Why haven't education reform efforts amounted to much? Because they start with the wrong problem, says John Abbott, director of the 21st Century Learning Initiative. Overhauling the educational paradigm means replacing the metaphor — the concept of the world and its inhabitants as machine-like entities — that has shaped the education system, as well as many other aspects of our culture.
Creating “Collaborative Learning Communities”
“It is essential to view learning as a total community responsibility,” he says, and to expect no short cuts. Children need to be integrated, fully contributing members of the broader community, so they can feel useful and valued. (It is not just the children who need this, he adds; healthy communities also need children.)
On a practical level, the most powerful lever for change, Abbott says, is people coming together to “rethink the role of community in the learning process,” agreeing how to divide up responsibilities among professional teachers and other community members, and then launching small pilot projects that are true to their new vision. These efforts will build on each other, he says, and large-scale change will follow.
We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.
Conflict with conspecifics from neighbouring groups over territory, mating opportunities and other resources is observed in many social organisms, including humans. Here we investigate the evolutionary origins of social instincts, as shaped by selection resulting from between-group conflict in the presence of a collective action problem. We focus on the effects of the differences between individuals on the evolutionary dynamics. Our theoretical models predict that high-rank individuals, who are able to usurp a disproportional share of resources in within-group interactions, will act seemingly altruistically in between-group conflict, expending more effort and often having lower reproductive success than their low-rank group-mates. Similar behaviour is expected for individuals with higher motivation, higher strengths or lower costs, or for individuals in a leadership position. Our theory also provides an evolutionary foundation for classical equity theory, and it has implications for the origin of coercive leadership and for reproductive skew theory.
A solution to the collective action problem in between-group conflict with within-group inequality • Sergey Gavrilets & Laura Fortunato
Fifty cities around the world began mapping their shared resources in October and November during Shareable's worldwide Map Jam. This is just the beginning of the Sharing Cities Network - an ambitious project to create one hundred sharing cities by 2015.
june holley's insight:
Networks move meta!
The Sharing Cities Network will scale up and replicate successful sharing models by:
It is common in the study of networks to investigate meso-scale features to try to understand network structure and function. For example, numerous algorithms have been developed to try to identify ``communities,'' which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that ``communities'' are associated with bottlenecks of dynamical processes that begin at locally-biased seed sets of nodes, and we employ several different community-identification procedures to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for ``size-resolved community structure'' that can arise in real (and realistic) networks: (i) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (ii) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (iii) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions.
Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks Lucas G. S. Jeub, Prakash Balachandran, Mason A. Porter, Peter J. Mucha, Michael W. Mahoney
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.
The Simple Rules of Social Contagion Nathan O. Hodas & Kristina Lerman
From Crystal Ball to Magic Wand: The New World Order in Times of Digital Revolution. Dirk Helbing, ETH Zurich. Talk delivered via skype on March 24, 2014, to the AAAI workshop on THE INTERSECTION OF ROBUST INTELLIGENCE AND TRUST IN AUTONOMOUS SYSTEMS
We need another Apollo project, but this time focusing on our Earth. I am ready for this, are you?
Please watch this movie to the end. The solution to our world's problems is different from what many strategic thinkers believed.
Community detection in online social networks is typically based on the analysis of the explicit connections between users, such as "friends" on Facebook and "followers" on Twitter. But online users often have hundreds or even thousands of such connections, and many of these connections do not correspond to real friendships or more generally to accounts that users interact with. We claim that community detection in online social networks should be question-oriented and rely on additional information beyond the simple structure of the network. The concept of 'community' is very general, and different questions such as "who do we interact with?" and "with whom do we share similar interests?" can lead to the discovery of different social groups. In this paper we focus on three types of communities beyond structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that the communities obtained in the three weighted cases are highly different from each other, and from the communities obtained by considering only the unweighted structural network. Our results confirm that asking a precise question is an unavoidable first step in community detection in online social networks, and that different questions can lead to different insights into the network under study.
Followers Are Not Enough: Beyond Structural Communities in Online Social Networks David Darmon, Elisa Omodei, Joshua Garland
One of the world's leading authorities on the digital revolution, Don Tapscott, shares the vital qualities and characteristics of our digitally connected world. He will explain how the power of networks will radically transform our ability to solve global challenges, the way states are governed and how the next generation will live and work in the future. You are invited to learn from his personal journey of profound discovery.
Noise permeates biology on all levels, from the most basic molecular, sub-cellular processes to the dynamics of tissues, organs, organisms and populations. The functional roles of noise in biological processes can vary greatly. Along with standard, entropy-increasing effects of producing random mutations, diversifying phenotypes in isogenic populations, limiting information capacity of signaling relays, it occasionally plays more surprising constructive roles by accelerating the pace of evolution, providing selective advantage in dynamic environments, enhancing intracellular transport of biomolecules and increasing information capacity of signaling pathways. This short review covers the recent progress in understanding mechanisms and effects of fluctuations in biological systems of different scales and the basic approaches to their mathematical modeling.
Here are resources to help those interested in the positive deviance (PD) approach to social, organisational, and individual behaviour change. The PD approach is premised on the belief that, in every community, there are certain individuals or groups whose uncommon behaviours and strategies enable them to find better solutions to problems than their peers, while having access to the same resources and often facing more daunting challenges.