical data Matjaz Perc http://arxiv.org/abs/1408.5124
Via Complexity Digest
Support is growing for a decadesold physics idea suggesting that localized episodes of disordered brain activity help keep the overall system in healthy balance
Via Claudia Mihai
The idea is advanced that selforganization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of selforganization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of selforganization and of decision making are identical. This makes it clear how selforganization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous selforganization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural selforganization in dynamical systems, and by the probabilistic formulation of classical and behavioral decision theories. In all these cases, selforganization is described as the process of evaluating the probabilities of macroscopic states or prospects in the search for a state with the largest probability. The general way of deriving the probability measure for classical systems is the principle of minimal information, that is, the conditional entropy maximization under given constraints. Behavioral biases of decision makers can be characterized in the same way as analogous to quantum fluctuations in natural systems Selforganization in complex systems as decision making V.I. Yukalov, D. Sornette arXiv:1408.1529, 2014 http://arxiv.org/abs/1408.1529
Via Complexity Digest
WHEN physicists take an interest in the living world, some biologists fear the worst. After all, goes the bad joke, there's only so much you can gain by modelling a cow as a sphere. But one crucial idea from physics may hold valuable insights into complex biological behaviour in everything from birds to gene networks. There is increasing evidence that many systems we observe in living things are close to what's called a critical point – they sit on a knifeedge, precariously poised between order and disorder. Odd as it may sound, this strategy could confer a variety of benefits, in particular the flexibility to deal with a complex and unpredictable environment. http://www.newscientist.com/article/mg22229660.700oneruleoflifearewepoisedontheborderoforder.html ; Draft at http://philipball.blogspot.mx/2014/04/criticalityandphasetransitionsin.html
Via Complexity Digest
The Resources section contains annotated links to a wide variety of webbased resources related to complex systems. These include journals, conferences, tutorials, software, videos, among other types of resources that will be useful for all levels of interest.
Via Complexity Digest, Bill Aukett, Bernard Ryefield
Commercial aviation is feasible thanks to the complex sociotechnical air transportation system, which involves interactions between human operators, technical systems, and procedures. In view of the expected growth in commercial aviation, significant changes in this sociotechnical system are in development both in the USA and Europe. Such a complex sociotechnical system may generate various types of emergent behavior, which may range from simple emergence, through weak emergence, up to strong emergence. The purpose of this paper is to demonstrate that agentbased modeling and simulation allows identifying changed and novel rare emergent behavior in this complex sociotechnical system.
Via Bernard Ryefield
This video provides a basic introduction to the science of complex systems, focusing on patterns in nature. (For more information on agentbased modeling, vi...
Via Lorien Pratt, Bernard Ryefield
The éToile Platform is an open, interactive, new way of sharing educational resources for Master and PhD levels in Complexity Sciences domains. In different modules, students and researchers can: check their knowledge using the étoile evaluation tests;interact with other people studying the same subjects;use the éToile facilities for studying and researching on the Internet;contribute for an ecology of pedagogical resources;certificated their mastery of a core curriculum in Complexity Sciences;interact with a worldwide community of students and scientific researchers within the CSDigital Campus.
Via Bernard Ryefield
There is common ground in analysing financial systems and ecosystems, especially in the need to identify conditions that dispose a system to be knocked from seeming stability into another, less happy state.
Via Juan I. Perotti
The hallmark of deterministic chaos is that it creates information—the rate being given by the KolmogorovSinai metric entropy. Since its introduction half a century ago, the metric entropy has been used as a unitary quantity to measure a system’s intrinsic unpredictability. Here, we show that it naturally decomposes into two structurally meaningful components: A portion of the created information—the ephemeral information—is forgotten and a portion—the bound information—is remembered. The bound information is a new kind of intrinsic computation that differs fundamen tally from information creation: it measures the rate of active information storage. We show that it can be directly and accurately calculated via symbolic dynamics, revealing a hitherto unknown richness in how dynamical systems compute. Chaos Forgets and Remembers: Measuring Information Creation, Destruction, and Storage Ryan G. James, Korana Burke, James P. Crutchfield http://www.santafe.edu/research/workingpapers/abstract/a0504be522643a0cc27e85bb3bf074e5/
Via Complexity Digest
