Professor and researcher Carol Dweck recently gave a TEDx Talk shared by TED titled “The power of believing that you can improve.” I’ve embedded it below, but you can also see it on the TED site at the previous link.
We present new empirical evidence, based on millions of interactions on Twitter, confirming that human contacts scale with population sizes. We integrate such observations into a reaction-diffusion metapopulation framework providing an analytical expression for the global invasion threshold of a contagion process. Remarkably, the scaling of human contacts is found to facilitate the spreading dynamics. Our results show that the scaling properties of human interactions can significantly affect dynamical processes mediated by human contacts such as the spread of diseases, and ideas.
The Scaling of Human Contacts in Reaction-Diffusion Processes on Heterogeneous Metapopulation Networks Michele Tizzoni, Kaiyuan Sun, Diego Benusiglio, Márton Karsai, Nicola Perra
While ideas may appear to be only in your head, it turns out they're super social: Darwin talked about evolution for decades before publishing the Origin of Species. And while it might be you that gets the promotion, it's your connections up, down…
In complex systems, the network of interactions we observe between system's components is the aggregate of the interactions that occur through different mechanisms or layers. Recent studies reveal that the existence of multiple interaction layers can have a dramatic impact in the dynamical processes occurring on these systems. However, these studies assume that the interactions between systems components in each one of the layers are known, while typically for real-world systems we do not have that information. Here, we address the issue of uncovering the different interaction layers from aggregate data by introducing multilayer stochastic block models (SBMs), a generalization of single-layer SBMs that considers different mechanisms of layer aggregation. First, we find the complete probabilistic solution to the problem of finding the optimal multilayer SBM for a given aggregate observed network. Because this solution is computationally intractable, we propose an approximation that enables us to verify that multilayer SBMs are more predictive of network structure in real-world complex systems.
Multilayer stochastic block models reveal the multilayer structure of complex networks Toni Valles-Catala, Francesco A. Massucci, Roger Guimera, Marta Sales-Pardo http://arxiv.org/abs/1411.1098
The last decade and a half has seen an ardent development of self-organised criticality (SOC), a new approach to complex systems, which has become important in many domains of natural as well as social science, such as geology, biology, astronomy, and economics, to mention just a few. This has led many to adopt a generalist stance towards SOC, which is now repeatedly claimed to be a universal theory of complex behaviour. The aim of this paper is twofold. First, I provide a brief and non-technical introduction to SOC. Second, I critically discuss the various bold claims that have been made in connection with it. Throughout, I will adopt a rather sober attitude and argue that some people have been too readily carried away by fancy contentions. My overall conclusion will be that none of these bold claims can be maintained. Nevertheless, stripped of exaggerated expectations and daring assertions, many SOC models are interesting vehicles for promising scientific research.
Jon Husband is an old friend, and I have been planning to involve him in Socialogy since I started the project, but the timing hadn't worked out until now. Jon describes himself in this way: I am a coach, consultant, writer and public speaker…
june holley's insight:
1. Adequate elbow room.
The sense that we are our own boss and that, except in exceptional circumstances, we do not have some boss breathing down our necks. However, not too much elbow room so that we don’t know what to do next.
2. Continuous Learning.
Such learning is possible only when people are able to (a) set goals that are reasonable challenges for them and (b) get accurate feedback in time for them to correct their behaviour. This learning drives innovation.
3. An optimal level of variety.
The ability to vary our work so as to avoid boredom and fatigue and so as to gain the best advantages from settling into a satisfying rhythm of work.
4. Mutual support and respect.
People need to be able to automatically get and give help from their work mates. There also needs to be respect for the contribution made regardless of matters such as IQ.
We need a sense that our work contributes to social welfare in some way. That is, it should not be something that might just as well be done by a trained monkey. Nor should it be something that society would be better without. Meaningfulness includes both the worth of the work, and having knowledge of the whole product or service.
6. A desirable future.
Work that will continue to allow for personal growth and increasing skills.
I heard IBM’s general manager of design speak at an IBM event in New York City recently, and I was impressed by his observations about trying to inject design thinking into IBM’s product planning. In...
By W. Brian Arthur; External Professor, Santa Fe Institute; Visiting Researcher, Palo Alto Research Center.
Economics is a stately subject, one that has altered little since its modern foundations were laid in Victorian times. Now it is changing radically. Standard economics is suddenly being challenged by a number of new approaches: behavioral economics, neuroeconomics, new institutional economics. One of the new approaches came to life at the Santa Fe Institute: complexity economics.
Complexity economics got its start in 1987 when a now-famous conference of scientists and economists convened by physicist Philip Anderson and economist Kenneth Arrow met to discuss the economy as an evolving complex system. That conference gave birth a year later to the Institute’s first research program – the Economy as an Evolving Complex System – and I was asked to lead this. That program in turn has gone on to lay down a new and different way to look at the economy.
Understanding “New Power” Power clearly isn't what it used to be. We see Goliaths being toppled by Davids all around us, from the networked drivers of Uber to the crowdfunded creatives of Kickstarter. But it's difficult to understand…
Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone. However, understanding why cooperation arises despite the natural tendency of individuals toward selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics. Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.
Evolutionary dynamics of time-resolved social interactions Phys. Rev. E 90, 052825 – Published 25 November 2014 Alessio Cardillo, Giovanni Petri, Vincenzo Nicosia, Roberta Sinatra, Jesús Gómez-Gardeñes, and Vito Latora