Social Collective Behavior Research Articles

Collective behaviors and networks

The goal of this thematic series is to provide a discussion venue about recent advances in the study of networks and their applications to the study of collective behavior in socio-technical systems. The series includes contributions exploring the intersection between data-driven studies of complex networks and agent-based models of collective social behavior. Particular attention is devoted to topics aimed at understanding social behavior through the lens of data about technology-mediated communication. These include: modeling social dynamics of attention and collaboration, characterizing online group formation and evolution, and studying the emergence of roles and interaction patterns in social media environments.

Can electoral popularity be predicted using socially generated big data?

Abstract Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.

Un modelo de difusión anisótropa para el estudio del tráfico urbano

In order to design or redesign urban transportation networks, the employment of mathematical models is very useful for predicting the effects of possible modifications of implementing. Such models allow the determination of vehicular flows and travel times for every link of the network from the knowledge of its inherent features and the corresponding traffic demand. They are based on a phenomenological law of the social collective behavior of the drivers called Wardrop principle. It is an optimization problem, in general, very demanding from the computational point of view.In order to accelerate the computation process, in this paper, a continuum model for the urban traffic is proposed. The fundamental assumption behind this theory is that the variation of network properties is small in close regions when compared with the full system. Accordingly, it is possible to use continuous functions for representing travel times or vehicular flows. Essentially, the problem is formulated as a system of non-linear anisotropic diffusion (differential) equations that can be conveniently solved by means of the finite element method. The efficiency of the proposed model is studied by means of a comparison with results obtained with the classical optimization approach. As shown, the results are similar although the computation times are significantly reduced.

Swarm Optimization Methods in Microwave Imaging

Swarm intelligence denotes a class of new stochastic algorithms inspired by the collective social behavior of natural entities (e.g., birds, ants, etc.). Such approaches have been proven to be quite effective in several applicative fields, ranging from intelligent routing to image processing. In the last years, they have also been successfully applied in electromagnetics, especially for antenna synthesis, component design, and microwave imaging. In this paper, the application of swarm optimization methods to microwave imaging is discussed, and some recent imaging approaches based on such methods are critically reviewed.

Hybrid Artificial Intelligence–Based PBA for Benchmark Functions and Facility Layout Design Optimization

AbstractFacility layout design (FLD) presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, FLD present a difficult combinatorial optimization problem for engineers. Swarm intelligence (SI), an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization, is being increasingly used to resolve various complex optimization problems. This study proposes a new optimization hybrid swarm algorithm, the particle bee algorithm (PBA), which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows (NW) technique for improving searching efficiency and a self-parameter-updating (SPU) technique for preventing trapping into a local optimum in high-dimensional problems. This study compares the performance of PBA with th...

Quantifying collective behavior in mammalian cells

Collective social behaviors generate one emergent response from many individual actors without central coordination: bees swarm, birds flock, and fish school. Single-celled bacteria and amoebae collectively swarm, detecting chemical signals emitted by close neighbors to synchronize their motion. Within organisms, collective behaviors drive multicellular processes during migration and wound healing. Similar to unicellular organisms, mammalian cells coordinate their responses during these complex processes by detecting changes in concentration of signaling molecules. However, the signaling pathways used by mammalian cells are typically more interdependent than those in unicellular organisms, and gene expression varies with the time-dependent concentration of signaling molecules. Understanding the role of intercellular communications for collective behavior in mammalian cells thus demands sensitive measurements of responses to chemical signals. In PNAS, Sun et al. (1) quantify the spatiotemporal evolution of calcium response to ATP stimulation in fibroblasts. They find that the response of dense colonies of cells to ATP stimulation is faster and more synchronized than that of isolated cells. By quantitatively correlating responses between neighboring pairs of cells, they identify pacemaker cells; by physically separating cells in a hydrogel matrix, they confirm that cells communicate via gap junctions to synchronize their responses. These experiments show that both temporal and spatial dynamics of collective response in mammalian cells depend on intercellular signaling.

Reconciling long-term cultural diversity and short-term collective social behavior

An outstanding open problem is whether collective social phenomena occurring over short timescales can systematically reduce cultural heterogeneity in the long run, and whether offline and online human interactions contribute differently to the process. Theoretical models suggest that short-term collective behavior and long-term cultural diversity are mutually excluding, since they require very different levels of social influence. The latter jointly depends on two factors: the topology of the underlying social network and the overlap between individuals in multidimensional cultural space. However, while the empirical properties of social networks are intensively studied, little is known about the large-scale organization of real societies in cultural space, so that random input specifications are necessarily used in models. Here we use a large dataset to perform a high-dimensional analysis of the scientific beliefs of thousands of Europeans. We find that interopinion correlations determine a nontrivial ultrametric hierarchy of individuals in cultural space. When empirical data are used as inputs in models, ultrametricity has strong and counterintuitive effects. On short timescales, it facilitates a symmetry-breaking phase transition triggering coordinated social behavior. On long timescales, it suppresses cultural convergence by restricting it within disjoint groups. Moreover, ultrametricity implies that these results are surprisingly robust to modifications of the dynamical rules considered. Thus the empirical distribution of individuals in cultural space appears to systematically optimize the coexistence of short-term collective behavior and long-term cultural diversity, which can be realized simultaneously for the same moderate level of mutual influence in a diverse range of online and offline settings.

How Citation Boosts Promote Scientific Paradigm Shifts and Nobel Prizes

Nobel Prizes are commonly seen to be among the most prestigious achievements of our times. Based on mining several million citations, we quantitatively analyze the processes driving paradigm shifts in science. We find that groundbreaking discoveries of Nobel Prize Laureates and other famous scientists are not only acknowledged by many citations of their landmark papers. Surprisingly, they also boost the citation rates of their previous publications. Given that innovations must outcompete the rich-gets-richer effect for scientific citations, it turns out that they can make their way only through citation cascades. A quantitative analysis reveals how and why they happen. Science appears to behave like a self-organized critical system, in which citation cascades of all sizes occur, from continuous scientific progress all the way up to scientific revolutions, which change the way we see our world. Measuring the “boosting effect” of landmark papers, our analysis reveals how new ideas and new players can make their way and finally triumph in a world dominated by established paradigms. The underlying “boost factor” is also useful to discover scientific breakthroughs and talents much earlier than through classical citation analysis, which by now has become a widespread method to measure scientific excellence, influencing scientific careers and the distribution of research funds. Our findings reveal patterns of collective social behavior, which are also interesting from an attention economics perspective. Understanding the origin of scientific authority may therefore ultimately help to explain how social influence comes about and why the value of goods depends so strongly on the attention they attract.

How simple rules determine pedestrian behavior and crowd disasters

With the increasing size and frequency of mass events, the study of crowd disasters and the simulation of pedestrian flows have become important research areas. However, even successful modeling approaches such as those inspired by Newtonian force models are still not fully consistent with empirical observations and are sometimes hard to calibrate. Here, a cognitive science approach is proposed, which is based on behavioral heuristics. We suggest that, guided by visual information, namely the distance of obstructions in candidate lines of sight, pedestrians apply two simple cognitive procedures to adapt their walking speeds and directions. Although simpler than previous approaches, this model predicts individual trajectories and collective patterns of motion in good quantitative agreement with a large variety of empirical and experimental data. This model predicts the emergence of self-organization phenomena, such as the spontaneous formation of unidirectional lanes or stop-and-go waves. Moreover, the combination of pedestrian heuristics with body collisions generates crowd turbulence at extreme densities--a phenomenon that has been observed during recent crowd disasters. By proposing an integrated treatment of simultaneous interactions between multiple individuals, our approach overcomes limitations of current physics-inspired pair interaction models. Understanding crowd dynamics through cognitive heuristics is therefore not only crucial for a better preparation of safe mass events. It also clears the way for a more realistic modeling of collective social behaviors, in particular of human crowds and biological swarms. Furthermore, our behavioral heuristics may serve to improve the navigation of autonomous robots.

A hybrid AI-based particle bee algorithm for facility layout optimization

Facility layout (FL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, this generally presents a difficult combinatorial optimization problem for engineers. Swarm intelligence, an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm—the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows technique for improving searching efficiency as well as a self-parameter-updating technique for preventing trapping into a local optimum in high-dimensional problems. This study compares PBA performance against BA and PSO performance in practical FL problem. Results show PBA performance is comparable to those of BA and PSO and can be efficiently employed to solve practical FL problem with high dimensionality.

From Credit to Collective Action: The Role of Microfinance in Promoting Women's Social Capital and Normative Influence

Can economic ties positively influence social relations and actions? If so, how does this influence operate? Microfinance programs, which provide credit through a group-based lending strategy, provide the ideal setting for exploring these questions. This article examines whether structuring socially isolated women into peer-groups for an explicitly economic purpose, such as access to credit, has any effect on the women's collective social behavior. Based on interviews with 400 women from 59 microfinance groups in West Bengal, India, I find that one third of these groups undertook various collective actions. Improvements in women's social capital and normative influence fostered this capacity for collective action. Several factors contributed to these transformations, including economic ties among members, the structure of the group network, and women's participation in group meetings. Based on these findings, I argue that microfinance groups have the potential to promote women's social capital and normative influence, thereby facilitating women's collective empowerment. I conclude by discussing the need for refining our understanding of social capital and social ties that promote normative influence.

Eurasian Gates: The Earliest Human Dispersals

This paper revises the current state of the debate about the earliest hominin dispersals out of Africa. First we review the archaeological evidence for the earliest occupation of Asia and Europe. Next we summarize the environmental parameters related to the earliest phases of human evolution--specifically, climatic implications for human adaptations andfaunal dispersals. We discuss which were the first hominins to leave Africa, and we propose the invention of technology as a fundamental step for the development of our genus, likely related to changes in subsistence and diet during the late Pliocene and early Pleistocene. From our point of view, hominins were able to generalize the use of technology, as well as to generate, integrate, and diffuse new information into their collective social behavior. We refer to this concept as "socialization." Hence, technology and the socialization thereof became integral aspects of the ecological niche of hominins.

Animat Market - Trading Interactions as Collective Social Adaptive Behavior

We argue that human economic interactions, particularly bargaining and trading in market envi ronments, can be considered as collective social adaptive behaviors. Such interactions are social in the sense that they depend on socially-agreed market regulations and communication protocols, and are collective in the sense that global market dynamics depend on the interactions of groups of traders. Moreover, the tools and techniques of adaptive behavior research could be profitably employed to build predictive models of existing or planned market systems. Despite these poten tial applications, we note that there is a near-total absence of papers in the adaptive behavior lit erature that deal with autonomous agents capable of exhibiting trading behaviors. We summarize work in experimental economics where human trading behavior is studied under laboratory con ditions. We propose that such experiments could and should be used as 'benchmarks' for evaluat ing and comparing different architectures and strategies for trading animats. We present results from experiments where an elementary machine learning technique endows simple autonomous- software agents with the capability to adapt while interacting via price-bargaining in market envi ronments. The environments are based on artificial retail markets used in experimental econom ics research. We demonstrate that groups of simple agents can exhibit human-like collective mar ket behaviors. These results invite a Braitenberg-style eliminative materialism perspective on the dynamics of experimental retail markets.

Student Walkout: A Fortuitous Examination of Elementary Collective Behavior

The traditional separation of explanations of collective behavior and routine social behavior is noted. Blumer's work is examined as representative of and influential in maintaining this separation. Participants' reports of the initiation and development of an elementary form of collective behavior are presented. This case study records repeated examples of the processes Blumer and others have restricted to routine social behavior and thus challenges the separation they have maintained. These data are used to illustrate some rudimentary principles which are applicable to the production, maintenance, and alteration of lines of social behavior in routine and collective behavior settings.