Emergence Of Collective Phenomena Research Articles

Recovery of dynamical similarity from lossy representations of collective behavior of midge swarms.

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.

More is different: On the emergence of collective phenomena in fractured rocks

Fractures widely exist in crustal rocks and form complex networks dominating the bulk behaviour of geological media. Thus, understanding how fracture networks affect subsurface processes/phenomena is highly relevant to many rock engineering applications. However, the large-scale behaviour of a fractured rock mass consisting of numerous fractures and rocks cannot be predicted by simple applications of the knowledge of individual fractures and/or rocks, due to upscaling complexities involving the hierarchy of scales, heterogeneities, and physical mechanisms as well as the possible emergence of qualitatively different macroscopic properties. In other words, macroscopic phenomena in fractured rocks arise from the many-body effects (i.e. collective behaviour) of numerous interacting fractures and rocks, such that the emergent properties at the fracture system scale are much richer than those of individual components. Hence, more is different! This paper gives a discussion on the mechanism of emergence in fractured media from a combined statistical physics and rock mechanics perspective, and further presents a multiscale conceptual framework to link microscopic responses of single fractures/rocks to macroscopic behaviour of rock masses consisting of many fractures and rocks. This framework can serve as a useful tool to bridge experimentally-established constitutive relationships of fracture/rock samples at the laboratory scale to phenomenologically-observed macroscopic properties of fractured rock masses at the site scale.

Dissipative phase transition in an open Tavis–Cummings model with a two-photon drive

We investigate the steady-state quantum phases and associated collective phenomena of an open Tavis–Cummings (TC) model driven by a two-photon source. The standard (non-dissipative) TC model describes the quantum phases of an ensemble of N q two-level quantum emitters interacting with a single-mode electromagnetic field. The interplay among coherent driving, dissipation, and dipole interactions of the open TC model results in emergent collective phenomena, leading to a dissipative or non-equilibrium phase transition from the normal to the superradiant phase. We solve the Liouvillian equation analytically using the semi-classical mean-field approximation. We carry out stability analysis of the steady-state phases and determine the phase boundary. The use of Holstein–Primakoff transformation in the thermodynamic limit reduces the system to an effective coupled-oscillator model, the solution of which yields collective modes.

Relating plasticity to dislocation properties by data analysis: scaling vs. machine learning approaches

Plasticity modelling has long relied on phenomenological models based on ad-hoc assumption of constitutive relations, which are then fitted to limited data. Other work is based on the consideration of physical mechanisms which seek to establish a physical foundation of the observed plastic deformation behavior through identification of isolated defect processes (’mechanisms’) which are observed either experimentally or in simulations and then serve to formulate so-called physically based models. Neither of these approaches is adequate to capture the complexity of plastic deformation which belongs into the realm of emergent collective phenomena, and to understand the complex interplay of multiple deformation pathways which is at the core of modern high performance structural materials. Data based approaches offer alternative pathways towards plasticity modelling whose strengths and limitations we explore here for a simple example, namely the interplay between rate and dislocation density dependent strengthening mechanisms in fcc metals.

Generalized splay states in phase oscillator networks.

Networks of coupled phase oscillators play an important role in the analysis of emergent collective phenomena. In this article, we introduce generalized m-splay states constituting a special subclass of phase-locked states with vanishing mth order parameter. Such states typically manifest incoherent dynamics, and they often create high-dimensional families of solutions (splay manifolds). For a general class of phase oscillator networks, we provide explicit linear stability conditions for splay states and exemplify our results with the well-known Kuramoto-Sakaguchi model. Importantly, our stability conditions are expressed in terms of just a few observables such as the order parameter or the trace of the Jacobian. As a result, these conditions are simple and applicable to networks of arbitrary size. We generalize our findings to phase oscillators with inertia and adaptively coupled phase oscillator models.

Threefold way to the dimension reduction of dynamics on networks: An application to synchronization

Several complex systems can be modeled as large networks in which the state of the nodes continuously evolves through interactions among neighboring nodes, forming a high-dimensional nonlinear dynamical system. One of the main challenges of Network Science consists in predicting the impact of network topology and dynamics on the evolution of the states and, especially, on the emergence of collective phenomena, such as synchronization. We address this problem by proposing a Dynamics Approximate Reduction Technique (DART) that maps high-dimensional (complete) dynamics unto low-dimensional (reduced) dynamics while preserving the most salient features, both topological and dynamical, of the original system. DART generalizes recent approaches for dimension reduction by allowing the treatment of complex-valued dynamical variables, heterogeneities in the intrinsic properties of the nodes as well as modular networks with strongly interacting communities. Most importantly, we identify three major reduction procedures whose relative accuracy depends on whether the evolution of the states is mainly determined by the intrinsic dynamics, the degree sequence, or the adjacency matrix. We use phase synchronization of oscillator networks as a benchmark for our threefold method. We successfully predict the synchronization curves for three phase dynamics (Winfree, Kuramoto, theta) on the stochastic block model. Moreover, we obtain the bifurcations of the Kuramoto-Sakaguchi model on the mean stochastic block model with asymmetric blocks and we show numerically the existence of periphery chimera state on the two-star graph. This allows us to highlight the critical role played by the asymmetry of community sizes on the existence of chimera states. Finally, we systematically recover well-known analytical results on explosive synchronization by using DART for the Kuramoto-Sakaguchi model on the star graph.

Functional Heterogeneity in Superorganisms: Emerging Trends and Concepts

Abstract Social insects are biological benchmarks of self-organization and decentralized control. Their integrated yet accessible nature makes them ideal models for the investigation of complex social network interactions, and the mechanisms that shape emergent group capabilities. Increasingly, interindividual heterogeneity, and the functional role that it may play, is seen as an important facet of colonies’ social architecture. Insect superorganisms present powerful model systems for the elucidation of conserved trends in biology, through the strong and consistent analogies that they display with multicellular organisms. As such, research relating to the benefits and constraints of heterogeneity in behavior, morphology, phenotypic plasticity, and colony genotype provides insight into the underpinnings of emergent collective phenomena, with rich potential for future exploration. Here, we review recent advances and trends in the understanding of functional heterogeneity within social insects. We highlight the scope for fundamental advances in biological knowledge, and the opportunity for emerging concepts to be verified and expanded upon, with the aid of bioinspired engineering in swarm robotics, and computational task allocation.

Defect dynamics for self-propelled rods in a doublet of circular confinement

Rod-shaped active micro/nano-particles, such as bacterial and bipolar metallic micro/ nano-motors, exhibit emergent collective phenomena far from equilibrium state compared to passive particles. We apply a simulation approach —dissipative particle dynamics (DPD)— to explore the collectively ordered states of self-propelled rods (SPRs) which rely on the defect dynamics in the rod cluster. The SPRs are confined in a doublet of circular confinement and repel each other when two rods touch each other. When the rod flow in the connecting passage of the circles is suppressed, namely the small passage width, the steady vortex pattern is obtained in the two circles for weak activity. In binary-passage system, long single-passage system, and short single-passage system, ferromagnetic vortices (FMV), uncorrelated vortices (UV), and anti-ferromagnetic vortices (AFMV) can be obtained, respectively. For intermediate activity, a regular periodic oscillation of the rod number n r in one circle can be seen clearly. For strong activity, mixing of high- and low-frequency oscillation is observed. The amplitude of the rod current in the passage I p follows the relation , where F a denotes the self-propelled force.

Unpredictability in pedestrian flow: The impact of stochasticity and anxiety in the event of an emergency

Random interactions among pedestrians, usually produced by psychological effects (e.g. anxiety), can produce collective nontrivial phenomena in their flow dynamics. Here, we improve our understanding on these phenomena by implementing cellular automaton (CA) simulations in a simplified one-dimensional corridor model. We consider different types of randomness that could be used to parametrize social, cultural or psychological behaviors; their incidence on the average evacuation time and standard deviation are studied. The randomness in the pedestrians’ motion allows separating between different phases of movement, which in the end, drives the dynamics. Our study allows to distinguish among emergent collective phenomena, unpredictability, and unexpected Brownian type behavior in certain regimes, that occur in one-dimensional hallways, such as exists in airplanes, cinemas, massive sports events, etc. These findings may be relevant to evacuation procedures in the event of an emergency, where anxiety may compromise people’s performance and the effectiveness of the evacuation plan. Therefore, it could be highly useful to introduce these aspects in the simulation of pedestrian dynamics.

An agent-based framework of active matter with applications in biological and social systems

Active matter, as other types of self-organizing systems, relies on the take-up of energy that can be used for different activities, such as active motion or structure formation. Here we provide an agent-based framework to model these processes at different levels of organization, physical, biological and social, using the same dynamic approach. Driving variables describe the take-up, storage and conversion of energy, whereas driven variables describe the energy consuming activities. The stochastic dynamics of both types of variables follow a modified Langevin equation. Additional nonlinear functions allow one to encode system-specific hypotheses about the relation between driving and driven variables. To demonstrate the applicability of this framework, we recast a number of existing models of Brownian agents and active Brownian particles. Specifically, active motion, clustering and self-wiring of networks based on chemotactic interactions, online communication and polarization of opinions based on emotional influence are discussed. The framework allows one to obtain critical parameters for active motion and the emergence of collective phenomena. This highlights the role of energy take-up and dissipation in obtaining different dynamic regimes.

Collective Phenomena in Heavy Ion Collisions

To create conditions which ruled one billionth of a second after the Big Bang, it is necessary to heat and compact the nuclear matter. During the first microseconds after the Big Bang the universe went through such a phase transition at very high temperatures but very low net baryon density. At very high temperatures or densities, the hadrons melt and their constituents, the quarks and gluons, form a new phase of matter, the so called quark-gluon plasma. Relativistic heavy ion collisions aim to create a quark gluon plasma where quarks and gluons can move freely over volumes that are large in comparison to the typical size of a hadron. When the particles collide at high energies, it leads to the conversion of particle collision participants in a much heavier particle. If the energy density is large enough, after a collision occurs the formation of quark-gluon plasma. In the dense nuclear medium, it comes to collective phenomena such as increased production of strangeness, damping charmonium and collective motion of particles. In nuclear medium, it comes to individual collision of quarks, which also hadronize. Using simulation package Pythia, we analyzed the reaction system that results in individual collisions of quarks and antiquarks, and emergence of collective phenomena.

Collective sedimentation of squirmers under gravity.

Active particles, which interact hydrodynamically, display a remarkable variety of emergent collective phenomena. We use squirmers to model spherical microswimmers and explore the collective behavior of thousands of them under the influence of strong gravity using the method of multi-particle collision dynamics for simulating fluid flow. The sedimentation profile depends on the ratio of swimming to sedimentation velocity as well as on the squirmer type. It shows closely packed squirmer layers at the bottom and a highly dynamic region with exponential density dependence towards the top. The mean vertical orientation of the squirmers strongly depends on height. For swimming velocities larger than the sedimentation velocity, squirmers show strong convection in the exponential region. We quantify the strength of convection and the extent of convection cells by the vertical current density and its current dipole, which are large for neutral squirmers as well as for weak pushers and pullers.

Communication Dynamics and Emergence of Collective Phenomena in SNS

Communication Dynamics and Emergence of Collective Phenomena in SNS

Emergence of electron coherence and two-color all-optical switching in MoS2 based on spatial self-phase modulation

Generating electron coherence in quantum materials is essential in optimal control of many-body interactions and correlations. In a multidomain system this signifies nonlocal coherence and emergence of collective phenomena, particularly in layered 2D quantum materials possessing novel electronic structures and high carrier mobilities. Here we report nonlocal ac electron coherence induced in dispersed MoS2 flake domains, using coherent spatial self-phase modulation (SSPM). The gap-dependent nonlinear dielectric susceptibility χ(3) measured is surprisingly large, where direct interband transition and two-photon SSPM are responsible for excitations above and below the bandgap, respectively. A wind-chime model is proposed to account for the emergence of the ac electron coherence. Furthermore, all-optical switching is achieved based on SSPM, especially with two-color intraband coherence, demonstrating that electron coherence generation is a ubiquitous property of layered quantum materials.

Modeling opinion dynamics: How the network enhances consensus

In this paper we analyze emergent collective phenomena in the evolution of opinions in a society structured into few interacting nodes of a network. The presented mathematical structure combines two dynamics: a first one on each single node and a second one among the nodes, i.e. in the network. The aim of the model is to analyze the effect of a network structure on a society with respect to opinion dynamics and we show some numerical solutions addressed in this direction, i.e. comparing the emergent behaviors of a consensus-dissent dynamic on a single node when the effect of the network is not considered, with respect to the emergent behaviors when the effect of a network structure linking few interacting nodes is considered. We adopt the framework of the Kinetic Theory for Active Particles (KTAP), deriving a general mathematical structure which allows to deal with nonlinear features of the interactions and representing the conceptual framework toward the derivation of specific models. A specific model is derived from the general mathematical structure by introducinga consensus-dissent dynamics of interactions and a qualitative analysis is given.

Instability and network effects in innovative markets

We consider a network of interacting agents and we model the process of choice on the adoption of a given innovative product by means of statistical-mechanics tools. The modelization allows us to focus on the effects of direct interactions among agents in establishing the success or failure of the product itself. Mimicking real systems, the whole population is divided into two sub-communities called, respectively, Innovators and Followers, where the former are assumed to display more influence power. We study in detail and via numerical simulations on a random graph two different scenarios: no-feedback interaction, where innovators are cohesive and not sensitively affected by the remaining population, and feedback interaction, where the influence of followers on innovators is non negligible. The outcomes are markedly different: in the former case, which corresponds to the creation of a niche in the market, Innovators are able to drive and polarize the whole market. In the latter case the behavior of the market cannot be definitely predicted and become unstable. In both cases we highlight the emergence of collective phenomena and we show how the final outcome, in terms of the number of buyers, is affected by the concentration of innovators and by the interaction strengths among agents.

Emergent Collective Phenomena in a Mixture of Hard Shapes through Active Rotation

We investigate collective phenomena with rotationally driven spinners of concave shape. Each spinner experiences a constant internal torque in either a clockwise or counterclockwise direction. Although the spinners are modeled as hard, otherwise noninteracting rigid bodies, their active motion induces an effective interaction that favors rotation in the same direction. With increasing density and activity, phase separation occurs via spinodal decomposition, as well as self-organization into rotating crystals. We observe the emergence of cooperative, superdiffusive motion along interfaces, which can transport inactive test particles. Our results demonstrate novel phase behavior of actively rotated particles that is not possible with linear propulsion or in nondriven, equilibrium systems of identical hard particles.

Vortex arrays as emergent collective phenomena for circle swimmers

Collective properties of many rodlike circle swimmers are explored by computer simulations in two spatial dimensions. In the model considered, the center of mass of a single swimmer moves on a circle with radius $R$. Therefore, the model provides an interpolation between an interacting self-propelled-rod model for linear swimmers ($R\ensuremath{\rightarrow}\ensuremath{\infty}$) and that of interacting passive rotors ($R=0$). We map out the state diagram for various swimmer densities and radii $R$. For increasing density, the dilute state is followed by vortices consisting of single particles (singlet-vortex state), where neighboring particles are perpendicularly oriented, and vortices of swimmer pairs (doublet-vortex state). The vortices exhibit strong structural ordering on an array. At higher densities, a slowed rotor fluid with a significant degree of mutual rotation hindrance occurs. The single-particle vortex structure becomes unstable above a threshold in the circling radius $R$, while pair vortices are stable only for intermediate radii $R$. A simple theory is proposed to predict the topology of the state diagram. Our results are verifiable for bacterial and artificial rodlike circle swimmers.

FuturICT: Participatory computing to understand and manage our complex world in a more sustainable and resilient way

We have built particle accelerators to understand the forces that make up our physical world. Yet, we do not understand the principles underlying our strongly connected, techno-socio-economic systems. We have enabled ubiquitous Internet connectivity and instant, global information access. Yet we do not understand how it impacts our behavior and the evolution of society. To fill the knowledge gaps and keep up with the fast pace at which our world is changing, a Knowledge Accelerator must urgently be created. The financial crisis, international wars, global terror, the spreading of diseases and cyber-crime as well as demographic, technological and environmental change demonstrate that humanity is facing serious challenges. These problems cannot be solved within the traditional paradigms. Moving our attention from a component-oriented view of the world to an interaction-oriented view will allow us to understand the complex systems we have created and the emergent collective phenomena characterising them. This paradigm shift will enable new solutions to long-standing problems, very much as the shift from a geocentric to a heliocentric worldview has facilitated modern physics and the ability to launch satellites. The FuturICT flagship project will develop new science and technology to manage our future in a complex, strongly connected world. For this, it will combine the power of information and communication technology (ICT) with knowledge from the social and complexity sciences. ICT will provide the data to boost the social sciences into a new era. Complexity science will shed new light on the emergent phenomena in socially interactive systems, and the social sciences will provide a better understanding of the opportunities and risks of strongly networked systems, in particular future ICT systems. Hence, the envisaged FuturICT flagship will create new methods and instruments to tackle the challenges of the 21st century. FuturICT could indeed become one of the most important scientific endeavours ever, by revealing the principles that make socially interactive systems work well, by inspiring the creation of new platforms to explore our possible futures, and by initiating an era of social and socio-inspired innovations.

Adaptive-network models of collective dynamics

Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system’s collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects’ adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous. Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change. Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks’ adaptive response to the agents’ dynamics is sufficiently fast.