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
We show that strategies implemented in automatic theorem proving involve an interesting tradeoff between execution speed, proving speedup/computational time and usefulness of information. We advance formal definitions for these concepts by way of a notion of normality related to an expected (optimal) theoretical speedup when adding useful information (other theorems as axioms), as compared with actual strategies that can be effectively and efficiently implemented. We propose the existence of an ineluctable tradeoff between this normality and computational time complexity. The argument quantifies the usefulness of information in terms of (positive) speed-up. The results disclose a kind of no-free-lunch scenario and a tradeoff of a fundamental nature. The main theorem in this paper together with the numerical experiment---undertaken using two different automatic theorem provers AProS and Prover9 on random theorems of propositional logic---provide strong theoretical and empirical arguments for the fact that finding new useful information for solving a specific problem (theorem) is, in general, as hard as the problem (theorem) itself.
Rare Speed-up in Automatic Theorem Proving Reveals Tradeoff Between Computational Time and Information Value Santiago Hernández-Orozco, Francisco Hernández-Quiroz, Hector Zenil, Wilfried Sieg
Ongoing greenhouse gas emissions can alter climate suitability for plant growth, in turn affecting biological and social systems. Using the latest generation of available climate projections we show that there will be fewer days with suitable climates for plant growth, despite an increase in days above freezing. This decline in suitable plant growing days is due to interactions among unsuitable temperatures, light, and water availability. Our analysis shows that reductions in suitable plant growing days will be most pronounced in tropical areas and in countries that are among the poorest and most highly dependent on plant-related goods and services. Changes in suitable plant growing days will be less severe under strong and moderate mitigation scenarios, highlighting the importance of reducing emissions to ameliorate the biological and social impacts of these changes.
Mora C, Caldwell IR, Caldwell JM, Fisher MR, Genco BM, Running SW (2015) Suitable Days for Plant Growth Disappear under Projected Climate Change: Potential Human and Biotic Vulnerability. PLoS Biol 13(6): e1002167. http://dx.doi.org/10.1371/journal.pbio.1002167 ;
Cities and their transportation systems become increasingly complex and multimodal as they grow, and it is natural to wonder if it is possible to quantitatively characterize our difficulty to navigate in them and whether such navigation exceeds our cognitive limits. A transition between different searching strategies for navigating in metropolitan maps has been observed for large, complex metropolitan networks. This evidence suggests the existence of another limit associated to the cognitive overload and caused by large amounts of information to process. In this light, we analyzed the world's 15 largest metropolitan networks and estimated the information limit for determining a trip in a transportation system to be on the order of 8 bits. Similar to the "Dunbar number," which represents a limit to the size of an individual's friendship circle, our cognitive limit suggests that maps should not consist of more than about 250 connections points to be easily readable. We also show that including connections with other transportation modes dramatically increases the information needed to navigate in multilayer transportation networks: in large cities such as New York, Paris, and Tokyo, more than 80% of trips are above the 8-bit limit. Multimodal transportation systems in large cities have thus already exceeded human cognitive limits and consequently the traditional view of navigation in cities has to be revised substantially.
Information measures and cognitive limits in multilayer navigation Riccardo Gallotti, Mason A. Porter, Marc Barthelemy
Today’s mobile devices offer a variety of computational, memory, storage, communication and sensing resources. In addition, mobile communication technologies are continuously evolving and mobile networks are becoming more and more complex. Modern mobile devices are capable of supporting a wide range of new innovative applications from real-time location-based tracking to mobile gaming. However, the usage of power-hungry applications, sensors and their demand for 24/7 Internet connectivity requires an efficient energy management mechanism in mobile devices. With the increasing energy limitations, there has been a corresponding rise of energy management solutions proposed by researchers. However, this research area is still immature and existing literature lacks the critical review of recent self organization based energy management techniques. This paper aims to provide a structured overview of the research developments on self organization based energy management techniques used in mobile complex networks. This review paper surveys the state-of-the-art self organization based energy management techniques that have been proposed over the period of 2010–2015. Based on the proposed optimization, we have grouped the existing approaches in different categories, which are further classified at different levels, from energy-efficient operating systems to computation off-loading. With this classification we aim to provide an easy and summarized view of the latest self organization based energy management techniques that can be implemented in mobile devices.
Self organization based energy management techniques in mobile complex networks: a review Bahjat Fatima and Munam Ali Shah
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)
Multicellular eukaryotes can perform functions that exceed the possibilities of an individual cell. These functions emerge through interactions between differentiated cells that are precisely arranged in space. Bacteria also form multicellular collectives that consist of differentiated but genetically identical cells. How does the functionality of these collectives depend on the spatial arrangement of the differentiated bacteria? In a previous issue of PLOS Biology, van Gestel and colleagues reported an elegant example of how the spatial arrangement of differentiated cells gives rise to collective behavior in Bacillus subtilus colonies, further demonstrating the similarity of bacterial collectives to higher multicellular organisms.
Termites, like many social insects, build nests of complex architecture. These constructions have been proposed to optimize different structural features. Here we describe the nest network of the termite Nasutitermes ephratae, which is among the largest nest-network reported for termites and show that it optimizes diverse parameters defining the network architecture. The network structure avoids multiple crossing of galleries and minimizes the overlap of foraging territories. Thus, these termites are able to minimize the number of galleries they build, while maximizing the foraging area available at the nest mounds. We present a simple computer algorithm that reproduces the basics characteristics of this termite nest network, showing that simple rules can produce complex architectural designs efficiently.
Emergence, self-organization and network efficiency in gigantic termite-nest-networks build using simple rules Diego Griffon, Carmen Andara, Klaus Jaffe
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
For centuries quality of life was a subject of studies across different disciplines. However, only with the emergence of a digital era, it became possible to investigate this topic on a larger scale. Over time it became clear that quality of life not only depends on one, but on three relatively different parameters: social, economic and well-being measures. In this study we focus only on the first two, since the last one is often very subjective and consequently hard to measure. Using a complete set of bank card transactions recorded by Banco Bilbao Vizcaya Argentaria (BBVA) during 2011 in Spain, we first create a feature space by defining various meaningful characteristics of a particular area performance through activity of its businesses, residents and visitors. We then evaluate those quantities by considering available official statistics for Spanish provinces (e.g., housing prices, unemployment rate, life expectancy) and investigate whether they can be predicted based on our feature space. For the purpose of prediction, our study proposes a supervised machine learning approach. Our finding is that there is a clear correlation between individual spending behavior and official socioeconomic indexes denoting quality of life. Moreover, we believe that this modus operandi is useful to understand, predict and analyze the impact of human activity on the wellness of our society on scales for which there is no consistent official statistics available (e.g., cities and towns, districts or smaller neighborhoods).
Predicting Regional Economic Indices using Big Data of Individual Bank Card Transactions Stanislav Sobolevsky, Emanuele Massaro, Iva Bojic, Juan Murillo Arias, Carlo Ratti
We extend the concept of graph isomorphisms to multilayer networks, and we identify multiple types of isomorphisms. For example, in multilayer networks with a single "aspect" (i.e., type of layering), permuting vertex labels, layer labels, and both of types of layers each yield a different type of isomorphism. We discuss how multilayer network isomorphisms naturally lead to defining isomorphisms in any type of network that can be represented as a multilayer network. This thereby yields isomorphisms for multiplex networks, temporal networks, networks with both such features, and more. We reduce each of the multilayer network isomorphism problems to a graph isomorphism problem, and we use this reduction to prove that the multilayer network isomorphism problem is computationally equally hard as the graph isomorphism problem. One can thus use software that has been developed to solve graph isomorphism problems as a practical means for solving multilayer network isomorphism problems.
Isomorphisms in Multilayer Networks Mikko Kivelä, Mason A. Porter
Since December 2006, more than a thousand cities in México have suffered the effects of the war between several drug cartels, amongst themselves, as well as with Mexican armed forces. Sources are not in agreement about the number of casualties of this war, with reports varying from 30 to 100 thousand dead; the economic and social ravages are impossible to quantify. In this work we analyze the official report of casualties in terms of the location and the date of occurrence of the homicides. We show how the violence, as reflected by the number of casualties, has increased over time and spread across the country. Next, based on the correlations between cities in the changes of the monthly number of casualties attributed to organized crime, we construct a narco-war network where nodes are the affected cities and links represent correlations between them. We find that close geographical distance between violent cities does not imply a strong correlation amongst them. We observe that the dynamics of the conflict has evolved in short-term periods where a small core of violent cities determines the main theatre of the war at each stage. This kind of analysis may also help to describe the emergence and propagation of gang-related violence waves.
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
Since Newton we have sought laws that “entail” the evolution of the system. These dreams range from reductionism, dreams of a final entailing theory, upward. In this chapter I hope to show that no laws at all entail the becoming of the biosphere. Ever new, typically unprestatable, biological functions arise, often as Darwinian preadaptations, and once they exist, they do not cause, but ENABLE an often unprestatable set of “opportunities” forming a new “adjacent possible” into which evolution flows, creating yet new adaptations that enable new adjacent possibles in an unprestatable becoming. Because we cannot prestate the variables, we can write no differential equation laws of motion for evolution, so cannot integrate those equations. Thus no laws entail evolution. Since the biosphere is part of the universe, if the above is correct, there can be no final theory that entails all that becomes in the universe. The discussion rests on the legitimacy of “functions” in biology, subsets of the causal consequences of parts of organisms. Physics cannot distinguish between causal consequences. I try to justify “functions”, whose unprestatable becoming are parts of the ever changing phase space of evolution, hence no entailing laws. “Functions” are justified in the non-ergodic universe above the level of atoms by Kantian wholes such as collectively autocatalytic sets in protocells that can sense, evaluate, and act in their worlds, yielding teleonomy and biosemiotics. Modernity is based on Newton and Darwin: these ideas may take us beyond Modernity.
Urbanization promotes economy, mobility, access and availability of resources, but on the other hand, generates higher levels of pollution, violence, crime, and mental distress. The health consequences of the agglomeration of people living close together are not fully understood. Particularly, it remains unclear how variations in the population size across cities impact the health of the population. We analyze the deviations from linearity of the scaling of several health-related quantities, such as the incidence and mortality of diseases, external causes of death, wellbeing, and health-care availability, in respect to the population size of cities in Brazil, Sweden and the USA. We find that deaths by non-communicable diseases tend to be relatively less common in larger cities, whereas the per-capita incidence of infectious diseases is relatively larger for increasing population size. Healthier life style and availability of medical support are disproportionally higher in larger cities. The results are connected with the optimization of human and physical resources, and with the non-linear effects of social networks in larger populations. An urban advantage in terms of health is not evident and using rates as indicators to compare cities with different population sizes may be insufficient.
The non-linear health consequences of living in larger cities Luis E. C. Rocha, Anna E. Thorson, Renaud Lambiotte
We present a new Life-like cellular automaton (CA) capable of logic universality -- the X-rule. The CA is 2D, binary, with a Moore neighborhood and λ parameter similar to the game-of-Life, but is not based on birth/survival and is non-isotropic. We outline the search method. Several glider types and stable structures emerge spontaneously within X-rule dynamics. We construct glider-guns based on periodic oscillations between stable barriers, and interactions to create logical gates.
The X-rule: universal computation in a non-isotropic Life-like Cellular Automaton José Manuel Gómez Soto, Andrew Wuensche
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
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
Common decision-making models arise from firm axiomatic foundations but do not account for a variety of empirically observed choice patterns such as risk attitudes in the face of high-impact events. Here, we argue that one reason for this mismatch between theory and data lies in the neglect of basic biological principles such as metabolic homeostasis. We use Bayesian model comparison to show that models based on homeostatic considerations explain human decisions better than classic economic models—both in a novel virtual foraging task and in standard economic gambles. Specifically, we show that in line with the principle of homeostasis human choice minimizes the probability of reaching a lower bound. Our results highlight that predictions from biological principles provide simple, testable, and ecologically rational explanations for apparent biases in decision-making.
In social networks, individuals constantly drop ties and replace them by new ones in a highly unpredictable fashion. This highly dynamical nature of social ties has important implications for processes such as the spread of information or of epidemics. Several studies have demonstrated the influence of a number of factors on the intricate microscopic process of tie replacement, but the macroscopic long-term effects of such changes remain largely unexplored. Here we investigate whether, despite the inherent randomness at the microscopic level, there are macroscopic statistical regularities in the long-term evolution of social networks. In particular, we analyze the email network of a large organization with over 1,000 individuals throughout four consecutive years. We find that, although the evolution of individual ties is highly unpredictable, the macro-evolution of social communication networks follows well-defined statistical laws, characterized by exponentially decaying log-variations of the weight of social ties and of individuals' social strength. At the same time, we find that individuals have social signatures and communication strategies that are remarkably stable over the scale of several years.
Long-term evolution of techno-social networks: Statistical regularities, predictability and stability of social behaviors Antonia Godoy-Lorite, Roger Guimera, Marta Sales-Pardo
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
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
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
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