Emergence is a phenomenon taken for granted in science but also still not well understood. We have developed a model of artificial genetic evolution intended to allow for emergence on genetic, population and social levels. We present the details of the current state of our environment, agent, and reproductive models. In developing our models we have relied on a principle of using non-linear systems to model as many systems as possible including mutation and recombination, gene-environment interaction, agent metabolism, agent survival, resource gathering and sexual reproduction. In this paper we review the genetic dynamics that have emerged in our system including genotype-phenotype divergence, genetic drift, pseudogenes, and gene duplication. We conclude that emergence-focused design in complex system simulation is necessary to reproduce the multilevel emergence seen in the natural world.
Emergence-focused design in complex system simulation Chris Marriott, Jobran Chebib
The importance of networks for social-ecological processes has been recognized in the literature; however, existing studies have not sufficiently addressed the dynamic nature of networks. Using data on the social learning networks of 265 farmers in Ethiopia for 2011 and 2012 and stochastic actor-oriented modeling, we explain the mechanisms of network evolution and soil conservation. The farmers’ preferences for information exchange within the same social groups support the creation of interactive, clustered, nonhierarchical structures within the evolving learning networks, which contributed to the diffusion of the practice of composting. The introduced methods can be applied to determine whether and how social networks can be used to facilitate environmental interventions in various contexts.
Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network.
The Majority Illusion in Social Networks Kristina Lerman, Xiaoran Yan, Xin-Zeng Wu
What Makes an Organization “Networked”? In 1904, the great sociologist Max Weber visited the United States. As Moises Naim describes in The End of Power, travelling around the vast country for three months, he believed that it…
The mix of products that a country exports predicts that country's subsequent pattern of diversification and economic growth. But does this product mix also predict income inequality? Here we combine methods from econometrics, network science, and economic complexity to show that countries that export complex products - products that are exported by a few diversified countries - have lower levels of income inequality - at comparable levels of GDP per capita and education - than countries exporting simpler products. Using multivariate analysis we show that the connection between income inequality and economic complexity is stronger than what can be explained using aggregate measures of income, institutions, export concentration, and human capital, and also, that increases in economic complexity are accompanied by decreases in income inequality over long periods of time. Finally, we use the position of a country in the network of related products - or product space - to explain how changes in a country's export structure translate into changes in income inequality. We interpret these results by combining the literature in institutions with that on economic complexity and structural transformations. We argue that the connection between income inequality and economic complexity is also evidence of the co-evolution between institutions and productive activities.
Linking Economic Complexity, Institutions and Income Inequality D. Hartmann, M. Guevara, C. Jara-Figueroa, M. Aristarán, C.A. Hidalgo
Recent grassroots movements have suggested that online social networks might play a key role in their organization, as adherents have a fast, many-to-many, communication channel to help coordinate their mobilization. The structure and dynamics of the networks constructed from the digital traces of protesters have been analyzed to some extent recently. However, less effort has been devoted to the analysis of the semantic content of messages exchanged during the protest. Using the data obtained from a microblogging service during the brewing and active phases of the 15M movement in Spain, we perform the first large scale test of theories on collective emotions and social interaction in collective actions. Our findings show that activity and information cascades in the movement are larger in the presence of negative collective emotions and when users express themselves in terms related to social content. At the level of individual participants, our results show that their social integration in the movement, as measured through social network metrics, increases with their level of engagement and of expression of negativity. Our findings show that non-rational factors play a role in the formation and activity of social movements through online media, having important consequences for viral spreading.
Sentiment cascades in the 15M movement Alvarez R, Garcia D, Moreno Y, Schweitzer F EPJ Data Science 2015, 4 :6 (30 May 2015)
The adaptive immune system uses the experience of past infections to prepare its limited repertoire of specialized receptors to protect organisms from future threats. What is the best way of doing this? Building a theoretical framework from first principles, we predict the composition of receptor repertoires that are optimally adapted to minimize the cost of infections from a given pathogenic environment. A naive repertoire can reach these optima through a biologically plausible competitive mechanism. Our findings explain how limited populations of immune receptors can self-organize to provide effective immunity against highly diverse pathogens. Our results also inform the design and interpretation of experiments surveying immune repertoires.
How a well-adapted immune system is organized Andreas Mayer, Vijay Balasubramanian, Thierry Mora, and Aleksandra M. Walczak
We perform a multifractal analysis of the evolution of London's street network from 1786 to 2010. First, we show that a single fractal dimension, commonly associated with the morphological description of cities, does not su ce to capture the dynamics of the system. Instead, for a proper characterization of such a dynamics, the multifractal spectrum needs to be considered. Our analysis reveals that London evolves from an inhomogeneous fractal structure, that can be described in terms of a multifractal, to a homogeneous one, that converges to monofractality. We argue that London's multifractal to monofracal evolution might be a special outcome of the constraint imposed on its growth by a green belt. Through a series of simulations, we show that multifractal objects, constructed through di usion limited aggregation, evolve towards monofractality if their growth is constrained by a non-permeable boundary.
Multifractal to monofractal evolution of the London's street network Roberto Murcio, A. Paolo Masucci, Elsa Arcaute, Michael Batty
The present study asks how cooperation and consequently structure can emerge in many different evolutionary contexts. Cooperation, here, is a persistent behavioural pattern of individual entities pooling and sharing resources. Examples are: individual cells forming multicellular systems whose various parts pool and share nutrients; pack animals pooling and sharing prey; families firms, or modern nation states pooling and sharing financial resources. In these examples, each atomistic decision, at a point in time, of the better-off entity to cooperate poses a puzzle: the better-off entity will book an immediate net loss -- why should it cooperate? For each example, specific explanations have been put forward. Here we point out a very general mechanism -- a sufficient null model -- whereby cooperation can evolve. The mechanism is based the following insight: natural growth processes tend to be multiplicative. In multiplicative growth, ergodicity is broken in such a way that fluctuations have a net-negative effect on the time-average growth rate, although they have no effect on the growth rate of the ensemble average. Pooling and sharing resources reduces fluctuations, which leaves ensemble averages unchanged but -- contrary to common perception -- increases the time-average growth rate for each cooperator.
The evolutionary advantage of cooperation Ole Peters, Alexander Adamou
The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure.
The anatomy of urban social networks and its implications in the searchability problem • C. Herrera-Yagüe, C. M. Schneider, T. Couronné, Z. Smoreda, R. M. Benito, P. J. Zufiria & M. C. González
A substantial volume of research has been devoted to studies of community structure in networks, but communities are not the only possible form of large-scale network structure. Here we describe a broad extension of community structure that encompasses traditional communities but includes a wide range of generalized structural patterns as well. We describe a principled method for detecting this generalized structure in empirical network data and demonstrate with real-world examples how it can be used to learn new things about the shape and meaning of networks.
Generalized communities in networks M. E. J. Newman, Tiago P. Peixoto
Diffusion of information, behavioural patterns or innovations follows diverse pathways depending on a number of conditions, including the structure of the underlying social network, the sensitivity to peer pressure and the influence of media. Here we study analytically and by simulations a general model that incorporates threshold mechanism capturing sensitivity to peer pressure, the effect of `immune' nodes who never adopt, and a perpetual flow of external information. While any constant, non-zero rate of dynamically-introduced innovators leads to global spreading, the kinetics by which the asymptotic state is approached show rich behaviour. In particular we find that, as a function of the density of immune nodes, there is a transition from fast to slow spreading governed by entirely different mechanisms. This transition happens below the percolation threshold of fragmentation of the network, and has its origin in the competition between cascading behaviour induced by innovators and blocking of adoption due to immune nodes. This change is accompanied by a percolation transition of the induced clusters.
Kinetics of Social Contagion Zhongyuan Ruan, Gerardo Iniguez, Marton Karsai, Janos Kertesz
Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed and further studied users’ choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals’ choices played a stronger role in limiting exposure to cross-cutting content.
Exposure to ideologically diverse news and opinion on Facebook Eytan Bakshy, Solomon Messing, Lada A. Adamic
The determination of the most central agents in complex networks is important because they are responsible for a faster propagation of information, epidemics, failures and congestion, among others. A challenging problem is to identify them in networked systems characterized by different types of interactions, forming interconnected multilayer networks. Here we describe a mathematical framework that allows us to calculate centrality in such networks and rank nodes accordingly, finding the ones that play the most central roles in the cohesion of the whole structure, bridging together different types of relations. These nodes are the most versatile in the multilayer network. We investigate empirical interconnected multilayer networks and show that the approaches based on aggregating—or neglecting—the multilayer structure lead to a wrong identification of the most versatile nodes, overestimating the importance of more marginal agents and demonstrating the power of versatility in predicting their role in diffusive and congestion processes.
Ranking in interconnected multilayer networks reveals versatile nodes Manlio De Domenico, Albert Solé-Ribalta, Elisa Omodei, Sergio Gómez & Alex Arenas
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.