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The 2015 edition of the Global Risks report completes a decade of highlighting the most significant longterm risks worldwide, drawing on the perspectives of experts and global decisionmakers.
Today complex systems science is rapidly growing as a discipline, with relevance to many areas of science and as an approach to addressing a wide range of real world problems. Understanding the fundamental mathematical origins of complex systems science reveals its conceptual richness and ability to advance science and expand its application. I will review these origins, describe some current applications, and point to the opportunities of the future. Complex Systems Science: Where Does It Come From and Where is It Going To? Yaneer BarYam Opening plenary address at the Conference on Complex Systems 2015, at Arizona State University in Tempe, Arizona. http://www.necsi.edu/research/overview/ccs15.html ;
Via Complexity Digest
Student drop out in universities is a universal problem [..]. Even though researchers found several critical aspects that affect student success and that could reduce student drop out, implementation of research results have rarely led to any major improvements in graduation rates.
One way of understanding this, is by thinking of student success as something originating from a complex system.
Via verstelle
Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational factchecking methods that may one day mitigate the spread of harmful misinformation. Computational fact checking from knowledge networks Giovanni Luca Ciampaglia, Prashant Shiralkar, Luis M. Rocha, Johan Bollen, Filippo Menczer, Alessandro Flammini http://arxiv.org/abs/1501.03471
Via Complexity Digest, NESS
Cooperativeness is a defining feature of human nature. Theoreticians have suggested several mechanisms to explain this ubiquitous phenomenon, including reciprocity, reputation, and punishment, but the problem is still unsolved. Here we show, through experiments conducted with groups of people playing an iterated Prisoner's Dilemma on a dynamic network, that it is reputation what really fosters cooperation. While this mechanism has already been observed in unstructured populations, we find that it acts equally when interactions are given by a network that players can reconfigure dynamically. Furthermore, our observations reveal that memory also drives the network formation process, and cooperators assort more, with longer link lifetimes, the longer the past actions record. Our analysis demonstrates, for the first time, that reputation can be very well quantified as a weighted mean of the fractions of past cooperative acts and the last action performed. This finding has potential applications in collaborative systems and ecommerce.
Via Ashish Umre
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

From flocking birds to swarming molecules, physicists are seeking to understand 'active matter' — and looking for a fundamental theory of the living world.
Researchers are uncovering the hidden laws that reveal how the Internet grows, how viruses spread, and how financial bubbles burst.
Via Bernard Ryefield
In an article for the Journal of Industrial Ecology, SFI’s Luís Bettencourt and Christa Brelsford take a complex systems perspective ...
The Multi Agent Based programming, modeling and simulation environment of NetLogo has been used extensively during the last fifteen years for educational among other purposes. The learning subject, upon interacting with the Users Interface of NetLogo, can easily study properties of the simulated natural systems, as well as observe the latters response, when altering their parameters. In this research, NetLogo was used under the perspective that the learning subject (student or prospective teacher)interacts with the model in a deeper way, obtaining the role of an agent. This is not achieved by obliging the learner to program (write NetLogo code) but by interviewing them, together with applying the choices that they make on the model. The scheme was carried out, as part of a broader research, with interviews, and web page like interface menu selections, in a sample of 17 University students in Athens (prospective Primary School teachers) and the results were judged as encouraging. At a further stage, the computers were set as a network, where all the agents performed together. In this way the learners could watch onscreen the overall outcome of their choices and actions on the modeled ecosystem. This seems to open a new, small, area of research in NetLogo educational applications.
Via Bernard Ryefield
Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom opendata program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multimodal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset. The multilayer temporal network of public transport in Great Britain Riccardo Gallotti & Marc Barthelemy Scientific Data, Published online: 6 January 2015;  http://dx.doi.org/10.1038/sdata.2014.56
Via Complexity Digest, NESS
Human crowds often bear a striking resemblance to interacting particle systems, and this has prompted many researchers to describe pedestrian dynamics in terms of interaction forces and potential energies. The correct quantitative form of this interaction, however, has remained an open question. Here, we introduce a novel statisticalmechanical approach to directly measure the interaction energy between pedestrians. This analysis, when applied to a large collection of human motion data, reveals a simple power law interaction that is based not on the physical separation between pedestrians but on their projected time to a potential future collision, and is therefore fundamentally anticipatory in nature. Remarkably, this simple law is able to describe human interactions across a wide variety of situations, speeds and densities. We further show, through simulations, that the interaction law we identify is sufficient to reproduce many known crowd phenomena. Universal Power Law Governing Pedestrian Interactions Phys. Rev. Lett. 113, 238701 – Published 2 December 2014 Ioannis Karamouzas, Brian Skinner, and Stephen J. Guy http://dx.doi.org/10.1103/PhysRevLett.113.238701
Via Complexity Digest
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
