scholarly journals Impact of Variable Speed on Collective Movement of Animal Groups

2021 ◽  
Vol 9 ◽  
Author(s):  
Pascal P. Klamser ◽  
Luis Gómez-Nava ◽  
Tim Landgraf ◽  
Jolle W. Jolles ◽  
David Bierbach ◽  
...  
Keyword(s):  
Experimental Data ◽  
Group Size ◽  
Collective Behavior ◽  
Functional Dependence ◽  
Variable Speed ◽  
Animal Groups ◽  
Agent Based ◽  
Collective Dynamics ◽  
Collective Movement ◽  
Self Organized

The collective dynamics and structure of animal groups has attracted the attention of scientists across a broad range of fields. A variety of agent-based models have been developed to help understand the emergence of coordinated collective behavior from simple interaction rules. A common, simplifying assumption of such collective movement models, is that individual agents move with a constant speed. In this work we critically re-asses this assumption. First, we discuss experimental data showcasing the omnipresent speed variability observed in different species of live fish and artificial agents (RoboFish). Based on theoretical considerations accounting for inertia and rotational friction, we derive a functional dependence of the turning response of individuals on their instantaneous speed, which is confirmed by experimental data. We then investigate the interplay of variable speed and speed-dependent turning on self-organized collective behavior by implementing an agent-based model which accounts for both these effects. We show that, besides the average speed of individuals, the variability in individual speed can have a dramatic impact on the emergent collective dynamics: a group which differs to another only in a lower speed variability of its individuals (groups being identical in all other behavioral parameters), can be in the polarized state while the other group is disordered. We find that the local coupling between group polarization and individual speed is strongest at the order-disorder transition, and that, in contrast to fixed speed models, the group’s spatial extent does not have a maximum at the transition. Furthermore, we demonstrate a decrease in polarization with group size for groups of individuals with variable speed, and a sudden decrease in mean individual speed at a critical group size (N = 4 for Voronoi interactions) linked to a topological transition from an all-to-all to a distributed spatial interaction network. Overall, our work highlights the importance to account for fundamental kinematic constraints in general, and variable speed in particular, when modeling self-organized collective dynamics.

scholarly journals Cognitive Profiling of Nodes in 6G through Multiplex Social Network and Evolutionary Collective Dynamics

2021 ◽  
Vol 13 (5) ◽  
pp. 135
Author(s):  
Marialisa Scatá ◽  
Barbara Attanasio ◽  
Aurelio La Corte
Keyword(s):  
Social Network ◽  
Complex Systems ◽  
Communication Networks ◽  
Digital Health ◽  
Evolutionary Game ◽  
Mathematical Representation ◽  
Collective Dynamics ◽  
Self Organized ◽  
Learning And Cognition ◽  
Application Fields

Complex systems are fully described by the connectedness of their elements studying how these develop a collective behavior, interacting with each other following their inner features, and the structure and dynamics of the entire system. The forthcoming 6G will attempt to rewrite the communication networks’ perspective, focusing on a radical revolution in the way entities and technologies are conceived, integrated and used. This will lead to innovative approaches with the aim of providing new directions to deal with future network challenges posed by the upcoming 6G, thus the complex systems could become an enabling set of tools and methods to design a self-organized, resilient and cognitive network, suitable for many application fields, such as digital health or smart city living scenarios. Here, we propose a complex profiling approach of heterogeneous nodes belonging to the network with the goal of including the multiplex social network as a mathematical representation that enables us to consider multiple types of interactions, the collective dynamics of diffusion and competition, through social contagion and evolutionary game theory, and the mesoscale organization in communities to drive learning and cognition. Through a framework, we detail the step by step modeling approach and show and discuss our findings, applying it to a real dataset, by demonstrating how the proposed model allows us to detect deeply complex knowable roles of nodes.

scholarly journals Active sensing in groups: (what) do bats hear in the sonar cocktail party nightmare?

2019 ◽  
Author(s):  
Thejasvi Beleyur ◽  
Holger R. Goerlitz
Keyword(s):  
Group Size ◽  
Collective Behavior ◽  
Sensory Input ◽  
Nearest Neighbors ◽  
Active Sensing ◽  
Cocktail Party ◽  
Loud Calls ◽  
Sensory Strategies ◽  
Collective Group ◽  
Echo Detection

ABSTRACTActive sensing animals perceive their surroundings by emitting probes of energy and analyzing how the environment modulates these probes. However, the probes of conspecifics can jam active sensing, which should cause problems for groups of active sensing animals. This problem was termed the cocktail party nightmare for echolocating bats: as bats listen for the faint returning echoes of their loud calls, these echoes will be masked by the loud calls of other close-by bats. Despite this problem, many bats echolocate in groups and roost socially. Here, we present a biologically parametrized framework to quantify echo detection in groups. Incorporating known properties of echolocation, psychoacoustics, spatial acoustics and group flight, we quantify how well bats flying in groups can detect each other despite jamming. A focal bat in the center of a group can detect neighbors for group sizes of up to 100 bats. With increasing group size, fewer and only the closest and frontal neighbors are detected. Neighbor detection is improved for longer call intervals, shorter call durations, denser groups and more variable flight and sonar beam directions. Our results provide the first quantification of the sensory input of echolocating bats in collective group flight, such as mating swarms or emergences. Our results further generate predictions on the sensory strategies bats may use to reduce jamming in the cocktail party nightmare. Lastly, we suggest that the spatially limited sensory field of echolocators leads to limited interactions within a group, so that collective behavior is achieved by following only nearest neighbors.SIGNIFICANCE STATEMENTClose-by active sensing animals may interfere with each other. We investigated if and what many echolocators fly in a group hear – can they detect each other after all? We modelled acoustic and physical properties in group echolocation to quantify neighbor detection probability as group size increases. Echolocating bats can detect at least one of their closest neighbors per call up to group sizes of even 100 bats. Call parameters such as call rate and call duration play a strong role in how much echolocators in a group interfere with each other. Even when many bats fly together, they are indeed able to detect at least their nearest frontal neighbors – and this prevents them from colliding into one another.

scholarly journals Coarse Collective Dynamics of Animal Groups

2010 ◽  
pp. 299-309 ◽  
Author(s):  
Thomas A. Frewen ◽  
Iain D. Couzin ◽  
Allison Kolpas ◽  
Jeff Moehlis ◽  
Ronald Coifman ◽  
...  
Keyword(s):  
Animal Groups ◽  
Collective Dynamics

Epilogue

2020 ◽  
pp. 133-134
Author(s):  
Daniel Oro
Keyword(s):  
Social Groups ◽  
Group Versus ◽  
Self Organization ◽  
Emergent Properties ◽  
Animal Groups ◽  
Self Organized ◽  
Open Questions ◽  
The Individual ◽  
Share Information

Complex social animal groups behave as self-organized, single structures: they feed together, they defend against predators together, they escape from perturbations and disperse and migrate together and they share information. It is modestly evident that many individuals sharing information about their environment may be more successful in coping with perturbations than solitary individuals gathering information on their own. The group exists for and by means of all the individuals, and these exist for and by means of the group. Social groups have emergent properties that cannot be easily explained by either selection or self-organization. Yet, sociality has been shaped by the two forces. How sociality has evolved by selection is puzzling also because it confronts the benefits of the group versus the benefits of the individual, which is a historically debated theme. There are many other open questions about sociality that I have explored in this book. But in the end, the process that has fascinated me the most is social copying. Despite the sophisticated mechanisms evolved in increasing information in social groups—which has culminated in humans with language and technological interconnections—it is impressive how a simple behaviour such as social copying has maintained its strength when individuals make any kind of decisions, from insignificant to transcendent....

scholarly journals An agent-based approach for modelling collective dynamics in animal groups distinguishing individual speed and orientation

2020 ◽  
Vol 375 (1807) ◽  
pp. 20190383 ◽  
Author(s):  
Sara Bernardi ◽  
Marco Scianna
Keyword(s):  
Group Dynamics ◽  
A Priori ◽  
Material Point ◽  
Interparticle Spacing ◽  
Animal Groups ◽  
Collective Dynamics ◽  
Multi Scale ◽  
Hunting Strategies ◽  
Wide Range ◽  
Critical Model

Collective dynamics in animal groups is a challenging theme for the modelling community, being treated with a wide range of approaches. This topic is here tackled by a discrete model. Entering in more details, each agent, represented by a material point, is assumed to move following a first-order Newtonian law, which distinguishes speed and orientation. In particular, the latter results from the balance of a given set of behavioural stimuli, each of them defined by a direction and a weight, that quantifies its relative importance. A constraint on the sum of the weights then avoids implausible simultaneous maximization/minimization of all movement traits. Our framework is based on a minimal set of rules and parameters and is able to capture and classify a number of collective group dynamics emerging from different individual preferred behaviour, which possibly includes attractive, repulsive and alignment stimuli. In the case of a system of animals subjected only to the first two behavioural inputs, we also show how analytical arguments allow us to a priori relate the equilibrium interparticle spacing to critical model coefficients. Our approach is then extended to account for the presence of predators with different hunting strategies, which impact on the behaviour of a prey population. Hints for model refinement and applications are finally given in the conclusive part of the article. This article is part of the theme issue ‘Multi-scale analysis and modelling of collective migration in biological systems’.

scholarly journals Mechanical spectroscopy of insect swarms

2019 ◽  
Vol 5 (7) ◽  
pp. eaaw9305 ◽  
Author(s):  
Kasper van der Vaart ◽  
Michael Sinhuber ◽  
Andrew M. Reynolds ◽  
Nicholas T. Ouellette
Keyword(s):  
Information Flow ◽  
Collective Behavior ◽  
Mechanical Spectroscopy ◽  
Bulk Materials ◽  
Animal Groups ◽  
Environmental Perturbations ◽  
Storage And Loss Moduli ◽  
Social Animals ◽  
In The Wild ◽  
Flow Through

Social animals routinely form groups, which are thought to display emergent, collective behavior. This hypothesis suggests that animal groups should have properties at the group scale that are not directly linked to the individuals, much as bulk materials have properties distinct from those of their constituent atoms. Materials are often probed by measuring their response to controlled perturbations, but these experiments are difficult to conduct on animal groups, particularly in the wild. Here, we show that laboratory midge swarms have emergent continuum mechanical properties, displaying a collective viscoelastic response to applied oscillatory visual stimuli that allows us to extract storage and loss moduli for the swarm. We find that the swarms strongly damp perturbations, both viscously and inertially. Thus, unlike bird flocks, which appear to use collective behavior to promote lossless information flow through the group, our results suggest that midge swarms use it to stabilize themselves against environmental perturbations.

scholarly journals Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 980
Author(s):  
Gustavo Meirelles ◽  
Bruno Brentan ◽  
Joaquín Izquierdo ◽  
Edevar Luvizotto
Keyword(s):  
Systems Engineering ◽  
Collective Behavior ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Sensitivity Analyses ◽  
Grand Tour ◽  
Agent Based ◽  
Local Optimal Solution ◽  
Classical Optimization

Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA’s value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.

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