There is a long history of scientific inquiry about what role biological sex plays in differences between brain function in human males and females. Greater knowledge of the influence of biological sex on the human brain promises much-needed insights into brain function and especially dysfunctions that differentially affect the sexes (1). Certainly, advancing technologies and an increasing wealth of data (with more sophisticated analyses) should prompt robust future research—carefully conducted and well replicated—that can elucidate sex effects in the brain. However, this field of research has spurred an equally long history of debate as to whether inherent differences in brains of males and females predispose the sexes to stereotypical behaviors, or whether such claims reinforce and legitimate traditional gender stereotypes and roles in ways that are not scientifically justified—so-called neurosexism. Although this topic remains controversial, a commonly held belief is that the psyches of females and males are highly distinct. These differences are perceived as natural, fixed, and invariant across time and place (2), presumably due to unique female versus male brain circuitry that is largely fixed by a sexually differentiated genetic blueprint. A major challenge in the field is to crtically view previous experimental findings, as well as design future studies, outside the framework of this dichotomous model. Here, gender scholarship can hasten scientific progress by revealing the implicit assumptions that can give rise to inadvertent neurosexism.
Containing the spreading of crime in urban societies remains a major challenge. Empirical evidence suggests that, left unchecked, crimes may be recurrent and proliferate. On the other hand, eradicating a culture of crime may be difficult, especially under extreme social circumstances that impair the creation of a shared sense of social responsibility. Although our understanding of the mechanisms that drive the emergence and diffusion of crime is still incomplete, recent research highlights applied mathematics and methods of statistical physics as valuable theoretical resources that may help us better understand criminal activity. We review different approaches aimed at modeling and improving our understanding of crime, focusing on the nucleation of crime hotspots using partial differential equations, self-exciting point process and agent-based modeling, adversarial evolutionary games, and the network science behind the formation of gangs and large-scale organized crime. We emphasize that statistical physics of crime can relevantly inform the design of successful crime prevention strategies, as well as improve the accuracy of expectations about how different policing interventions should impact malicious human activity deviating from social norms. We also outline possible directions for future research, related to the effects of social and coevolving networks and to the hierarchical growth of criminal structures due to self-organization.
Statistical physics of crime: A review Maria R. D'Orsogna, Matjaz Perc
The dynamics of close relationships is important for understanding the migration patterns of individual life-courses. The bottom-up approach to this subject by social scientists has been limited by sample size, while the more recent top-down approach using large-scale datasets suffers from a lack of detail about the human individuals. We incorporate the geographic and demographic information of millions of mobile phone users with their communication patterns to study the dynamics of close relationships and its effect in their life-course migration. We demonstrate how the close age- and sex-biased dyadic relationships are correlated with the geographic proximity of the pair of individuals, e.g., young couples tend to live further from each other than old couples. In addition, we find that emotionally closer pairs are living geographically closer to each other. These findings imply that the life-course framework is crucial for understanding the complex dynamics of close relationships and their effect on the migration patterns of human individuals.
Spatial patterns of close relationships across the lifespan • Hang-Hyun Jo, Jari Saramäki, Robin I. M. Dunbar & Kimmo Kaski
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
Shame has biological roots, possibly enhancing trust, favoring social cohesion. We studied bioeconomic aspects of shame and guilt using three approaches: 1—Anthropo-linguistic studies of Guilt and Shame among the Yanomami, a culturally isolated traditional tribal society; 2—Estimates of the importance different languages assign to the concepts Shame, Guilt, Pain, Embarrassment, Fear and Trust, counting the number of synonyms listed by Google Translate; 3—Quantitative correlations between this linguistic data with socioeconomic indexes. Results showed that Yanomami is unique in having overlapping synonyms for Shame, Fear and Embarrassment. No language had overlapping synonyms for Shame andGuilt. Societies previously described as “Guilt Societies” have more synonyms for Guilt than for Shame. A large majority of languages, including those from societies previously described as “Shame Societies”, have more words for Shame than for Guilt. The number of synonyms for Guilt and Shame strongly correlated with estimates of corruption, ease of doing business and governance, but not with levels of interpersonal trust. We propose that cultural evolution of shame has continued the work of biological evolution, but its adaptive advantageto society is still unclear. Results suggest that recent cultural evolution must be responsible for the relationship between the levels of corruption of a society and the number of synonyms for Guilt and Shame in its language. This opens a novel window for the study of complex interactions between biological and cultural evolution of cognition and emotions, which might help broaden our insight into bioeconomics.
On the bioeconomics of shame and guilt Klaus Jaffe, Astrid Flórez, Marcos Manzanares, Rodolfo Jaffe, Cristina M. Gomes, Daniel Rodríguez, Carla Achury
Companies selling ‘probiotic’ foods have long claimed that cultivating the right gut bacteria can benefit mental well-being, but neuroscientists have generally been sceptical. Now there is hard evidence linking conditions such as autism and depression to the gut’s microbial residents, known as the microbiome. And neuroscientists are taking notice — not just of the clinical implications but also of what the link could mean for experimental design.
Recent wide-spread adoption of electronic and pervasive technologies has enabled the study of human behavior at an unprecedented level, uncovering universal patterns underlying human activity, mobility, and inter-personal communication. In the present work, we investigate whether deviations from these universal patterns may reveal information about the socio-economical status of geographical regions. We quantify the extent to which deviations in diurnal rhythm, mobility patterns, and communication styles across regions relate to their unemployment incidence. For this we examine a country-scale publicly articulated social media dataset, where we quantify individual behavioral features from over 145 million geo-located messages distributed among more than 340 different Spanish economic regions, inferred by computing communities of cohesive mobility fluxes. We find that regions exhibiting more diverse mobility fluxes, earlier diurnal rhythms, and more correct grammatical styles display lower unemployment rates. As a result, we provide a simple model able to produce accurate, easily interpretable reconstruction of regional unemployment incidence from their social-media digital fingerprints alone. Our results show that cost-effective economical indicators can be built based on publicly-available social media datasets.
Social media fingerprints of unemployment Alejandro Llorente, Manuel Garc\'\ia-Herranz, Manuel Cebrian, Esteban Moro
When electrons or atoms or individuals or societies interact with one another or their environment, the collective behavior of the whole is different from that of its parts. We call this resulting behavior emergent. Emergence thus refers to collective phenomena or behaviors in complex adaptive systems that are not present in their individual parts.
By David Pines, Co-Founder in Residence, Santa Fe Institute
The overwhelming success of the web 2.0, with online social networks as key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of these services for the first time has allowed researchers to quantify large-scale social patterns. However, the mechanisms that determine the fate of networks at a system level are still poorly understood. For instance, the simultaneous existence of numerous digital services naturally raises the question under which conditions these services can coexist. In analogy to population dynamics, the digital world is forming a complex ecosystem of interacting networks whose fitnesses depend on their ability to attract and maintain users' attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits a stable coexistence of several networks as well as the domination of a single one, in contrast to the principle of competitive exclusion. Interestingly, our model also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.
Ecology 2.0: Coexistence and Domination among Interacting Networks Kaj Kolja Kleineberg, Marián Boguñá
Our planet is experiencing an accelerated process of change associated to a variety of anthropogenic-related processes. Climate change and biodiversity decline are two facets of this phenomenon. The future of this transformation is uncertain, but there is general agreement about its negative unfolding that might threaten our own survival. Additionally, the pace of the expected changes is likely to be abrupt: catastrophic shifts might be the most likely outcome of this ongoing, apparently slow process. Although different strategies for geo-engineering the planet have been advanced, none seem likely to safely revert the large-scale problems associated to carbon dioxide accumulation or ecosystem degradation. An alternative possibility considered here is inspired in the rapidly growing potential for engineering living systems. It would involve designing synthetic organisms capable of reproducing and expanding to large geographic scales with the goal of achieving a long-term or a transient restoration of ecosystem-level homeostasis. Such a regional or even planetary-scale engineering would have to deal with the complexity of our biosphere. It will require not only a proper design of organisms but also understanding their place within ecological networks and their evolvability. This is a likely future scenario that will require integration of ideas coming from currently weakly connected domains, including synthetic biology, ecological and genome engineering, evolutionary theory, climate science, biogeography and invasion ecology, among others.
Each one of us may encounter several different strains of the ever-changing influenza virus during a lifetime. Scientists can now summarize such histories of infection over a lifetime of exposure. Fonville et al. visualize the interplay between protective responses and the evasive influenza virus by a technique called antibody landscape modeling (see the Perspective by Lessler). Landscapes reveal how exposure to new strains of the virus boost immune responses and indicate possibilities for optimizing future vaccination programs.
Antibody landscapes after influenza virus infection or vaccination J. M. Fonville et al.
The emergence of the Internet as the primary medium for information exchange has led to the development of many decentralized sharing systems. The most popular among them, BitTorrent, is used by tens of millions of people monthly and is responsible for more than one-third of the total Internet traffic. Despite its growing social, economic, and technological importance, there is little understanding of how users behave in this ecosystem. Because of the decentralized structure of peer-to-peer services, it is very difficult to gather data on users behaviors, and it is in this sense that peer-to-peer file-sharing has been called the “dark matter” of the Internet. Here, we investigate users activity patterns and uncover socioeconomic factors that could explain their behavior.
Impact of heterogeneity and socioeconomic factors on individual behavior in decentralized sharing ecosystems Arnau Gavaldà-Miralles, et al.
Over a scientist’s career, a reputation is developed, a standing within a research community, based largely upon the quantity and quality of his/her publications. Here, we develop a framework for quantifying the influence author reputation has on a publication’s future impact. We find author reputation plays a key role in driving a paper’s citation count early in its citation life cycle, before a tipping point, after which reputation has much less influence relative to the paper’s citation count. In science, perceived quality, and decisions made based on those perceptions, is increasingly linked to citation counts. Shedding light on the complex mechanisms driving these quantitative measures facilitates not only better evaluation of scientific outputs but also a more transparent evaluation of the scientists producing them.
Reputation and impact in academic careers Alexander Michael Petersen, Santo Fortunato, Raj K. Pan, Kimmo Kaski, Orion Penner, Armando Rungi, Massimo Riccaboni, H. Eugene Stanley, and Fabio Pammolli
In this paper we extend to a generic class of piecewise smooth dynamical systems a fundamental tool for the analysis of convergence of smooth dynamical systems: contraction theory. We focus on switched nondifferentiable systems satisfying Carathéodory conditions for the existence and uniqueness of a solution. After generalizing the classical definition of contraction to this class of dynamical systems, we give sufficient conditions for global convergence of their trajectories. The theoretical results are then applied to solve a set of representative problems such as proving global asymptotic stability of switched linear systems, giving conditions for incremental stability of piecewise smooth systems, and analyzing the convergence of networked switched systems.
Contraction Analysis for a Class of NonDifferentiable Systems with Applications to Stability and Network Synchronization
Male mammals often kill conspecific offspring. The benefits of such infanticide to males, and its costs to females, probably vary across mammalian social and mating systems. We used comparative analyses to show that infanticide primarily evolves in social mammals in which reproduction is monopolized by a minority of males. It has not promoted social counterstrategies such as female gregariousness, pair living, or changes in group size and sex ratio, but is successfully prevented by female sexual promiscuity, a paternity dilution strategy. These findings indicate that infanticide is a consequence, rather than a cause, of contrasts in mammalian social systems affecting the intensity of sexual conflict.
The evolution of infanticide by males in mammalian societies Dieter Lukas, Elise Huchard
Context: Many recent research areas such as human cognition and quantum physics call the observer-independence of traditional science into question. Also, there is a growing need for self-reflexivity in science, i.e., a science that reflects on its own outcomes and products. Problem: We introduce the concept of second-order science that is based on the operation of re-entry. Our goal is to provide an overview of this largely unexplored science domain and of potential approaches in second-order fields. Method: We provide the necessary conceptual groundwork for explorations in second-order science, in which we discuss the differences between first- and second-order science and where we present a roadmap for second-order science. The article operates mainly with conceptual differentiations such as the separation between three seemingly identical concepts such as Science II, Science 2.0 and second-order science. Results: Compared with first-order science, the potential of second-order science lies in 1. higher levels of novelty and innovations, 2. higher levels of robustness and 3. wider integration as well as higher generality. As first-order science advances, second-order science, with re-entry as its basic operation, provides three vital functions for first-order science, namely a rich source of novelty and innovation, the necessary quality control and greater integration and generality. Implications: Second-order science should be viewed as a major expansion of traditional scientific fields and as a scientific breakthrough towards a new wave of innovative research. Constructivist content: Second-order science has strong ties with radical constructivism, which can be qualified as the most important root/origin of second-order science. Moreover, it will be argued that a new form of cybernetics is needed to cope with the new problems and challenges of second-order science.
As more and more users access social network services from smart devices with GPS receivers, the available amount of geo-tagged information makes repeating classical experiments possible on global scales and with unprecedented precision. Inspired by the original experiments of Milgram, we simulated message routing within a representative sub-graph of the network of Twitter users with about 6 million geo-located nodes and 122 million edges. We picked pairs of users from two distant metropolitan areas and tried to find a route between them using local geographic information only; our method was to forward messages to a friend living closest to the target. We found that the examined network is navigable on large scales, but navigability breaks down at the city scale and the network becomes unnavigable on intra-city distances. This means that messages usually arrived to the close proximity of the target in only 3–6 steps, but only in about 20% of the cases was it possible to find a route all the way to the recipient, in spite of the network being connected.
During the Chinese New Year, people traditionally return to their families in villages all over China. 30 years ago, this event triggered the migration of about a million people over just a few days. The rapid growth of the Chinese economy has changed all that. During the latest celebration in January and February 2014, some 3.6 billion people travelled across China making this the largest seasonal migration on Earth.
The spreading of unsubstantiated rumors on online social networks (OSN) either unintentionally or intentionally (e.g., for political reasons or even trolling) can have serious consequences such as in the recent case of rumors about Ebola causing disruption to health-care workers. Here we show that indicators aimed at quantifying information consumption patterns might provide important insights about the virality of false claims. In particular, we address the driving forces behind the popularity of contents by analyzing a sample of 1.2M Facebook Italian users consuming different (and opposite) types of information (science and conspiracy news). We show that users' engagement across different contents correlates with the number of friends having similar consumption patterns (homophily), indicating the area in the social network where certain types of contents are more likely to spread. Then, we test diffusion patterns on an external sample of 4,709 intentional satirical false claims showing that neither the presence of hubs (structural properties) nor the most active users (influencers) are prevalent in viral phenomena. Instead, we found out that in an environment where misinformation is pervasive, users' aggregation around shared beliefs may make the usual exposure to conspiracy stories (polarization) a determinant for the virality of false information.
Viral Misinformation: The Role of Homophily and Polarization Aris Anagnostopoulos, Alessandro Bessi, Guido Caldarelli, Michela Del Vicario, Fabio Petroni, Antonio Scala, Fabiana Zollo, Walter Quattrociocchi
So what is “scaling”? In its most elemental form, it simply refers to how systems respond when their sizes change. What happens to cities or companies if their sizes are doubled? What happens to buildings, airplanes, economies, or animals if they are halved? Do cities that are twice as large have approximately twice as many roads and produce double the number of patents? Should the profits of a company twice the size of another company double? Does an animal that is half the mass of another animal require half as much food?
The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion. The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets. The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to "forget" old memes and replace them over time with the new ones. The evaluation of our framework is carried out by using a dataset of Twitter trending topics. Over a one-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.
Clustering memes in social media streams Mohsen JafariAsbagh, Emilio Ferrara, Onur Varol, Filippo Menczer, Alessandro Flammini