Self-Propelled Particles with Soft-Core Interactions: Patterns, Stability, and Collapse

D’Orsogna, Maria R.; Chuang, Yao-Li; Bertozzi, Andrea L.; Chayes, L.

doi:10.1103/physrevlett.96.104302

Cited by 573 publications

(767 citation statements)

References 26 publications

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“…The tendency to match behaviour of nearby neighbours, which is also termed allelomimetic behaviour [53], is typically expressed in an explicit rule of alignment for an individual trying to match the speed and direction of its neighbours. Recent experimental evidence has suggested that it might not be an explicit behavioural rule [41,42], but rather that coordinated motion might be achieved using social repulsion and attraction only (as supported by theoretical models [54][55][56]). Finally, an attraction rule mimics a tendency to group with conspecifics in order to produce aggregation, a prerequisite of school existence.…”

Section: A Typology Of Individual Based Models For Fish Schoolingmentioning

confidence: 94%

From behavioural analyses to models of collective motion in fish schools

et al. 2012

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Fish schooling is a phenomenon of long-lasting interest in ethology and ecology, widely spread across taxa and ecological contexts, and has attracted much interest from statistical physics and theoretical biology as a case of self-organized behaviour. One topic of intense interest is the search of specific behavioural mechanisms at stake at the individual level and from which the school properties emerges. This is fundamental for understanding how selective pressure acting at the individual level promotes adaptive properties of schools and in trying to disambiguate functional properties from non-adaptive epiphenomena. Decades of studies on collective motion by means of individual-based modelling have allowed a qualitative understanding of the self-organization processes leading to collective properties at school level, and provided an insight into the behavioural mechanisms that result in coordinated motion. Here, we emphasize a set of paradigmatic modelling assumptions whose validity remains unclear, both from a behavioural point of view and in terms of quantitative agreement between model outcome and empirical data. We advocate for a specific and biologically oriented re-examination of these assumptions through experimental-based behavioural analysis and modelling.

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Section: A Typology Of Individual Based Models For Fish Schoolingmentioning

confidence: 94%

From behavioural analyses to models of collective motion in fish schools

et al. 2012

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“…Most notably we find that the potential can be classified as one in which the system is "confining" in the large N limit (where N is the number of particles) or non-confining. For second-order systems the language used is H-stability vs. catastrophic to describe the energy per particle in the limit of large N [6,10]. For this paper we are interested in the confining case such that the N → ∞ limit has stable solutions that are confined to some finite region, typically approximating a continuum density (possibly concentrated as a measure).…”

Section: Introductionmentioning

confidence: 99%

Swarming on Random Graphs

et al. 2013

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We consider a compromise model in one dimension in which pairs of agents interact through first-order dynamics that involve both attraction and repulsion. In the case of all-to-all coupling of agents, this system has a lowest energy state in which half of the agents agree upon one value and the other half agree upon a different value. The purpose of this paper is to study the behavior of this compromise model when the interaction between the N agents occurs according to an Erdős-Rényi random graph G (N, p). We study the effect of changing p on the stability of the compromised state, and derive both rigorous and asymptotic results suggesting that the stability is preserved for probabilities greater than p c = O( log N N ). In other words, relatively few interactions are needed to preserve stability of the state. The results rely on basic probability arguments and the theory of eigenvalues of random matrices.

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“…This has become a widely used tool in the area of complex systems [66,60,69,18,17,32,58]. For example, agent-based models have been used for modeling many types of cooperative behavior [31,56,14,11,24]. In a network context, this approach enables exploration of how changing dynamics of individual agents can affect the evolution of the network, providing control parameters which would be inaccessible in a graph-based model.…”

Section: Agent-based Modelsmentioning

confidence: 99%

Geographical influences of an emerging network of gang rivalries

Hegemann¹,

Smith²,

Barbaro³

et al. 2011

Physica A: Statistical Mechanics and its Applications

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We propose an agent-based model to simulate the creation of street gang rivalries. The movement dynamics of agents are coupled to an evolving network of gang rivalries, which is determined by previous interactions among agents in the system. Basic gang data, geographic information, and behavioral dynamics suggested by the criminology literature are integrated into the model. The major highways, rivers, and the locations of gangs' centers of activity influence the agents' motion. We use a policing division of the Los Angeles Police Department as a case study to test our model. We use common metrics from graph theory to analyze our model, comparing networks produced by our simulations and an instance of a Geographical Threshold Graph to the network existing in the criminology literature.

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Self-Propelled Particles with Soft-Core Interactions: Patterns, Stability, and Collapse

Cited by 573 publications

References 26 publications

From behavioural analyses to models of collective motion in fish schools

From behavioural analyses to models of collective motion in fish schools

Swarming on Random Graphs

Geographical influences of an emerging network of gang rivalries

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