1,761 research outputs found
Coping with Extreme Events: Institutional Flocking
Recent measurements in the North Atlantic confirm that the thermohaline circulation driving the Gulf Stream has come to a stand. Oceanographic monitoring over the last 50 years already showed that the circulation was weakening. Under the influence of the large inflow of melting water in Northern Atlantic waters during last summer, it has now virtually stopped. Consequently, the KNMI and the RIVM estimate the average . In this essay we will explore how such a new risk profile affects the distribution of risks among societal groups, and the way in which governing institutions need to adapt in order to be prepared for situations of rapid but unknown change. The next section will first introduce an analytical perspective, building upon the Risk Society thesis and a proposed model of āinstitutional flockingā.temperature to decrease by 3Ā°C in the next 15 years
Predictive protocol of flocks with small-world connection pattern
By introducing a predictive mechanism with small-world connections, we
propose a new motion protocol for self-driven flocks. The small-world
connections are implemented by randomly adding long-range interactions from the
leader to a few distant agents, namely pseudo-leaders. The leader can directly
affect the pseudo-leaders, thereby influencing all the other agents through
them efficiently. Moreover, these pseudo-leaders are able to predict the
leader's motion several steps ahead and use this information in decision making
towards coherent flocking with more stable formation. It is shown that drastic
improvement can be achieved in terms of both the consensus performance and the
communication cost. From the industrial engineering point of view, the current
protocol allows for a significant improvement in the cohesion and rigidity of
the formation at a fairly low cost of adding a few long-range links embedded
with predictive capabilities. Significantly, this work uncovers an important
feature of flocks that predictive capability and long-range links can
compensate for the insufficiency of each other. These conclusions are valid for
both the attractive/repulsive swarm model and the Vicsek model.Comment: 10 pages, 12 figure
Refining self-propelled particle models for collective behaviour
Swarming, schooling, flocking and herding are all names given to the wide variety of collective behaviours exhibited by groups of animals, bacteria and even individual cells. More generally, the term swarming describes the behaviour of an aggregate of agents (not necessarily biological) of similar size and shape which exhibit some emergent property such as directed migration or group cohesion. In this paper we review various individual-based models of collective behaviour and discuss their merits and drawbacks. We further analyse some one-dimensional models in the context of locust swarming. In specific models, in both one and two dimensions, we demonstrate how varying the parameters relating to how much attention individuals pay to their neighbours can dramatically change the behaviour of the group. We also introduce leader individuals to these models with the ability to guide the swarm to a greater or lesser degree as we vary the parameters of the model. We consider evolutionary scenarios for models with leaders in which individuals are allowed to evolve the degree of influence neighbouring individuals have on their subsequent motion
Resilience and Controllability of Dynamic Collective Behaviors
The network paradigm is used to gain insight into the structural root causes
of the resilience of consensus in dynamic collective behaviors, and to analyze
the controllability of the swarm dynamics. Here we devise the dynamic signaling
network which is the information transfer channel underpinning the swarm
dynamics of the directed interagent connectivity based on a topological
neighborhood of interactions. The study of the connectedness of the swarm
signaling network reveals the profound relationship between group size and
number of interacting neighbors, which is found to be in good agreement with
field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl.
Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic
collective behaviors enabling us to uncover that the swarm signaling network is
a homogeneous clustered small-world network, thus facilitating emergent
outcomes if connectedness is maintained. Resilience of the emergent consensus
is tested by introducing exogenous environmental noise, which ultimately
stresses how deeply intertwined are the swarm dynamics in the physical and
network spaces. The availability of the signaling network allows us to
analytically establish for the first time the number of driver agents necessary
to fully control the swarm dynamics
Consensus reaching in swarms ruled by a hybrid metric-topological distance
Recent empirical observations of three-dimensional bird flocks and human
crowds have challenged the long-prevailing assumption that a metric interaction
distance rules swarming behaviors. In some cases, individual agents are found
to be engaged in local information exchanges with a fixed number of neighbors,
i.e. a topological interaction. However, complex system dynamics based on pure
metric or pure topological distances both face physical inconsistencies in low
and high density situations. Here, we propose a hybrid metric-topological
interaction distance overcoming these issues and enabling a real-life
implementation in artificial robotic swarms. We use network- and
graph-theoretic approaches combined with a dynamical model of locally
interacting self-propelled particles to study the consensus reaching pro- cess
for a swarm ruled by this hybrid interaction distance. Specifically, we
establish exactly the probability of reaching consensus in the absence of
noise. In addition, simulations of swarms of self-propelled particles are
carried out to assess the influence of the hybrid distance and noise
Ciliary flocking and emergent instabilities enable collective agility in a non-neuromuscular animal
Effective organismal behavior responds appropriately to changes in the
surrounding environment. Attaining this delicate balance of sensitivity and
stability is a hallmark of the animal kingdom. By studying the locomotory
behavior of a simple animal (\textit{Trichoplax adhaerens}) without muscles or
neurons, here, we demonstrate how monociliated epithelial cells work
collectively to give rise to an agile non-neuromuscular organism. Via direct
visualization of large ciliary arrays, we report the discovery of sub-second
ciliary reorientations under a rotational torque that is mediated by collective
tissue mechanics and the adhesion of cilia to the underlying substrate. In a
toy model, we show a mapping of this system onto an "active-elastic resonator".
This framework explains how perturbations propagate information in this array
as linear speed traveling waves in response to mechanical stimulus. Next, we
explore the implications of parametric driving in this active-elastic resonator
and show that such driving can excite mechanical 'spikes'. These spikes in
collective mode amplitudes are consistent with a system driven by parametric
amplification and a saturating nonlinearity. We conduct extensive numerical
experiments to corroborate these findings within a polarized active-elastic
sheet. These results indicate that periodic and stochastic forcing are valuable
for increasing the sensitivity of collective ciliary flocking. We support these
theoretical predictions via direct experimental observation of linear speed
traveling waves which arise from the hybridization of spin and overdamped
density waves. We map how these ciliary flocking dynamics result in agile
motility via coupling between an amplified resonator and a tuning
(Goldstone-like) mode of the system. This sets the stage for how activity and
elasticity can self-organize into behavior which benefits the organism as a
whole
Evolving collective behavior in an artificial ecology
Collective behavior refers to coordinated group motion, common to many animals. The dynamics of a group can be seen as a distributed model, each āanimalā applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures āliveā in an artificial world with hazards and food. Each creature has a simple artificial neural network brain that controls movement in different situations. A chromosome encodes the network structure and weights, which may be combined using artificial evolution with another chromosome, if a creature should choose to mate. Prey and predators coevolve without an explicit fitness function for schooling to produce sophisticated, nondeterministic, behavior. The work highlights the role of speciesā physiology in understanding behavior and the role of the environment in encouraging the development of sensory systems
Modeling flocks with perceptual agents from a dynamicist perspective
Computational simulations of flocks and crowds have typically been processed by a set of logic or syntactic rules. In recent decades, a new generation of systems has emerged from dynamicist approaches in which the agents and the environment are treated as a pair of dynamical systems coupled informationally and mechanically. Their spontaneous interactions allow them to achieve the desired behavior. The main proposition assumes that the agent does not need a full model or to make inferences before taking actions; rather, the information necessary for any action can be derived from the environment with simple computations and very little internal state. In this paper, we present a simulation framework in which the agents are endowed with a sensing device, an oscillator network as controller and actuators to interact with the environment. The perception device is designed as an optic array emulating the principles of the animal retina, which assimilates stimuli resembling optic flow to be captured from the environment. The controller modulates informational variables to action variables in a sensory-motor flow. Our approach is based on the Kuramoto model that describes mathematically a network of coupled phase oscillators and the use of evolutionary algorithms, which is proved to be capable of synthesizing minimal synchronization strategies based on the dynamical coupling between agents and environment. We carry out a comparative analysis with classical implementations taking into account several criteria. It is concluded that we should consider replacing the metaphor of symbolic information processing by that of sensory-motor coordination in problems of multi-agent organizations
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