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When toilet paper disappeared from store shelves across Japan in November 1973, the housewives who searched their neighborhood markets and stood in long lines for the scarce packs were described as unwitting perpetrators of a "toilet paper panic." The word "panic" evoked well-established notions of a social frenzy, irrational and overwrought. This article overturns a conception of panic laden with condescension and adopts, instead, a more empathetic approach to examining why a run on toilet paper and other daily necessities occurred in late 1973, and why it resonated so deeply across Japan as a "panic." In the broader context of the early 1970s, the toilet paper scare and its characterization as a "panic" can be understood as responses to sweeping and multifaceted economic challenges that destabilized daily life and threatened to upend middle-class lifestyles. After more than fifteen years of high economic growth, that sense of middle-class comfort and security was shaken by shortages and inflation as well as mounting evidence of government ineptitude and corporate profiteering. Buffeted by insecurity and uncertainty, Japanese "middle-classness" as both an experience and an ideal had never seemed as vulnerable as it did in the early 1970s. [ABSTRACT FROM AUTHOR]

Research on political participation almost unanimously assumes that social pressure by neighbors induces collective behavior. Yet most experimental studies focus on individually based forms of political and civic behavior, such as voting and recycling, in Western industrialized societies. The paper tests the effect of neighborly social pressure on collective action in Tunisia. In a field experiment, I manipulate whether neighbors or community outsiders invite citizens to contribute to a public good (i.e., trash collection). I run the experiment in three neighborhoods of varying socioeconomic composition in Tunis (n = 1199). I do not find evidence to suggest that neighborly social pressure encourages participation in neighborhood cleanups, with low participation rates both for the neighbor and outsider contact conditions. While the effect of social pressure does not significantly vary across communities, overall participation rates do. Residents of the poor neighborhood are most likely to respond in a socially desirable way when asked about their intentions but least likely to participate. The paper also discusses some limitations of the study and outlines avenues for future research. [ABSTRACT FROM AUTHOR]

In this paper, we analyze submersions with Poisson fibres. These are submersions whose total space carries a Poisson structure, on which the ambient Poisson structure pulls back, as a Dirac structure, to Poisson structures on each individual fibre. Our "Poisson--Dirac viewpoint" is prompted by natural examples of Poisson submersions with Poisson fibres -- in toric geometry and in Poisson--Lie groups -- whose analysis was not possible using the existing tools in the Poisson literature. The first part of the paper studies the Poisson--Dirac perspective of inducing Poisson structures on submanifolds. This is a rich landscape, in which subtle behaviours abound, as illustrated by a surprising "jumping phenomenon" concerning the complex relation between the induced and the ambient symplectic foliations, which we discovered here. These pathologies, however, are absent from the well-behaved and abundant class of coregular submanifolds, with which we are mostly concerned here. The second part of the paper studies Poisson submersions with Poisson fibres -- the natural Poisson generalization of flat symplectic bundles. These Poisson submersions have coregular Poisson--Dirac fibres, and behave functorially with respect to such submanifolds. We discuss the subtle collective behavior of the Poisson fibres of such Poisson fibrations, and explain their relation to pencils of Poisson structures. The third and final part applies the theory developed to Poisson submersions with Poisson fibres which arise in Lie theory. We also show that such submersions are a convenient setting for the associated bundle construction, and we illustrate this by producing new Poisson structures with a finite number of symplectic leaves. Some of the points in the paper being fairly new, we illustrate the many fine issues that appear with an abundance of (counter-)examples. [ABSTRACT FROM AUTHOR]

This paper introduces network science to museum studies. The spatial structure of the museum and the exhibit display largely determine what visitors see and in which order, thereby shaping their visit experience. Despite the importance of spatial properties in museum studies, few scientific tools have been developed to analyze and compare the results across museums. This paper introduces the six habitually used network science indices and assesses their applicability to museum studies. Network science is an empirical research field that focuses on analyzing the relationships between components in an attempt to understand how individual behaviors can be converted into collective behaviors. By taking the museum and the visitors as the network, this methodology could reveal unknown aspects of museum functions and visitor behavior, which could enhance exhibition knowledge and lead to better methods for creating museum narratives along the routes. [ABSTRACT FROM AUTHOR]

In this paper, we construct and analyze a mathematically reasonable and simplest population dynamics model based on Mark Granovetter’s idea for the spread of a matter (rumor, innovation, psychological state, etc.) in a population. The model is described by a one-dimensional difference equation. Individual threshold values with respect to the decision-making on the acceptance of a spreading matter are distributed throughout the population ranging from low (easily accepts it) to high (hardly accepts). Mathematical analysis on our model with some general threshold distributions (uniform; monotonically decreasing/increasing; unimodal) shows that a critical value necessarily exists for the initial frequency of acceptors. Only when the initial frequency of acceptors is beyond the critical, the matter eventually spreads over the population. Further, we give the mathematical results on how the equilibrium acceptor frequency depends on the nature of threshold distribution. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to optimize the thinning of an elliptical cylindrical array (ECA) composed of uniformly stimulated, isotropic antennas with the objective of achieving a directed beam characterized by a significantly reduced relative Sidelobe Level (SLL). The optimization process employs the Novel Particle Swarm Optimization (NPSO) method, which offers a fresh perspective on addressing electromagnetic optimization challenges by its ability to effectively explore solution spaces. By employing the NPSO algorithm, which emulates the collective behavior of swarming particles to search for optimal solutions, this study addresses complex optimization challenges inherent in antenna array design. This study focuses on identifying the optimal combination of ON–OFF components (use the minimum number of ON antenna elements) within the antenna array to produce a radiation pattern exhibiting the greatest decrease in SLL. Additionally, the First Null Beam Width (FNBW) is targeted for optimization without predefined values. The optimization approach also considers the effect of thinned array element spacing on the overall performance metrics. Out of total 36 elements, only 15 elements are switched ON and the remaining of the elements are OFF, so the total reduction or thinning of ECAA is 41.66%. Simulation results demonstrate that the proposed methodology enables a simultaneous reduction of more than half of the antenna array elements while achieving superior SLL minimization. This significant reduction in antenna elements not only contributes to simplifying the array design, but also enhances the array’s beamforming capabilities. [ABSTRACT FROM AUTHOR]

The construction of living systems from the bottom‐up helps to explore the complex processes of life and to understand their working mechanism. Prototissues, constructed using artificial cells as building blocks, mimic life systems at a high‐order tissue level, whilst artificial cells usually mimic living cells at the individual cell level. The 3D biomimetic prototissues demonstrate exceptional performances and collective functions, which reveal the working mechanisms of living tissues and hold promising potential for biomedical applications. This review systematically summarized the research progress of the field of prototissues. The engineering methods for fabricating two types of prototissues are introduced first, followed by the functions of prototissues including collective behaviors and signal communications, as well as their biomedical applications. The challenges and future trends are proposed at the end of the paper. [ABSTRACT FROM AUTHOR]

This special issue presents scientific findings of the latest advancements in synchronization and other collective behaviors within higher-order networks. While the concept of higher-order interactions was introduced long ago, recent research has witnessed a surge in interest surrounding these networks. These developments underscore the significant impact that non-pairwise connections can have on network dynamics, particularly synchronization. Therefore, this issue aims to represent the emerging trends within this field. The collection comprises papers exploring diverse topics, including synchronization, chimera states, spiral waves, stochastic resonance in higher-order networks, higher-order social networks, and real brain networks, and the impacts of link removal on synchronization and hyper-network alignment. The studies can shed light on the complexities of higher-order networks and pave the way for future advancements in the field. [ABSTRACT FROM AUTHOR]

Social behavior in crowds, such as herding or increased interpersonal spacing, is driven by the psychological states of pedestrians. Current macroscopic crowd models assume that these are static, limiting the ability of models to capture the complex interplay between evolving psychology and collective crowd dynamics that defines a "social crowd". This paper introduces a novel approach by explicitly incorporating an "activity" variable into the modeling framework, which represents the evolving psychological states of pedestrians and is linked to crowd dynamics. To demonstrate the role of activity, we model pedestrian egress when this variable captures stress and awareness of contagion. In addition, to highlight the importance of dynamic changes in activity, we examine a scenario in which an unexpected incident necessitates alternative exits. These case studies demonstrate that activity plays a pivotal role in shaping crowd behavior. The proposed modeling approach thus opens avenues for more realistic macroscopic crowd descriptions with practical implications for crowd management. [ABSTRACT FROM AUTHOR]

This paper focuses on Herbert A. Simon's visionary theory of the Artificial World. The artificial world evolves over time as a result of various actions, including interactions with the external world as well as interactions among its internal components. This paper proposes a mathematical theory of the conceptual framework of the artificial world. This goal requires the development of new mathematical tools, inspired in some way by statistical physics and stochastic game theory. The mathematical theory is applied in particular to the study of the dynamics of organizational learning, where cooperation and competition evolve through decomposition and recombination of organizational structures; the effectiveness of the evolutionary changes depends on the dynamic prevalence of cooperative over selfish behaviors, showing features common to the evolution of all living systems. [ABSTRACT FROM AUTHOR]

The modeling and control of epidemics, such as the novel Coronavirus, have become crucial on a global scale, for effective management of epidemic situations. This paper is focused on using the mean field approach for modeling and control of epidemics. The mean field approach is an effective alternative to the classical approach of using continuous time Markov decision process (CTMDP) models, which suffer from the curse of dimensionality and entail knowledge of global state information. The mean field approach captures the collective behavior of a dynamic system consisting of numerous interacting nodes representing individuals in the population. The objectives of this paper are twofold: (a) to provide an overview of the mean field approach to epidemic modeling and control, and (b) to present recent advances in this area. Emphasizing the importance of containing and suppressing epidemic spread through non-pharmaceutical interventions, the paper highlights the need to minimize loss of lives, reduce suffering, and alleviate the burden on the public healthcare system. A potential challenge here is the presence of a section of the population who act on their free will and deviate from recommended best practices, which could lead to a potential public health crisis. Motivated by this, the paper explores two specific threads to modeling and control. The first thread assumes that individual nodes comply with a socially optimal control policy mandated by a regulatory authority. The second thread allows for independent and strategic behavior by the individual nodes, modeled as a mean field game, where the strategies of rational agents are based on mean field Nash equilibria. The paper begins by discussing the modeling of epidemics using an extended SIVR (Susceptible-Infected-Vaccinated-Recovered) compartmental model, accompanied by an illustrative example. Next, a literature review is provided, focusing on the mean field approach for socially optimal control of epidemics and how a regulatory authority can effectively contain the spread. Subsequently, the paper presents an update on the use of mean field game-based approaches in studying epidemic spread and control. Finally, future research directions in this important area are discussed. [ABSTRACT FROM AUTHOR]

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process. [ABSTRACT FROM AUTHOR]

Social insects are ecologically and evolutionarily the most successful organisms on earth and can achieve robust collective behaviors through local interactions among group members. Colony migration has been considered as a leading example of collective decision making in social insects. In this paper, a piecewise colony migration system with recruitment switching is proposed to explore underlying mechanisms and synergistic effects of colony size and quorum on the outcomes of collective decision. The dynamical behavior of the nonsmooth system is studied, and sufficient conditions for the existence and stability of equilibrium are provided. The theoretical results suggest that large colonies are more likely to emigrate to a new site. More interesting findings include but are not limited to that (a) the system may exhibit oscillation when the colony size is below a critical level and (b) the system may also exhibit a bistable state, i.e., the colony migrates to a new site or the old nest depending on the initial size of recruiters. Bifurcation analysis shows that the variations of colony size and quorum threshold greatly impact the dynamics. The results suggest that it is important to distinguish between two populations of recruiters in modeling. This work may provide important insights on how simple and local interactions achieve the collective migrating activity in social insects. [ABSTRACT FROM AUTHOR]

The escalating process of urbanization has raised concerns about incidents arising from overcrowding, necessitating a deep understanding of large human crowd behavior and the development of effective crowd management strategies. This study employs computational methods to analyze real-world crowd behaviors, emphasizing self-organizing patterns. Notably, the intersection of two streams of individuals triggers the spontaneous emergence of striped patterns, validated through both simulations and live human experiments. Addressing a gap in computational methods for studying these patterns, previous research utilized the pattern-matching technique, employing the Nelder-Mead Simplex algorithm for fitting a two-dimensional sinusoidal function to pedestrian coordinates. This paper advances the pattern-matching procedure by introducing Simulated Annealing as the optimization algorithm and employing a two-dimensional square wave for data fitting. The amalgamation of Simulated Annealing and the square wave significantly enhances pattern fitting quality, validated through statistical hypothesis tests. The study concludes by outlining potential applications of this method across diverse scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time-consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior based on variational (approximate Bayesian) inference. Expanding upon Husserl's model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory, in particular, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitates the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals. [ABSTRACT FROM AUTHOR]

Inspired by the collective behaviors of active systems in nature, the collective behavior of micromotors has attracted more and more attention in recent years. However, little attention has been paid to the collective behavior of the immobilized micromotor, i.e., the micropump. In this paper, a unique pentacene-based micropump is reported, which demonstrates dynamic collective behavior activated by white light irradiation. The light irradiation may generate the photochemical reactions between pentacene and water, leading to the electroosmotic flow. As a result, this micropump is capable of pumping the surrounding solution inward along the substrate surface based on the electroosmosis mechanism. Intriguingly, the inward pumping causes the agglomeration of the tracer particles on the surface of the micropump. In addition, the aggregation can migrate following the change in the light irradiation position between two adjacent micropumps. Based on the aggregating and migrating behaviors of this pentacene-based micropump, we have achieved the conductivity restoration of the cracked circuit. [ABSTRACT FROM AUTHOR]

We consider the car following problem for a set of autonomous vehicles following each other on either an infinite or circular road. The behavior of each car is specified by its "speed regulator", a device that decides to increase or decrease the speed of the car as a function of the head-tail distance to its predecessor and the speed of both cars. A collective behavior emerges that corresponds to previously proposed cellular automata traffic models. We further analyze the traffic patterns of the system in the long term, as governed by the speed regulator and we study under which conditions traffic patterns of maximum flow can or cannot be reach. We show the existence of suboptimal flow conditions that require external coordination mechanisms (that we do not consider in this paper) in order to reach the optimal flow achievable with the given density. In contrast with other approaches, we do not try to reproduce observed or measured traffic patterns. We analyze a deterministic speed regulator in order to decipher the emergent dynamics, and to ponder what maneuvers can be safely performed. Here, we restrict our attention to the car following problem. By comparing our speed regulator with classical models, auch as the Nagel–Schreckenberg and KKW models, we observe that although our regulator is formulated in simple terms, its dynamics share similarities with these models. In particular, the KKW model is designed to reproduce the observed behavior that a trailing car in the synchronization range of the leading car tends to regulate its speed to maintain a constant distance. this same behavior is adopted by our speed regulator, showing that this is a safe way of driving. [ABSTRACT FROM AUTHOR]

My response to Carla Leone's paper focuses on how psychoanalysts perceive the significant others of their patients is an attempt to both endorse raising this important question and to augment our thinking about it. Leone identifies the dynamic backdrop for the tendency of individual therapists to unquestionably accept their patient's representation of the significant other. From an interpersonal-relational and family systemic perspective, I raise other dynamic possibilities regarding transference and countertransference, the self as co-constructed with the significant other, introduce the importance of attachment theory, and suggest creative ways to keep a balanced view of the partner who is not known to the analyst except through the patient's representation. [ABSTRACT FROM AUTHOR]

Several algorithms have been proposed in recent decades to solve optimization problems, some of which have been inspired by nature. The collective behavior of ants is an example of intelligence in the nature which has been a source of inspiration for optimization algorithms. Based on the concept of path integration, ants are able to continuously calculate their current location from their previous trajectory when exploring to find food. In the return journey, they go by calculating the outcome of the route, without returning exactly from their path, choosing a direct path to return to their original point. In this paper, the behavior of desert ants in finding their return path to the nest is the source of inspiration. By modeling the swarm intelligence of these ants based on the results of empirical scientific research, a new optimization algorithm is presented, termed Ant Path Integration. In this paper, the mathematical relationships governing the concept of path integration are presented. Simulation results on a variety of benchmark functions, including some of the CEC2019 benchmarks, show the superiority of this algorithm in finding the optimal solution in comparison to some well-known optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a method of generating collective behavior of a multi-legged robotic swarm using deep reinforcement learning. Most studies in swarm robotics have used mobile robots driven by wheels. These robots can operate only on relatively flat surfaces. In this study, a multi-legged robotic swarm was employed to generate collective behavior not only on a flat field but also on rough terrain fields. However, designing a controller for a multi-legged robotic swarm becomes a challenging problem because it has a large number of actuators than wheeled-mobile robots. This paper applied deep reinforcement learning to designing a controller. The proximal policy optimization (PPO) algorithm was utilized to train the robot controller. The controller was trained through the task that required robots to walk and form a line. The results of computer simulations showed that the PPO led to the successful design of controllers for a multi-legged robotic swarm in flat and rough terrains. [ABSTRACT FROM AUTHOR]

It is a significant issue to explore the collective behaviors of the delayed complex systems. This paper studies asymptotic collective behaviors of a delayed multi‐particle system with multiple processes while particles interact directionally and locally. Up to now, the studies on dynamic behaviors of multi‐particle systems do not involve limited processing capacity, that is, the information queuing phenomenon is ignored. To this aim, we introduce multiple processes to formulate the evolution of a delayed multi‐particle system. To optimize the delay parameters, we choose the greedy principle to set the processing order. The main idea is to decompose the velocity into each process and use functional differential equation theory and matrix theory to constrain the velocity variation rate while the processing order is fixed. As the new results, in the non‐critical and general situation, when the time delay is less than or equal to the threshold value and the eigenvalue 1 of the average matrix is semi‐simple, we achieve the criterion of flocking and multi‐cluster with exponential convergence rate involving multiple processes. Furthermore, we get the lower bound estimate of the processing capacity. [ABSTRACT FROM AUTHOR]

Exploring the collective behavior of neurons in complex networks has been a hot topic in complex network analysis. In this regard, many studies have been conducted to explore the collective behavior of neurons using different flow-based and map-based neuronal models. The map-based models have some advantages over the flow-based models, such as their simplicity, efficiency, and flexibility. This paper investigates the collective behaviors of a recently proposed one-dimensional piecewise nonlinear map-based neuronal model with rich dynamical properties. The neurons are configured in a Watts–Strogatz small-world network structure under five conditions considering electrical synapses, inner linking functions, chemical synapses, both electrical and chemical synapses, and both inner linking and chemical synaptic functions as the neurons' communication channels. We found that the electrically coupled neurons cannot achieve an entirely synchronous firing pattern; however, in other fourth cases, they can reach a synchronous state. Besides the synchronization state, cluster synchronization, imperfect synchronization, chimera, and solitary state are other firing patterns observed in the studied network. [ABSTRACT FROM AUTHOR]

Indirect benefits obtained through the reproduction of relatives are fundamental in the formation and maintenance of groups. Here, we examine the hypothesis that females of the temperate paper wasp Polistes dominulus preferentially form groups with close relatives. Genetic relatedness data were obtained for 180 groups of females collected at the early stages of the nesting cycle of a large population of P. dominulus in two sites in southwestern Spain. Average within-group relatedness values ranged from 0.189 to 0.491. Foundresses on early nests were significantly more closely related than females in winter aggregations or in stable groups (just before workers emerged). Within-group relatedness values were independent of group size. The vast majority of worker-producing nests ( c. 85%) had one or more females that were unrelated (or distantly related) to the remaining members of the group. These results provide further support to the hypothesis that indirect fitness benefits alone are unlikely to explain why P. dominulus foundresses form cooperative associations. [ABSTRACT FROM AUTHOR]

Understanding emergent collective phenomena in biological systems is a complex challenge due to the high dimensionality of state variables and the inability to directly probe agent-based interaction rules. Therefore, if one wants to model a system for which the underpinnings of the collective process are unknown, common approaches such as using mathematical models to validate experimental data may be misguided. Even more so, if one lacks the ability to experimentally measure all the salient state variables that drive the collective phenomena, a modeling approach may not correctly capture the behavior. This problem motivates the need for model-free methods to characterize or classify observed behavior to glean biological insights for meaningful models. Furthermore, such methods must be robust to low dimensional or lossy data, which are often the only feasible measurements for large collectives. In this paper, we show that a model-free and unsupervised clustering of high dimensional swarming behavior in midges (Chironomus riparius), based on dynamical similarity, can be performed using only two-dimensional video data where the animals are not individually tracked. Moreover, the results of the classification are physically meaningful. This work demonstrates that low dimensional video data of collective motion experiments can be equivalently characterized, which has the potential for wide applications to data describing animal group motion acquired in both the laboratory and the field. [ABSTRACT FROM AUTHOR]

This paper provides a survey of recent research progress in mathematical modeling and distributed control of wide-area networks. Firstly, the modeling is introduced for two types of wide-area networks, i.e., coopetitive networks and cooperative networks, with the help of algebraic graph theory. Particularly, bipartite network topologies and cluster network topologies are introduced for coopetitive networks. With respect to cooperative networks, an intermittent clustered network modeling is presented. Then, some classical distributed control strategies are reviewed for wide-area networks to ensure some desired collective behaviors, such as consensus (or synchronization), bipartite consensus (or polarization), and cluster consensus (or fragmentation). Finally, some conclusions and future directions are summarized. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a Boltzmann type model that describes a collective behavior of a large group of individuals. The model considers a mechanism where as two individuals collide, they adopt after the collision the same post-collisional velocity according to a distribution centered at the mid pre-collisional velocity. We show in this paper that the solutions of a spatially homogeneous model on R d converge exponentially towards the equilibrium state for the Wasserstein metric. The convergence of the solutions in the strong-norm L 1 is also proved for initial conditions satisfying a stronger regularity property. In a last part, these results are illustrated numerically. [ABSTRACT FROM AUTHOR]

This document discusses the use of computational simulation in crisis and disaster management. It highlights the advantages of simulation models in examining alternative strategies and predicting the effects of changes in real-world systems. The document also acknowledges the challenges in incorporating realistic human behavior, increasing acceptability of simulation models, and creating multi-scale models. The special issue focuses on current real-world problems and includes papers on topics such as epidemics, seismic crises, population sheltering management, and mass evacuation. Two companion papers on airport evacuation and the Syrian refugee crisis are also mentioned. [Extracted from the article]

Anthropologists have recently argued that the divide between nature and culture is not a universal framework suitable for understanding collective behavior but rather a local variation among various ways of composing the experience of the world. Notably, in the case of Philippe Descola’s anthropology, this critique led to a radical reconceptualization of social sciences and the humanities in terms of ontological regimes, which draws upon key aspects of the phenomenological tradition. In this paper, I develop a phenomenological perspective on Descola’s anthropology to clarify whether and how we can assess our engagement with the world beyond the divide between nature and culture. The paper is divided into three sections. In the first section, I present the main claims of Descola’s position, which he calls “relative universalism,” and introduce two critiques of this project: the potential conflation between his ontological framework and aspects of modern naturalism and the risk of reifying cultural determinations as ontological properties. In the second section, I address the first critique by showing how the universalist claim of Descola’s anthropology, according to which collective experience is organized by the duality of planes of physicality and interiority, can be elucidated through Husserl’s account of the embodied experience to avoid a conflation with the naturalist framework. Finally, I contend that anthropology’s idea of a diversity of ontological regimes can be made coherent by analyzing the two layers of the world constitution: the primordial experience of the lived body and the intersubjective process of communalization. [ABSTRACT FROM AUTHOR]

The collective behaviors of community members in dynamic bitcoin transaction network are significant to understand the evolutionary characteristics of communities for bitcoin transaction network. In this paper, we empirically investigate the behavior evolution of new nodes forming communities for the bitcoin transaction network. First, we divide the bitcoin transaction network into multiple time segments, and detect community on each time segment. Then, according to the set similarity method, we mark the community with maximal similarity σ < 0. 0 0 1 at adjacent timestamps as the new community. Finally, we propose an evolution index to illustrate the evolution trend of new nodes forming communities, and introduce the reshuffle model to compare with it. The results show that there are obvious differences in the early stage, and new traders tend to join new communities. However, after August 2011, the trends of before and after reorganization are very similar, which indicates that in bitcoin trading, the behaviors of new traders forming communities become random. Our work may be helpful for the understanding of user behavior characteristics in bitcoin trading, and provide a new perspective for the research of bitcoin transaction network. [ABSTRACT FROM AUTHOR]

This article provides comparative perspectives on collective behaviors that are widely found throughout the animal kingdom, ranging from insect and crustacea swarms, fish schools, bird flocks, and mammal herds to human crowds. Studies of nonhuman animal and human collective behaviors have progressed almost separately even though they have a similar history. Theoretical studies have investigated the reproduction of collective phenomena from simple inter-individual rules, and subsequent empirical and experimental studies have found diverse and complex collective behaviors that are difficult to explain with classical theoretical models. As a consequence, a wide variety of interaction rules have been proposed. To determine models to be implemented in nature and find fundamental mechanisms of collective behaviors, this paper argues that we should compare collective behaviors among various species while adopting Tinbergen's four questions regarding mechanism, function, development, and evolution as a methodological basis. As an example of a comparative collective behavior paradigm, we introduce our studies in which a mutual anticipation mechanism inspired by nonhuman animal collective behaviors can be linked to a self-organization function in human collective behaviors. We expect that the study of comparative collective behaviors will expand, the methodology will become more sophisticated, and new perspectives regarding the multitemporal features of collective behaviors will emerge. [ABSTRACT FROM AUTHOR]

In this paper, we present a survey of recent progress on the emergent behaviors of stochastic particle models which arise from the modeling of collective dynamics. Collective dynamics of interacting autonomous agents is ubiquitous in nature, and it can be understood as a formation of concentration in a state space. The jargons such as aggregation, herding, flocking and synchronization describe such concentration phenomena. Recently it became one of the emerging topics in the applied mathematics community due to possible engineering applications and close relation with nonlocal partial differential equations. When an autonomous agent system interacts with unknown environment as an open system, the effects of hidden and unidentified interactions between the environment and the autonomous system are often realized by stochastic noises in agent dynamics, and the temporal evolution of the autonomous system results in stochastic collective models. From the viewpoint of dynamical systems theory, it is very interesting how collective dynamics emerges from initial state. As concrete examples, we consider four specific stochastic collective models (stochastic Winfree and Kuramoto models for synchronization, the stochastic Cucker–Smale model for flocking, and a first-order stochastic nonlinear consensus model), and we also briefly review the state-of-the-art results for these models on the emergence of collective dynamics and discuss their applications in finance and optimization. [ABSTRACT FROM AUTHOR]

A number of studies have constructed coarse-grained (CG) models of water to understand its anomalous properties. Most of these properties emerge at low temperatures, and an accurate CG model needs to be applicable to these low-temperature ranges. However, direct use of CG models parameterized from other temperatures, e.g., room temperature, encounters a problem known as transferability, as the CG potential essentially follows the form of the many-body CG free energy function. Therefore, temperature-dependent changes to CG interactions must be accounted for. The collective behavior of water at low temperature is generally a many-body process, which often motivates the use of expensive many-body terms in the CG interactions. To surmount the aforementioned problems, we apply the Bottom-Up Many-Body Projected Water (BUMPer) CG model constructed from Paper I to study the low-temperature behavior of water. We report for the first time that the embedded three-body interaction enables BUMPer, despite its pairwise form, to capture the growth of ice at the ice/water interface with corroborating many-body correlations during the crystal growth. Furthermore, we propose temperature transferable BUMPer models that are indirectly constructed from the free energy decomposition scheme. Changes in CG interactions and corresponding structures are faithfully recapitulated by this framework. We further extend BUMPer to examine its ability to predict the structure, density, and diffusion anomalies by employing an alternative analysis based on structural correlations and pairwise potential forms to predict such anomalies. The presented analysis highlights the existence of these anomalies in the low-temperature regime and overcomes potential transferability problems. [ABSTRACT FROM AUTHOR]

In this paper, we consider the spread of an epidemic on a changing network, specifically focusing on two phenomena. The first part of the paper investigates a possible mechanism of disease outbreaks on college campuses. We present a toy model, dividing students into extroverts (high-degree nodes with a large number of contacts) and introverts (low-degree nodes with a small number of contacts). In our model, the average degree of extroverts is evolving with time, and its dynamics is coupled with the current epidemic situation: extroverts tend to increase their number of contacts for low level of epidemic, but as more and more students get infected, they start decreasing their average degree. Another phenomenon analyzed in the paper is vaccination: how should the vaccine be allocated to best benefit the population? We consider two possible vaccination strategies: (1) vaccinating people starting from high risk groups (older people with a higher risk of mortality) or (2) prioritizing vaccination of people with a higher number of contacts (such as college students) to decrease the epidemic outbreak. Both phenomena show the importance of diversity in the number of contacts. [ABSTRACT FROM AUTHOR]

This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, since each IoT can be represented as a process; a lattice can also be considered one of the best mathematical structures to abstract the collective behavior of IoTs since it has the hierarchical structure to represent multi-dimensional aspects of the interactions of IoTs. The dual approach using two mathematical structures is very challenging since the process algebra have to provide an expressive power to describe the smart behavior of IoTs, and the lattice has to provide an operational capability to handle the state-explosion problem generated from the interactions of IoTs. For these purposes, this paper presents a process algebra, called dTP-Calculus, which represents the smart behavior of IoTs with non-deterministic choice operation based on probability, and a lattice, called n:2-Lattice, which has special join and meet operations to handle the state explosion problem. The main advantage of the method is that the lattice can represent all the possible behavior of the IoT systems, and the patterns of behavior can be elaborated by finding the traces of the behavior in the lattice. Another main advantage is that the new notion of equivalences can be defined within n:2-Lattice, which can be used to solve the classical problem of exponential and non-deterministic complexity in the equivalences of Norm Chomsky and Robin Milner by abstracting them into polynomial and static complexity in the lattice. In order to prove the concept of the method, two tools are developed based on the ADOxx Meta-Modeling Platform: SAVE for the dTP-Calculus and PRISM for the n:2-Lattice. The method and tools can be considered one of the most challenging research topics in the area of modeling to represent the collective behavior of Smart IoT Systems. [ABSTRACT FROM AUTHOR]

Flow pattern is an important factor affecting fish collective behavior. In the reproductive season, schizothorax prenati will choose suitable flow area for migration, but the water conservancy project caused serious damage to its migration channel. The purpose of this study was to study the response of the collective behavior of Schizothorax prenati to the flow velocity and turbulent kinetic energy, to optimize the flow pattern and water management of the fish crossing facilities. In this paper, the Nearest Neighbor Distance (NND) and collective area were used to evaluate the cohesion of fish, and the Separation Swimming Index (SSI) was used to evaluate the repulsion of fish. In the experiment, 10 Schizothorax prenati with an average age of 3 years were selected from each group, a total of two groups, and each group was tested for 6 times. Each time allowed the fish to swim in a 3 m×14 m rectangular flume physical model (flow velocity decreased from 0.17 m/s on the left to 2.5 m/s on the right, and the turbulent energy was 0-0.225 m² /s2 ) and (16±1) ℃ water temperature. After shooting with the camera, the graph was collected to extract and analyze the swimming trajectory of the fish, at the same time, we observed the formation of fish in moving water and all collective indicators were calculated at 10-second intervals during the analysis of the fish. All coordinates recorded in a total of 12 tests were plotted as heat map, the darker the color, the more likely the fish were to cluster in that area, meanwhile, we calculated the NND, collective area and SSI of fish at all recorded times. Finally, by coupling the above three collective indexes with flow velocity and turbulent kinetic energy, the collective indexes under different hydraulics conditions were analyzed to evaluate the collective behavior of fish. The results showed that Schizothorax prenati had obvious collective behavior under specific hydraulic conditions, and its collective behavior was closely related to hydraulic factors. In this experiment, the fish mainly swam in the formation of “schooling” mode, and fish usually swam in a single partition in the experimental flume, showing a neatly arranged polar structure, and all individuals tended to swim in the same direction when the flow velocity was 0.27-0.70 m/s and the turbulent kinetic energy was 0.004-0.018 m² /s², the NND, collective area and SSI of the fish were 0-0.80 m, 0-5.00 m² and 0-1.20, and the collective behavior was the most compact and close, with better cohesion and repulsion. In the two typical swimming upstream routes, the average flow velocity and turbulence kinetic energy were basically consistent with the compact collective behavior hydraulic conditions of the Schizothorax prenati summarized in this paper, which verified the accuracy of this study. Therefore, it is recommended that engineering measures be taken to create a flow velocity of 0.27-0.70 m/s and a kinetic energy of 0.004-0.018 m² /s² in order to make the Schizothorax prenati mass migration, which allowed for a smaller flow velocity range than traditional fishway design methods. [ABSTRACT FROM AUTHOR]

The present paper aims to propose a new type of information-theoretic method to augment the ratio of collective potentiality to its cost for the interpretation. Collective potentiality represents how many components, such as connection weights or neurons, are collectively used to deal with relations between inputs and outputs, while individual potentiality shows how an individual component potentially contributes to the inputs or outputs. From our viewpoint, the conventional interpretation methods have focused on the individual potentiality of components, ignoring these collective behaviors. Thus, the present paper stresses that neural networks should try to deal with the collective properties of components and to examine the final results from multiple points of view. For implementing this concept of potentiality maximization, we introduce the complementary potentiality minimization, which aims to reduce the strength of larger weights as much as possible, and at the same time, it can be used for increasing the potentiality. The method was applied to two intuitively interpretable data sets, namely, the absenteeism and online shoppers data sets. With both data sets, experimental results confirmed that the method could increase collective potentiality and considerably reduce the corresponding cost. The new method could extract many groups of weights with the same small strength, all of which tried to respond to the coming inputs, while the conventional methods, including the regularization ones, tried to produce a smaller number of stronger connection weights very selectively. In particular, the present method could produce stable compressed weights similar to the original correlation coefficients of the data sets, meaning that simple, independent, and linear relations could be detected, contrary to the presupposed non-linear ones. Thus, the collective interpretation, applied to two business data sets, revealed a possibility that, behind seemingly complicated non-linear relations between inputs and outputs, neural networks with collective potentiality augmentation with a smaller cost can extract the very simple relations for easy interpretation. All complicated relations can possibly be generated, based on those simple ones. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have been increasingly used in the context of remote sensing missions such as target search and tracking, mapping, or surveillance monitoring. In the first part of our paper we consider agent dynamics, network topologies, and collective behaviors. The objective is to enable multiple UAVs to collaborate toward a common goal, as one would find in a remote sensing setting. An agreement protocol is carried out by the multi-agents using local information, and without external user input. The second part of the paper focuses on the equations of motion for a specific type of UAV, the quadcopter, and expresses them as an affine nonlinear control system. Finally, we illustrate our work with a simulation of an agreement protocol for dynamically sound quadcopters augmenting the particle graph theoretic approach with orientation and a proper dynamics for quadcopters. [ABSTRACT FROM AUTHOR]

Recent scholarship has mapped the dynamic between humiliation and violence, including the role of trauma and self-esteem. While existing research has mostly focused on individuals, there is a strong case for applying this framework to the macrosocial level. Taking an interdisciplinary approach that combines psychoanalysis, cultural anthropology, and political sociology, this paper carries out a step-by-step application of Gilligan's shame/violence theory to the case of Greece, focusing on the post-2009 era known as "the Crisis." This paper outlines the root causes of the shame/violence dynamic in Greece, with reference to communitarian moral codes and honor crimes, as well as political divisions, unresolved trauma, and shame/violence spirals originating in the mid-twentieth century. It then examines the role of humiliation during the current economic crisis, as well as the surge of political aggression. The application of Gilligan's theory provides us with a compelling interpretation of civic culture in contemporary Greece, throwing light on patterns of collective self-harming behavior ("suicide by cop") – as both a possible result of subjective humiliation, and a means of seeking pity and attention. The paper also identifies the existence of accumulated shame, which could lead to outbreaks of political extremism. [ABSTRACT FROM AUTHOR]

Data sparsity and long-tail items recommendation, two typical problems of recommender systems, can be effectively alleviated by leveraging user-side information and item-side information. Knowledge graph (KG) is a source of side information. Due to the specificity of data distribution, the current KG-enhanced recommendation algorithms have the problem of weak generalization and robustness (the performance varies greatly in multiple data sets). Moreover, the previous knowledge-aware recommendation models are insufficient to distill the collaborative signal and personal features from the collective behaviors of users while simultaneously distilling the connectivity and exclusive entity features from the knowledge graph on the item-side. In this paper, we propose a multi-task framework Multi-Rec with high flexibility and adaptability based on alternating learning, which consists of a recommender system (RS) task and a knowledge graph embedding (KGE) task. The RS task consists of user feature learning and user structure learning modules. Correspondingly, the KGE task consists of knowledge graph feature learning and knowledge graph structure learning modules. The correlation between each sub-learning task is carried out by designed cross units and exchange units. Through the end-to-end framework, our model generates high-efficiency node embeddings for downstream recommender tasks and outperforms several state-of-the-art baselines on four real-world datasets through extensive experiments. [ABSTRACT FROM AUTHOR]

In social insects, collective behavior is regulated by substances secreted by various glands of different members of society. In this study, we investigate whether salivary glands of the arboreal termite Nasutitermes corniger (Motschulsky) (Isoptera: Termitidae) contain substances that regulate the intensity of foraging behavior. Foraging arenas of N. corniger nests maintained under laboratory conditions received the addition of two filter paper substrates, one impregnated with salivary gland extract from third‐instar small workers (SW3) (treatment) and the other impregnated with distilled water (control). A similar experiment was conducted using salivary gland extract from fourth‐instar large workers (LW4). Occurrences of initial exploration, mass recruitment, and substrate gnawing behaviors, as well as numbers of recruited and gnawing workers, were recorded. There was a higher rate of mass recruitment to treated substrates than to the control (SW3: 80 vs. 25%; LW4: 90 vs. 25%). Gnawing behavior also occurred at higher rates on treated substrates (SW3: 95 vs. 35%; LW4: 100 vs. 45%). The number of workers recruited to and gnawing on treated substrate was significantly higher than the number of workers recruited to and gnawing on control substrate. The higher rates of worker recruitment, as well as the higher numbers of recruited and gnawing workers, show that salivary glands of last‐instar workers contain substances with arresting and/or phagostimulating pheromonal actions or substances that release compounds with these properties. [ABSTRACT FROM AUTHOR]

This paper considers a Cournot oligopoly model with an arbitrary number of rational agents under incomplete information in the classical case (linear cost and demand functions). Within the dynamic reflexive collective behavior model, at each time instant each agent adjusts his output, taking a step towards the maximum profit under the expected choice of the competitors. Convergence conditions to a Cournot–Nash equilibrium are analyzed using the errors transition matrices of the dynamics. Restrictions on the ranges of agents' steps are imposed and their effect on the convergence properties of the dynamics is demonstrated. Finally, a method is proposed to determine the maximum step ranges ensuring the convergent dynamics of collective behavior for an arbitrary number of agents. [ABSTRACT FROM AUTHOR]

External gradients can strongly influence the collective behavior of microswimmers. In this paper, under an external linear chemical gradient, we study the behavior of two hydrodynamically interacting self-propelled chiral swimmers in the low-Reynolds number regime. We use the generalized squirmer model called the chiral squirmer, a spherically shaped body with an asymmetric surface slip velocity, to represent the swimmer. We find that the external gradient favors the attraction between the swimmers and, in some situations, leads to a bounded state in which the swimmers move in a highly synchronous manner. Furthermore, due to this cooperative motion, these swimmers reach the chemical target faster than individual swimmers. This study may help in understanding the collective behavior of chiral swimmers and in designing synthetic microswimmers for targeted drug delivery. [ABSTRACT FROM AUTHOR]

Exposed to the constant light, the master clock located in the bilaterally paired suprachiasmatic nucleus (SCN) above the optic chiasma exhibits three rhythmic behaviors in hamsters. Some hamsters remain or lose circadian rhythms due to synchronization or desynchronization between the SCN neurons, respectively. Interestingly, the other hamsters show a phenomenon called "split", in which the left SCN and right SCN oscillate with a stable anti-phase. In this paper, a modified Kuramoto model is built to explain these three rhythmic behaviors, where the phase-shift of cellular coupling is taken into account. Three cases of phase-shifts are considered, including that first case exists in all the SCN neurons, second case exists between the left and right SCN, and the last case exists within each group. We found that the phase-shift is able to induce the anti-phase synchronization between the left SCN and right SCN in the former two cases, but eliminate this anti-phase synchronization in the latter case. Our findings provide an alternative explanation for the emergency of the split and shed light on the collective behaviors of the SCN neurons. [ABSTRACT FROM AUTHOR]

The interplay between system dynamics and topological structure plays a crucial role in nonlinear system and modern network science. In this paper, we intend to investigate the effects of both heterogeneity and asymmetric coupling on the collective behaviors of Kuramoto oscillators, and reveal the processes how the synchronization transits to multi-cluster or disorder states. We find that the complete synchronization and anti-phase synchronization states in a star motif change into the remote synchronization state, owing to the disparity in the values of oscillators' natural frequencies, and further become into multi-cluster due to the existence of multiple hubs in complete bipartite network, and finally achieve disorder state in complex networks. These findings are helpful to understand the forming processes of remote synchronization and general multi-cluster in coupled systems. [ABSTRACT FROM AUTHOR]

The in situ measurement of the bioelectric potential in xilematic and floematic superior plants reveals valuable insights into the biological activity of these organisms, including their responses to lunar and solar cycles and collective behaviour. This paper reports on the "Cyberforest Experiment" conducted in the open-air Paneveggio forest in Valle di Fiemme, Trento, Italy, where spruce (i.e., Picea abies) is cultivated. Our analysis of the bioelectric potentials reveals a strong correlation between higher-order complexity measurements and thermodynamic entropy and suggests that bioelectrical signals can reflect the metabolic activity of plants. Additionally, temporal correlations of bioelectric signals from different trees may be precisely synchronized or may lag behind. These correlations are further explored through the lens of quantum field theory, suggesting that the forest can be viewed as a collective array of in-phase elements whose correlation is naturally tuned depending on the environmental conditions. These results provide compelling evidence for the potential of living plant ecosystems as environmental sensors. [ABSTRACT FROM AUTHOR]

Synchronization has an important role in neural networks dynamics that is mostly accompanied by cognitive activities such as memory, learning, and perception. These activities arise from collective neural behaviors and are not totally understood yet. This paper aims to investigate a cortical model from this perspective. Historically, epilepsy has been regarded as a functional brain disorder associated with excessive synchronization of large neural populations. Epilepsy is believed to arise as a result of complex interactions between neural networks characterized by dynamic synchronization. In this paper, we investigated a network of neural populations in a way the dynamics of each node corresponded to the Jansen–Rit neural mass model. First, we study a one-column Jansen–Rit neural mass model for four different input levels. Then, we considered a Watts–Strogatz network of Jansen–Rit oscillators. We observed an epileptic activity in the weak input level. The network is considered to change various parameters. The detailed results including the mean time series, phase spaces, and power spectrum revealed a wide range of different behaviors such as epilepsy, healthy, and a transition between synchrony and asynchrony states. In some points of coupling coefficients, there is an abrupt change in the order parameters. Since the critical state is a dynamic candidate for healthy brains, we considered some measures of criticality and investigated them at these points. According to our study, some markers of criticality can occur at these points, while others may not. This occurrence is a result of the nature of the specific order parameter selected to observe these markers. In fact, The definition of a proper order parameter is key and must be defined properly. Our view is that the critical points exhibit clear characteristics and invariance of scale, instead of some types of markers. As a result, these phase transition points are not critical as they show no evidence of scaling invariance. [ABSTRACT FROM AUTHOR]

Analyzing and understanding the movement patterns of the citizen's with in a city, plays an important role in urban and transportation planning. Though many recent research papers focused on mining LBSN services data and performed in-depth analysis of users' mobility patterns and their impact on their social inter-connections and friends. This paper focuses on understanding the Citizen's movement patterns of socially interconnected users in friendship networks, by analyzing their spatial-temporal footprints/check-ins. The aim of this paper is to find the impact of structural patterns hidden in the nodes of a friendship network and external environment changes on the check-in patterns of the users. First, we classify each spatial check-in event based on its cause into either self reinforcing behavior or social influence or external stimulus. Then we mine the collective behavior of the all the users during some special events. [ABSTRACT FROM AUTHOR]