To investigate the dynamics of social networks and the formation and evolution of online communities in response to extreme events, we collected three datasets from Twitter shortly before and after the 2011 earthquake and tsunami in Japan. We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more directly affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global average. By investigating the evolution of communities, we find that the behavior of joining or quitting a community is far from random: users tend to stay in their current status and are less likely to join new communities from solitary or shift to other communities from their current community. While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users changed their conversations to earthquake-related content. This study builds a systematic framework for investigating human behaviors under extreme events with online social network data and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events.
Network Structure and Community Evolution on Twitter: Human Behavior Change in Response to the 2011 Japanese Earthquake and Tsunami • Xin Lu & Christa Brelsford
What allows a creative enterprise—a film studio, a design firm, a start-up—to flourish? It's an old question, but one that has become increasingly relevant in the transition to an information economy. In the new book Collective…
Intersting exampl eof Vox is a general interest news site for the 21st century. Its mission is simple: Explain the news. Politics, public policy, world affairs, pop culture, science, business, food, sports, and everything else that matters are part of our editorial ambit. Our goal is to move people from curiosity to understanding.
Liz Rykert's insight:
Interesting example of how to present content online in ways that support browsing and exploring and help people go deep when they need or want to.
In his physics laboratory, Ajay Sood watches hundreds of tiny brass pins spontaneously align themselves into flower-shaped patterns in experiments where he’s coaxed non-living matter to behave like flocks of birds flying in synchrony.
The millimetre-sized tapered pins, initially scattered in a disordered manner on a smooth metal surface, jump into action when the surface vibrates and align themselves within seconds into a highly-ordered pattern, as if each pin knows exactly where to go.
The experiments by Sood and his colleagues at the Indian Institute of Science (IISc), Bangalore, and the Tata Institute of Fundamental Research (TIFR) Centre for Interdisciplinary Sciences, Hyderabad, are among the first to display spontaneous alignment of speed and orientation by non-living and non-robotic objects.
The scientists have used their results to construct a new theoretical model of flocking which challenges current ideas that some species of birds, fish and insects display flocking behaviour through the so-called nearest-neighbour effect: individuals merely mimic the movement of nearest neighbours, but the group shows collective behaviour. The findings of the IISc-TIFR team appeared today in the journalNature Communications.
“We’re studying the physics of non-living objects to understand better a phenomenon widely observed in the biological world,” Sood said.
A number of social-ecological systems exhibit complex behavior associated with nonlinearities, bifurcations, and interaction with stochastic drivers. These systems are often prone to abrupt and unexpected instabilities and state shifts that emerge as a discontinuous response to gradual changes in environmental drivers. Predicting such behaviors is crucial to the prevention of or preparation for unwanted regime shifts. Recent research in ecology has investigated early warning signs that anticipate the divergence of univariate ecosystem dynamics from a stable attractor. To date, leading indicators of instability in systems with multiple interacting components have remained poorly investigated. This is a major limitation in the understanding of the dynamics of complex social-ecological networks. Here, we develop a theoretical framework to demonstrate that rising variance—measured, for example, by the maximum element of the covariance matrix of the network—is an effective leading indicator of network instability. We show that its reliability and robustness depend more on the sign of the interactions within the network than the network structure or noise intensity. Mutualistic, scale free and small world networks are less stable than their antagonistic or random counterparts but their instability is more reliably predicted by this leading indicator. These results provide new advances in multidimensional early warning analysis and offer a framework to evaluate the resilience of social-ecological networks.
Early Warning Signs in Social-Ecological Networks.
PLoS ONE 9(7): e101851. doi:10.1371/journal.pone.0101851 (2014)
Recently much attention has been paid to the study of the robustness of interdependent and multiplex networks and, in particular, the networks of networks. The robustness of interdependent networks can be evaluated by the size of a mutually connected component when a fraction of nodes have been removed from these networks. Here we characterize the emergence of the mutually connected component in a network of networks in which every node of a network (layer) alpha is connected with q_alpha its randomly chosen replicas in some other networks and is interdependent of these nodes with probability r. We find that when the superdegrees q_alpha of different layers in a network of networks are distributed heterogeneously, multiple percolation phase transition can occur. We show that, depending on the value of r, these transition are continuous or discontinuous.
It is time to move from innovation as an ideology to innovation as a process.
Liz Rykert's insight:
Important conversation about Innovation in the social sector. Three points highlighted:
"First, innovation is often perceived as a development shortcut; thus innovation becomes overrated. The tremendous value that is created by incremental improvements of the core, routine activities of social sector organizations gets sidelined. Therefore pushing innovation at the expense of strengthening more routine activities may actually destroy rather than create value.
Second, innovation in social sector organizations often has little external impact to show when it is enacted in unpredictable environments. Even proven innovations often fail when transferred to a different context. Yet the cumulative learning from failures may be tremendously valuable in understanding how a particular context ticks. This potentially builds and strengthens an organization’s capacity for productive innovation over time. In other words, if we evaluate innovation primarily by its outcome in the form of external impact, we may undervalue the positive internal organizational impact that comes from learning from failed innovation.
Third, the hoped-for success factors for innovation that researchers and consultants have identified ignore the power of negative organizational factors, such as bad leadership, dysfunctional teams, and overambitious production goals."
In his newest and best book, the surgeon Atul Gawande lets us have it right between the eyes: no matter how careful we are or healthful our habits, like everyone else, we will die, and probably after a long period of decline and debility.
Liz Rykert's insight:
Review and description of the new book by Atul Gawande. Looks very interesting. I feel like these issues of care for the elderly and how we manage our deaths is one that needs so much more thought and attention and action. I am picking up this book based on this review.
I am in an improvisational theater performing group. We improvise full-length plays with nothing planned in advance. No structure. No outline. No character or plot development. Nothing, except for 2 locations we get from the audience at the beginning of...
Liz Rykert's insight:
Love the seven simple rules identified for improv and complex adaptive systems. Thanks to Lisa Kimball for tweeting this story!
Artificial, neurobiological, and social networks are three distinct complex adaptive systems (CASs), each containing discrete processing units (nodes, neurons, and humans, respectively). Despite the apparent differences, these three networks are bound by common underlying principles which describe the behavior of the system in terms of the connections of its components, and its emergent properties. The longevity (long-term retention and functionality) of the components of each of these systems is also defined by common principles. Here, I will examine some properties of the longevity and function of the components of artificial and neurobiological systems, and generalize these to the longevity and function of the components of social CAS. In other words, I will show that principles governing the long-term functionality of computer nodes and of neurons, may be extrapolated to the study of the long-term functionality of humans (or more precisely, of the noemes, an abstract combination of “existence” and “digital fame”). The study of these phenomena can provide useful insights regarding practical ways that can be used to maximize human longevity. The basic law governing these behaviors is the “Law of Requisite Usefulness,” which states that the length of retention of an agent within a CAS is proportional to the agent's contribution to the overall adaptability of the system.
Technological integration and hyperconnectivity: Tools for promoting extreme human lifespans Marios Kyriazis
As long as there have been people watching birds, there have been theories as to how and why they do what they do. In the modern era, theories about why birds flock and why they migrate in v-formations have abounded, yet answers have been few. But new research using creative technology on both starling murmurations and bald ibis’ migration reveals that complex flight dynamics and rapid-fire adjustments based on sensory feedback previously believed impossible for birds are indeed occurring.
We have known for at least 100 years that a brain is organized as a network of connections between nerve cells. But in the last 10 years there has been a rapid growth in our capacity to quantify the complex topological pattern of brain connectivity, using mathematical tools drawn from graph theory. Here we bring together articles and reviews from some of the world’s leading experts in contemporary brain network analysis by graph theory. The contributions are focused on three big questions that seem important at this stage in the scientific evolution of the field: How does the topology of a brain network relate to its physical embedding in anatomical space and its biological costs? How does brain network topology constrain brain dynamics and function? And what seem likely to be important future methodological developments in the application of graph theory to analysis of brain networks? Clearer understanding of the principles of brain network organization is fundamental to understanding many aspects of cognitive function, brain development and clinical brain disorders. We hope this issue provides a forward-looking window on this fast moving field and captures some of the excitement of recent progress in applying the concepts of graph theory to measuring and modeling the complexity of brain networks.
Complex network theory and the brain Issue compiled and edited by David Papo, Javier M. Buldú, Stefano Boccaletti and Edward T. Bullmore
The importance of complexity is well-captured by Hawking's comment: "Complexity is the science of the 21st century". From the movement of flocks of birds to the Internet, environmental sustainability, and market regulation, the study and understanding of complex non-linear systems has become highly influential over the last 30 years.
In this Very Short Introduction, one of the leading figures in the field, John Holland, introduces the key elements and conceptual framework of complexity. From complex physical systems such as fluid flow and the difficulties of predicting weather, to complex adaptive systems such as the highly diverse and interdependent ecosystems of rainforests, he combines simple, well-known examples -- Adam Smith's pin factory, Darwin's comet orchid, and Simon's 'watchmaker' -- with an account of the approaches, involving agents and urn models, taken by complexity theory.
Over the past thirty years, a new systemic conception of life has emerged at the forefront of science. New emphasis has been given to complexity, networks, and patterns of organisation leading to a novel kind of 'systemic' thinking. This volume integrates the ideas, models, and theories underlying the systems view of life into a single coherent framework. Taking a broad sweep through history and across scientific disciplines, the authors examine the appearance of key concepts such as autopoiesis, dissipative structures, social networks, and a systemic understanding of evolution. The implications of the systems view of life for health care, management, and our global ecological and economic crises are also discussed. Written primarily for undergraduates, it is also essential reading for graduate students and researchers interested in understanding the new systemic conception of life and its implications for a broad range of professions - from economics and politics to medicine, psychology and law.