Structural and dynamical patterns on online social networks: the Spanish May 15th movement as a case study

The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics.     

The importance of fission–fusion social group dynamics in birds

Almost all animal social groups show some form of fission–fusion dynamics, whereby group membership is not spatio‐temporally stable. These dynamics have major implications at both population and individual levels, exerting an important influence on patterns of social behaviour, information transfer and epidemiology. However, fission–fusion dynamics in birds have received relatively little attention. We review the existing evidence for fission–fusion sociality in birds alongside a more general explanation of the social and ecological processes that may drive fission–fusion dynamics. Through a combination of recent methodological developments and novel technologies with well‐established areas of ornithological research, avian systems offer great potential to further our understanding of fission–fusion social systems and the consequences they have at an individual and population level. In particular, investigating the interaction between social structure and environmental covariates can promote a deeper understanding of the evolution of social behaviour and the adaptive value of group living, as well as having important consequences for applied research.     

Quantitative study of polymer conformation and dynamics by single-particle tracking

We present a new method for analyzing the dynamics of conformational fluctuations of individual flexible polymer molecules. In single-particle tracking (SPT), one end of the polymer molecule is tethered to an immobile substratum. A microsphere attached to the other end serves as an optical marker. The conformational fluctuations of the polymer molecule can be measured by optical microscopy via the motion of the microsphere. The bead-and-spring theory for polymer dynamics is further developed to account for the microsphere, and together the measurement and the theory yield quantitative information about molecular conformations and dynamics under nonperturbing conditions. Applying the method to measurements carried out on DNA molecules provides information complementary to recent studies of single DNA molecules under extensional force. Combining high precision measurements with the theoretical analysis presented here creates a powerful tool for studying conformational dynamics of biological and synthetic macromolecules at the single-molecule level.     

Entropy of dynamical social networks

Human dynamical social networks encode information and are highly adaptive. To characterize the information encoded in the fast dynamics of social interactions, here we introduce the entropy of dynamical social networks. By analysing a large dataset of phone-call interactions we show evidence that the dynamical social network has an entropy that depends on the time of the day in a typical week-day. Moreover we show evidence for adaptability of human social behavior showing data on duration of phone-call interactions that significantly deviates from the statistics of duration of face-to-face interactions. This adaptability of behavior corresponds to a different information content of the dynamics of social human interactions. We quantify this information by the use of the entropy of dynamical networks on realistic models of social interactions.     

Neural mechanisms of social learning and decision-making

One of the hallmarks of human society is the ubiquitous interactions among individuals. Indeed, a significant portion of human daily routine decision making is socially related. Normative economic theory, namely game theory, has prescribed the canonical decision strategy when "rational" social agents have full information about the decision environment. In reality, however, social decision is often influenced by the trait and state parameters of selves and others. Therefore, understanding the cognitive and neural processes of inferring the decision parameters is pivotal for social decision making. Recently, both correlational and causal non-invasive neuroimaging studies have started to reveal the critical neural computations underlying social learning and decision-making, and highlighted the unique roles of "social" brain structures such as temporal-parietal junction(TPJ) and dorsomedial prefrontal cortex(dmPFC). Here we review recent advances in social decision neuroscience and maintain the focus on how the inference about others is dynamically acquired during social learning, as well as how the prosocial(altruistic)behavior results from orchestrated interactions of different brain regions specified under the social utility framework. We conclude by emphasizing the importance of combining computational decision theory with the identification of neural mechanisms that represent, evaluate and integrate value related social information and generate decision variables guiding behavioral output in the complex social environment.     

Seasonality and wildlife disease: how seasonal birth, aggregation and variation in immunity affect the dynamics of Mycoplasma gallisepticum in house finches

We examine the role of host seasonal breeding, host seasonal social aggregation and partial immunity in affecting wildlife disease dynamics, focusing on the dynamics of house finch conjunctivitis (Mycoplasma gallisepticum (MG) in Carpodacus mexicanus). This case study of an unmanaged emerging infectious disease provides useful insight into the important role of seasonal factors in driving ongoing disease dynamics. Seasonal breeding can force recurrent epidemics through the input of fresh susceptibles, which will clearly affect a wide variety of wildlife disease dynamics. Seasonal patterns of social aggregation and foraging behaviour could change transmission dynamics. We use latitudinal variation in the timing of breeding, and social systems to model seasonal dynamics of house finch conjunctivitis across eastern North America. We quantify the patterns of seasonal breeding, and social aggregation across a latitudinal gradient in the eastern range of the house finch, supplemented with known field and laboratory information on immunity to MG in finches. We then examine the interactions of these factors in a theoretical model of disease dynamics. We find that both forms of seasonality could explain the dynamics of the house finch-MG system, and that these factors could have important effects on the dynamics of wildlife diseases generally. In particular, while either alone is sufficient to create recurrent cycles of prevalence in a population with an endemic disease, both are required to produce the specific semi-annual pattern of disease prevalence seen in the house finch conjunctivitis system.     

Comparison between perfect information and passive–adaptive social learning models of forest harvesting

This paper compares perfect information and passive–adaptive social learning models of forest harvesting using a simple Markov chain model for land-use dynamics. A perfect information model assumes that landowners know true utility values of forest conservation and harvesting. In contrast, a passive–adaptive social learning model assumes that landowners do not know true utility values and they learn these values by their past experiences and by exchanging information with others in a society. We determine conditions under which the same consequences expected from perfect information and passive–adaptive social learning models. We found that the outcome from a perfect information model resembles that from passive–adaptive social learning model only when the perfect information model incorporates little discounting for future values. The stability analysis of landscape dynamics predicts a cyclic overexploitation of forest resources in a passive–adaptive social learning model with short-term memory, while instability of landscapes is never expected in a perfect information model. We discuss the role of discounting the future and discounting the past in the context of forest management.     

Avoiding costly mistakes in groups: The evolution of error management in collective decision making

Individuals continuously have to balance the error costs of alternative decisions. A wealth of research has studied how single individuals navigate this, showing that individuals develop response biases to avoid the more costly error. We, however, know little about the dynamics in groups facing asymmetrical error costs and when social influence amplifies either safe or risky behavior. Here, we investigate this by modeling the decision process and information flow with a drift–diffusion model extended to the social domain. In the model individuals first gather independent personal information; they then enter a social phase in which they can either decide early based on personal information, or wait for additional social information. We combined the model with an evolutionary algorithm to derive adaptive behavior. We find that under asymmetric costs, individuals in large cooperative groups do not develop response biases because such biases amplify at the collective level, triggering false information cascades. Selfish individuals, however, undermine the group’s performance for their own benefit by developing higher response biases and waiting for more information. Our results have implications for our understanding of the social dynamics in groups facing asymmetrical errors costs, such as animal groups evading predation or police officers holding a suspect at gunpoint.     

Uncertainty and the role of information acquisition in the evolution of context-dependent emigration

There is increasing empirical evidence that individuals utilize social and environmental cues in making decisions as to whether or not to disperse. However, we lack theory exploring the influence of information acquisition and use on the evolution of dispersal strategies and metapopulation dynamics. We used an individual-based, spatially explicit simulation model to explore the evolution of emigration strategies under varying precision of information about the natal patch, cost of information acquisition, and environmental predictability. Our findings show an interesting interplay between information use and the evolved emigration propensity. Lack of information led to higher emigration probabilities in more unpredictable environments but to lower emigration probabilities in constant or highly predictable scenarios. Somewhat-informed dispersal strategies were selected for in most cases, even when the acquisition of information was associated with a moderate reproductive cost. Notably, selection rarely favored investment in acquisition of high-precision information, and the tendency to invest in information acquisition was greatest in predictable environments when the associated cost was low. Our results highlight that information use can affect dispersal in a complex manner and also emphasize that information-acquisition behaviors can themselves come under strong selection, resulting in evolutionary dynamics that are tightly coupled to those of context-dependent behaviors.     

Convergent evolution of kin-based sociality in a lizard

Studies of social birds and mammals have produced extensive theory regarding the formation and dynamics of kin-based social groups in vertebrates. However, comparing kin dynamics in birds and mammals to social reptiles provides the opportunity to identify selective factors that promote independent origins of kin sociality across vertebrates. We combined a 5-year mark-recapture study with a DNA microsatellite analysis of relatedness in a social lizard (Xantusia vigilis) to examine the formation and stability of kin groups. We found that these lizards are highly sedentary and that groups often form through the delayed dispersal of offspring. Groups containing juveniles had higher relatedness than adult-only groups, as juveniles were commonly found in aggregations with at least one parent and/or sibling. Groups containing nuclear family members were more stable than groups of less-related lizards, as predicted by social theory. We conclude that X. vigilis aggregations conform to patterns of kin sociality observed in avian and mammalian systems and represent an example of convergent evolution in social systems. We suggest that kin-based sociality in this and other lizards may be a by-product of viviparity, which can promote delayed juvenile dispersal by allowing prolonged interaction between a neonate and its mother.     

Coupled information diffusion--pest dynamics models predict delayed benefits of farmer cooperation in pest management programs

Worldwide, the theory and practice of agricultural extension system have been dominated for almost half a century by Rogers' "diffusion of innovation theory". In particular, the success of integrated pest management (IPM) extension programs depends on the effectiveness of IPM information diffusion from trained farmers to other farmers, an important assumption which underpins funding from development organizations. Here we developed an innovative approach through an agent-based model (ABM) combining social (diffusion theory) and biological (pest population dynamics) models to study the role of cooperation among small-scale farmers to share IPM information for controlling an invasive pest. The model was implemented with field data, including learning processes and control efficiency, from large scale surveys in the Ecuadorian Andes. Our results predict that although cooperation had short-term costs for individual farmers, it paid in the long run as it decreased pest infestation at the community scale. However, the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time, giving rise to natural lags in IPM diffusion and applications. We further showed that if individuals learn from others about the benefits of early prevention of new pests, then educational effort may have a sustainable long-run impact. Consistent with models of information diffusion theory, our results demonstrate how an integrated approach combining ecological and social systems would help better predict the success of IPM programs. This approach has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations.     

Evolutionary psychology and the generation of culture,part II: Case study: A computational theory of social exchange

Models of the various adaptive specializations that have evolved in the human psyche could become the building blocks of a scientific theory of culture. The first step in creating such models is the derivation of a so-called “computational theory” of the adaptive problem each psychological specialization has evolved to solve. In Part II, as a case study, a sketch of a computational theory of social exchange (cooperation for mutual benefit) is developed. The dynamics of natural selection in Pleistocene ecological conditions define adaptive information processing problems that humans must be able to solve in order to participate in social exchange: individual recognition, memory for one's history of interaction, value communication, value modeling, and a shared grammar of social contracts that specifies representational structure and inferential procedures. The nature of these adaptive information processing problems places constraints on the class of cognitive programs capable of solving them; this allows one to make empirical predictions about how the cognitive processes involved in attention, communication, memory, learning, and reasoning are mobilized in situations of social exchange. Once the cognitive programs specialized for regulating social exchange are mapped, the variation and invariances in social exchange within and between cultures can be meaningfully discussed.     

The type and timing of social information alters offspring production

The acquisition and use of information are essential for decision-making in an uncertain world. The use of social information, or information from the behaviour of others, may be a common and efficient mechanism to improve estimates of resource quality by animals. According to theory, social information cues with higher information content should have a greater influence on decision-making, and current information should be weighed more than prior information. However, experimental tests of these hypotheses remain scarce. We exposed female cactus bugs (Chelinidea vittiger) to different types of social information (the presence of conspecific eggs or nymphs) presented at different times (current or prior to egg laying) to determine the influence of social information on offspring production. We found that social information substantially altered the number of eggs produced. The presence of conspecific eggs, regardless of timing, consistently increased egg production, whereas nymphs only increased egg production when present during egg laying. We conclude that the type and timing of social information may have an important, yet unappreciated, influence on reproductive allocation.     

Higher frequency of social learning in China than in the West shows cultural variation in the dynamics of cultural evolution

Cultural evolutionary models have identified a range of conditions under which social learning (copying others) is predicted to be adaptive relative to asocial learning (learning on one''s own), particularly in humans where socially learned information can accumulate over successive generations. However, cultural evolution and behavioural economics experiments have consistently shown apparently maladaptive under-utilization of social information in Western populations. Here we provide experimental evidence of cultural variation in people''s use of social learning, potentially explaining this mismatch. People in mainland China showed significantly more social learning than British people in an artefact-design task designed to assess the adaptiveness of social information use. People in Hong Kong, and Chinese immigrants in the UK, resembled British people in their social information use, suggesting a recent shift in these groups from social to asocial learning due to exposure to Western culture. Finally, Chinese mainland participants responded less than other participants to increased environmental change within the task. Our results suggest that learning strategies in humans are culturally variable and not genetically fixed, necessitating the study of the ‘social learning of social learning strategies'' whereby the dynamics of cultural evolution are responsive to social processes, such as migration, education and globalization.     

The implicit assumption of symmetry and the species abundance distribution

Species abundance distributions (SADs) have played a historical role in the development of community ecology. They summarize information about the number and the relative abundance of the species encountered in a sample from a given community. For years ecologists have developed theory to characterize species abundance patterns, and the study of these patterns has received special attention in recent years. In particular, ecologists have developed statistical sampling theories to predict the SAD expected in a sample taken from a region. Here, we emphasize an important limitation of all current sampling theories: they ignore species identity. We present an alternative formulation of statistical sampling theory that incorporates species asymmetries in sampling and dynamics, and relate, in a general way, the community-level SAD to the distribution of population abundances of the species integrating the community. We illustrate the theory on a stochastic community model that can accommodate species asymmetry. Finally, we discuss the potentially important role of species asymmetries in shaping recently observed multi-humped SADs and in comparisons of the relative success of niche and neutral theories at predicting SADs.     

The leading eight: social norms that can maintain cooperation by indirect reciprocity

The theory of indirect reciprocation explains the evolution of cooperation among unrelated individuals, engaging in one-shot interaction. Using reputation, a player acquires information on who are worth cooperating and who are not. In a previous paper, we formalized the reputation dynamics, a rule to assign a binary reputation (good or bad) to each player when his action, his current reputation, and the opponent's reputation are given. We then examined all the possible reputation dynamics, and found that there exist only eight reputation dynamics named "leading eight" that can maintain the ESS with a high level of cooperation, even if errors are included in executing intended cooperation and in reporting the observation to the public. In this paper, we study the nature of these successful social norms. First, we characterize the role of each pivot of the reputation dynamics common to all of the leading eight. We conclude that keys to the success in indirect reciprocity are to be nice (maintenance of cooperation among themselves), retaliatory (detection of defectors, punishment, and justification of punishment), apologetic, and forgiving. Second, we prove the two basic properties of the leading eight, which give a quantitative evaluation of the ESS condition and the level of cooperation maintained at the ESS.     

Sequence evolution correlates with structural dynamics

Biochemical activity and core stability are essential properties of proteins, maintained usually by conserved amino acids. Structural dynamics emerged in recent years as another essential aspect of protein functionality. Structural dynamics enable the adaptation of the protein to binding substrates and to undergo allosteric transitions, while maintaining the native fold. Key residues that mediate structural dynamics would thus be expected to be conserved or exhibit coevolutionary patterns at least. Yet, the correlation between sequence evolution and structural dynamics is yet to be established. With recent advances in efficient characterization of structural dynamics, we are now in a position to perform a systematic analysis. In the present study, a set of 34 enzymes representing various folds and functional classes is analyzed using information theory and elastic network models. Our analysis shows that the structural regions distinguished by their coevolution propensity as well as high mobility are predisposed to serve as substrate recognition sites, whereas residues acting as global hinges during collective dynamics are often supported by conserved residues. We propose a mobility scale for different types of amino acids, which tends to vary inversely with amino acid conservation. Our findings suggest the balance between physical adaptability (enabled by structure-encoded motions) and chemical specificity (conferred by correlated amino acid substitutions) underlies the selection of a relatively small set of versatile folds by proteins.     

Vultures acquire information on carcass location from scavenging eagles

Vultures are recognized as the scroungers of the natural world, owing to their ecological role as obligate scavengers. While it is well known that vultures use intraspecific social information as they forage, the possibility of inter-guild social information transfer and the resulting multi-species social dilemmas has not been explored. Here, we use data on arrival times at carcasses to show that such social information transfer occurs, with raptors acting as producers of information and vultures acting as scroungers of information. We develop a game-theoretic model to show that competitive asymmetry, whereby vultures dominate raptors at carcasses, predicts this evolutionary outcome. We support this theoretical prediction using empirical data from competitive interactions at carcasses. Finally, we use an individual-based model to show that these producer–scrounger dynamics lead to vultures being vulnerable to declines in raptor populations. Our results show that social information transfer can lead to important non-trophic interactions among species and highlight important potential links among social evolution, community ecology and conservation biology. With vulture populations suffering global declines, our study underscores the importance of ecosystem-based management for these endangered keystone species.     

Behavioural ecology at the spatial–social interface

Spatial and social behaviour are fundamental aspects of an animal's biology, and their social and spatial environments are indelibly linked through mutual causes and shared consequences. We define the ‘spatial–social interface’ as intersection of social and spatial aspects of individuals' phenotypes and environments. Behavioural variation at the spatial–social interface has implications for ecological and evolutionary processes including pathogen transmission, population dynamics, and the evolution of social systems. We link spatial and social processes through a foundation of shared theory, vocabulary, and methods. We provide examples and future directions for the integration of spatial and social behaviour and environments. We introduce key concepts and approaches that either implicitly or explicitly integrate social and spatial processes, for example, graph theory, density-dependent habitat selection, and niche specialization. Finally, we discuss how movement ecology helps link the spatial–social interface. Our review integrates social and spatial behavioural ecology and identifies testable hypotheses at the spatial–social interface.