An all-leader agent-based model for turning and flocking birds

Cristiani, Emiliano; Menci, Marta; Papi, Marco; Brafman, Léonard

doi:10.1007/s00285-021-01675-2

Cited by 11 publications

(8 citation statements)

References 31 publications

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“…[44]) identified nearly lossless propagation through the flock of turns made by individual starlings; a phenomenon that was modelled by [45] and by Cristiani et al . [46]. Nearly lossless propagation is also evident in the global turning behaviours of our simulated flocks.…”

Section: Comparisons With Published Observationsmentioning

confidence: 65%

“…It was shown how the modelling approach can be extended to account for the presence of high-density outer borders and subgroups [11,37] (figure 6). Previous models [26,41,[45][46][47] capture some but not all these facets of flocking. Any future models that account for the observations will have more structure than our minimally structured model and hence (explicitly or implicitly) will assume something more about the flock beyond what is required to match the data.…”

Section: Discussionmentioning

confidence: 99%

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Stochastic modelling of bird flocks: accounting for the cohesiveness of collective motion

Reynolds

McIvor

Thornton

et al. 2022

J. R. Soc. Interface.

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Collective behaviour can be difficult to discern because it is not limited to animal aggregations such as flocks of birds and schools of fish wherein individuals spontaneously move in the same way despite the absence of leadership. Insect swarms are, for example, a form of collective behaviour, albeit one lacking the global order seen in bird flocks and fish schools. Their collective behaviour is evident in their emergent macroscopic properties. These properties are predicted by close relatives of Okubo's 1986 [ Adv. Biophys. 22 , 1–94. ( doi:10.1016/0065-227X(86)90003-1 )] stochastic model. Here, we argue that Okubo's stochastic model also encapsulates the cohesiveness mechanism at play in bird flocks, namely the fact that birds within a flock behave on average as if they are trapped in an elastic potential well. That is, each bird effectively behaves as if it is bound to the flock by a force that on average increases linearly as the distance from the flock centre increases. We uncover this key, but until now overlooked, feature of flocking in empirical data. This gives us a means of identifying what makes a given system collective. We show how the model can be extended to account for intrinsic velocity correlations and differentiated social relationships.

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Section: Comparisons With Published Observationsmentioning

confidence: 65%

Section: Discussionmentioning

confidence: 99%

Stochastic modelling of bird flocks: accounting for the cohesiveness of collective motion

Reynolds

McIvor

Thornton

et al. 2022

J. R. Soc. Interface.

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“…• The flocking and escaping of birds: The beauty of the flocking and escaping behaviors of birds [40] involves multiple complex topics that remain challenging to explore [20,21,28,47]. In this section, we describe an attempt to simulate this phenomenon with several assumptions.…”

Section: Simulationsmentioning

confidence: 99%

“…The collective motion of animals, as a fascinating natural phenomenon, is a complex topic that involves biology, physics, mathematics, and related fields [1][2][3]. This topic has raised scientific interests in the theoretical and empirical studies of various species, such as schools of fish [4][5][6][7][8], swarms of insects [9][10][11][12][13], flocks of birds [14][15][16][17][18][19][20][21], and crowds of pedestrians [22][23][24][25][26][27]. Many theoretical models have focused on the microscopic description of each individual in a group during collective motion, such as the social force model [22][23][24]28], the Vicsek model [29,30], and the Cucker-Smale model [31,32].…”

mentioning

confidence: 99%

“…In this work, we present a general statistical mechanics framework for describing the collective motion of a group of animals. With consideration of the maximum entropy principle, the interaction, boundary, and desire effects, as well as the time-delay effect of information delivery among the group [21,[36][37][38], this framework provides a "bottom to top" analytical bridge between empirical data and theoretical models, as well as a simulation tool. We show that some common models, such as the Vicsek model, the social force model, and some of their variants, are special cases of this general framework.…”

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confidence: 99%

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A General Statistical Mechanics Framework for the Collective Motion of Animals

Cai¹,

Zhang²,

Chen³

et al. 2021

Preprint

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We propose a general statistical mechanics framework for the collective motion of animals. The framework considers the principle of maximum entropy, the interaction, boundary, and desire effects, as well as the time-delay effect. These factors provide the ability to describe and solve dynamic and non-equilibrium problems under this framework. We show that the Vicsek model, the social force model, and some of their variants can be considered special cases of this framework. Furthermore, this framework can be extended to the maximum caliber setting. We demonstrate the potential of this framework for model comparisons and parameter estimations by applying the model to observed data from a field study of the emergent behavior of termites. Finally, we demonstrate the flexibility of the framework by simulating some collective moving phenomena for birds and ants.

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