Language change is a complex social phenomenon, revealing pathways of communication and sociocultural influence. But, while language change has long been a topic of study in sociolinguistics, traditional linguistic research methods rely on circumstantial evidence, estimating the direction of change from differences between older and younger speakers. In this paper, we use a data set of several million Twitter users to track language changes in progress. First, we show that language change can be viewed as a form of social influence: we observe complex contagion for phonetic spellings and "netspeak" abbreviations (e.g., lol), but not for older dialect markers from spoken language. Next, we test whether specific types of social network connections are more influential than others, using a parametric Hawkes process model. We find that tie strength plays an important role: densely embedded social ties are significantly better conduits of linguistic influence. Geographic locality appears to play a more limited role: we find relatively little evidence to support the hypothesis that individuals are more influenced by geographically local social ties, even in their usage of geographical dialect markers.
The Social Dynamics of Language Change in Online Networks Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna Wallach, Fernando Diaz, Jacob Eisenstein
We put high hopes on analyzing big data, but we failed as we haven´t found solutions to the essential problems of our society. Questions like: What is the superior way of organisation of our society in the future or what’s the role of democratic principles in the future? - need to be asked and solved. In the past globalisation, optimization, administration, regulation have served us well and brought us to the level where we are but apparently as the economic situation shows now, we are in a stagnation and all those principles have reached their limits. We need new success principles. ‘I think those success principles are co-creation, co-evolution, collective intelligence, self-organization and self-regulation.’ - says Prof. Dr. Dirk Helbing, Computational Social Science, Department of Humanities, Social and Political Sciences, ETH/Zurich
Embracing Complexity: Strategic Perspectives for an Age of Turbulence [Jean G. Boulton, Peter M. Allen, Cliff Bowman] on Amazon.com. *FREE* shipping on qualifying offers. The book describes what it means to say the world is complex and explores what that means for managers
Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the functional forms of either the preferential attachment function or fitness function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential attachment and node fitness without imposing such functional constraints that works by maximizing a log-likelihood function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for both preferential attachment and node fitness, thus validating the hypothesis that these processes together drive complex network evolution, we also find that node fitness plays the bigger role in determining the degree of a node. This is the first validation of its kind on real-world network data. But surprisingly the rate of preferential attachment is found to deviate from the conventional log-linear form when node fitness is taken into account. The proposed method is implemented in the R package PAFit.
Joint estimation of preferential attachment and node fitness in growing complex networks Thong Pham, Paul Sheridan & Hidetoshi Shimodaira Scientific Reports 6, Article number: 32558 (2016) doi:10.1038/srep32558
Spontaneous synchronization has long served as a paradigm for behavioral uniformity that can emerge from interactions in complex systems. When the interacting entities are identical and their coupling patterns are also identical, the complete synchronization of the entire network is the state inheriting the system symmetry. As in other systems subject to symmetry breaking, such symmetric states are not always stable. Here, we report on the discovery of the converse of symmetry breaking—the scenario in which complete synchronization is not stable for identically coupled identical oscillators but becomes stable when, and only when, the oscillator parameters are judiciously tuned to nonidentical values, thereby breaking the system symmetry to preserve the state symmetry. Aside from demonstrating that diversity can facilitate and even be required for uniformity and consensus, this suggests a mechanism for convergent forms of pattern formation in which initially asymmetric patterns evolve into symmetric ones.
Symmetric States Requiring System Asymmetry Takashi Nishikawa and Adilson E. Motter Phys. Rev. Lett. 117, 114101
Hierarchy is a ubiquitous organizing principle in biology, and a key reason evolution produces complex, evolvable organisms, yet its origins are poorly understood. Here we demonstrate for the first time that hierarchy evolves as a result of the costs of network connections. We confirm a previous finding that connection costs drive the evolution of modularity, and show that they also cause the evolution of hierarchy. We further confirm that hierarchy promotes evolvability in addition to evolvability caused by modularity. Because many biological and human-made phenomena can be represented as networks, and because hierarchy is a critical network property, this finding is immediately relevant to a wide array of fields, from biology, sociology, and medical research to harnessing evolution for engineering.
Mengistu H, Huizinga J, Mouret J-B, Clune J (2016) The Evolutionary Origins of Hierarchy. PLoS Comput Biol 12(6): e1004829. doi:10.1371/journal.pcbi.1004829
Why did the New York Stock Exchange suspend trading without warning on July 8, 2015? Why did certain Toyota vehicles accelerate uncontrollably against the will of their drivers? Why does the programming inside our airplanes occasionally surprise its creators? After a thorough analysis by the top experts, the answers still elude us. You don’t understand the software running your car or your iPhone. But here’s a secret: neither do the geniuses at Apple or the Ph.D.’s at Toyota—not perfectly, anyway. No one, not lawyers, doctors, accountants, or policy makers, fully grasps the rules governing your tax return, your retirement account, or your hospital’s medical machinery. The same technological advances that have simplified our lives have made the systems governing our lives incomprehensible, unpredictable, and overcomplicated. In Overcomplicated, complexity scientist Samuel Arbesman offers a fresh, insightful field guide to living with complex technologies that defy human comprehension. As technology grows more complex, Arbesman argues, its behavior mimics the vagaries of the natural world more than it conforms to a mathematical model. If we are to survive and thrive in this new age, we must abandon our need for governing principles and rules and accept the chaos. By embracing and observing the freak accidents and flukes that disrupt our lives, we can gain valuable clues about how our algorithms really work. What’s more, we will become better thinkers, scientists, and innovators as a result.
What do societies, the Internet, and the human brain have in common? The immediate answer might be "not that much", but in reality they are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and at quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.
The new challenges of multiplex networks: measures and models Federico Battiston, Vincenzo Nicosia, Vito Latora
Not many startups have spent a decade fine-tuning their tech platform prior to launch. But not many startups are trying to radically rethink the structure of the internet.
UK-based MaidSafe, which has been building an alternative, decentralized p2p network since before Steve Jobs announced the original iPhone, is finally — finally! — gearing up for a tentative launch — flicking the switch on its first alpha test network today.
Ties between individuals on a social networks can represent different dimensions of interactions, and the spreading of information and innovations on these networks could potentially be driven by some dimensions more than by others. In this paper we investigate this issue by studying the diffusion of microfinance within rural India villages and accounting for the whole multilayer structure of the underlying social networks. We define a new measure of node centrality, diffusion versatility, and show that this is a better predictor of microfinance participation rate than previously introduced measures defined on aggregated single-layer social networks. Moreover, we untangle the role played by each social dimension and find that the most prominent role is played by the nodes that are central on layers concerned with trust, shedding new light on the key triggers of the diffusion of microfinance.
Untangling the role of diverse social dimensions in the diffusion of microfinance Elisa Omodei, Alex Arenas
Part of Sustainable Economies Law Center's approach in catalyzing a more just and resilient society is to be the change we want to see. We have adopted principles that distribute "ownership" throughout the organization, allow for more dignified livelihoods, expand access to our #legal services, and #empower a new generation of #grassroots legal experts. In our effort towards #radical transparency, we provide on our website the #Sustainable #Economies #Law #Center Organizational Policies
For our vision, The Institute for Research in Complexity and Society is dedicated to applying insights from the study of complexity science to social problems faced by the world community. What is unique about our institute is that we address these issues in a manner that is rigorous theoretically, is supported empirically, and applied pragmatically. That is why we bring together scientists, mathematicians, practitioners, organizational leaders, and technological pioneers in a collaborative setting unhampered by the downsides of traditional academic and government organizational cultures.
Our mission is to engage with communities of practice, small and large organizations, and social enterprises directly, and to do so in ways that improve their effectiveness and at the same time further the development of theory and research methods generalizable to in other milieus as a means to further the accumulation and measurement of social value.
When small companies grow rapidly, the culture can get lost in a sea of new people, processes, geographic expansion, aggressive growth targets, and the avalanche of changes needed to scale. The culture can become a boat anchor, dragging behind the desired direction and pulling people in the wrong direction. But when senior leaders make a conscious decision to keep the best of the cultural elements that brought the company success in the first place, great things can happen.
Cafe Rio Mexican Grill did just that. In 2011, Dave Gagnon, a former Burger King senior vice president of North America company operations and training, took over as CEO and COO. Andy Hooper, who had led the culture-shaping work at Burger King, joined Cafe Rio as chief people officer. The organization had an outstanding culture, and was in its third year of nearly double-digit comparable sales growth. But to grow rapidly, the executive leadership team needed to codify the culture that was largely built on ‘tribal knowledge transfer’ to scale for national expansion.
We study the dynamic network of real world person-to-person interactions between approximately 1,000 individuals with 5-min resolution across several months. There is currently no coherent theoretical framework for summarizing the tens of thousands of interactions per day in this complex network, but here we show that at the right temporal resolution, social groups can be identified directly. We outline and validate a framework that enables us to study the statistical properties of individual social events as well as series of meetings across weeks and months. Representing the dynamic network as sequences of such meetings reduces the complexity of the system dramatically. We illustrate the usefulness of the framework by investigating the predictability of human social activity.
Fundamental structures of dynamic social networks Vedran Sekara, Arkadiusz Stopczynski, and Sune Lehmann
“Big Bet” philanthropy has gotten a lot of press lately, and, indeed, the dollar amounts here are somewhat staggering in the nonprofit world, but the Blue Meridian initiative has some special design aspects that deserve a thoughtful response from readers.
With big data, we can multiply our options and filter out things we don’t want to see. But there is much to be said for making discoveries through pure serendipity: contingency and randomness often furnish the transformational or counterintuitive ideas that propel humanity forward.
The living-together of distinct organisms in a single termite nest along with the termite builder colony, is emblematic in its ecological and evolutionary significance. On top of preserving biodiversity, these interspecific and intraspecific symbioses provide useful examples of interindividual associations thought to underly transitions in organic evolution. Being interindividual in nature, such processes may involve emergent phenomena and hence call for analytical solutions provided by computing tools and modelling, as opposed to classical biological methods of analysis. Here we provide selected examples of such solutions, showing that termite studies may profit from a symbiotic-like link with computing science to open up wide and new research avenues in ecology and evolution.
Fruitful symbioses between termites and computers Og DeSouza, Elio Tuci, Octavio Miramontes
In the last years, network scientists have directed their interest to the multi-layer character of real-world systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world. The contributions presented in this Focus Issue cover, from different points of view, the many achievements and still open questions in the field of multi-layer networks, such as: new frameworks and structures to represent and analyze heterogeneous complex systems, different aspects related to synchronization and centrality of complex networks, interplay between layers, and applications to logistic, biological, social, and technological fields.
Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems Stefano Boccaletti, Regino Criado, Miguel Romance and Joaquín J. Torres
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