Parallel Modeling of Fish Interaction

Youseff, Lamia; Barbaro, Alethea; Trethewey, Peterson F; Birnir, Björn; Gilbert, John R.

doi:10.1109/cse.2008.8

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…There is a large number of models for self-organized dynamics [1,2,4,5,7,13,12,17,19,20,22,21,26,25,27]. In this paper we studied a general class of models for self-organized dynamics which take the form (3.1),…”

Section: Discussionmentioning

confidence: 99%

“…One of the many questions addressed concerns the emergent behavior in these systems and in particular, the emergence of "flocking behavior". Many models have been introduced to appraise the emergent behavior of self-organized systems [2,3,7,13,17,22,25,27]. The starting point for our discussion is the pioneering work of Cucker-Smale, [8,9], which led to many subsequent studies [3,6,14,15,16,23].…”

Section: Introductionmentioning

confidence: 99%

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A New Model for Self-organized Dynamics and Its Flocking Behavior

2011

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To Claude Bardos on his 70 th birthday, with friendship and admiration Abstract. We introduce a model for self-organized dynamics which, we argue, addresses several drawbacks of the celebrated Cucker-Smale (C-S) model. The proposed model does not only take into account the distance between agents, but instead, the influence between agents is scaled in term of their relative distance. Consequently, our model does not involve any explicit dependence on the number of agents; only their geometry in phase space is taken into account. The use of relative distances destroys the symmetry property of the original C-S model, which was the key for the various recent studies of C-S flocking behavior. To this end, we introduce here a new framework to analyze the phenomenon of flocking for a rather general class of dynamical systems, which covers systems with non-symmetric influence matrices. In particular, we analyze the flocking behavior of the proposed model as well as other strongly asymmetric models with "leaders".The methodology presented in this paper, based on the notion of active sets, carries over from the particle to kinetic and hydrodynamic descriptions. In particular, we discuss the hydrodynamic formulation of our proposed model, and prove its unconditional flocking for slowly decaying influence functions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Model for Self-organized Dynamics and Its Flocking Behavior

2011

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“…A natural classification is offered by the underlying topic. Many models of self-organized dynamics are driven by examples from biology: these include aggregation of bacteria and amoeba [6,51,59,74,98], dynamics of insects [14,34], school of fish [1,67,111] flocking of birds [4,25,26,27,37,38,61,93,103,107], and related models in ecology [58]. Self-organized dynamics found its in many other areas, from pedestrian and traffic dynamics [64,92], social networks and economics [48,66,69,77,83], complex networks [5,44,90] and opinion dynamics [7,24,42,49,50,63,75,105,109,110], all the way to applications in marketing [2,3], production networks [94], robotics [33,…”

Section: Further Reading On Self-organized Dynamicsmentioning

confidence: 99%

Heterophilious Dynamics Enhances Consensus

2014

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We review a general class of models for self-organized dynamics based on alignment. The dynamics of such systems is governed solely by interactions among individuals or "agents", with the tendency to adjust to their 'environmental averages'. This, in turn, leads to the formation of clusters, e.g., colonies of ants, flocks of birds, parties of people, rendezvous in mobile networks, etc. A natural question which arises in this context is to understand when and how clusters emerge through the self-alignment of agents, and what type of "rules of engagement" influence the formation of such clusters. Of particular interest to us are cases in which the self-organized behavior tends to concentrate into one cluster, reflecting a consensus of opinions, flocking of birds, fish or cells, rendezvous of mobile agents, and in general, concentration of other traits intrinsic to the dynamics. Many standard models for self-organized dynamics in social, biological and physical science assume that the intensity of alignment increases as agents get closer, reflecting a common tendency to align with those who think or act alike. Moreover, "Similarity breeds connection," reflects our intuition that increasing the intensity of alignment as the difference of positions decreases, is more likely to lead to a consensus. We argue here that the converse is true: when the dynamics is driven by local interactions, it is more likely to approach a consensus when the interactions among agents increase as a function of their difference in position. Heterophily -the tendency to bond more with those who are different rather than with those who are similar, plays a decisive rôle in the process of clustering. We point out that the number of clusters in heterophilious dynamics decreases as the heterophily dependence among agents increases. In particular, sufficiently strong heterophilious interactions enhance consensus.Date: October 15, 2018. 1991 Mathematics Subject Classification. 92D25,74A25,76N10.

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