Making noise: Emergent stochasticity in collective motion
Making noise: Emergent stochasticity in collective motion
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DOI：
10.1016/j.jtbi.2010.08.034
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发表时间：
2010-12-07
影响因子：
2
通讯作者：
Wood, A. Jamie
中科院分区：
文献类型：
--
作者：
Bode, Nikolai W. F.;Franks, Daniel W.;Wood, A. Jamie
基于个体的自推进粒子（SPPs）模型是解释动物群体集体运动特征的一种流行和有前途的方法。已经提出了许多捕捉群体运动某些特征的模型，但一个共同的框架尚未出现。所有这些模型的关键是在spp的个体行为中包含“噪声”或随机误差。在这里，我们提出了一个一维的全随机SPP模型，该模型展示了一种将噪声引入SPP模型的新方法，同时保留了先前模型的紧急行为，如相干群和自发方向切换。这种纯粹的个体对个体的局部模型与文献中的先前模型相关，并且可以很容易地扩展到更高的维度。它的粗粒度行为定性地再现了最近报告的蝗虫运动数据。我们认为，我们的方法为目前关于模型构建的推理提供了另一种选择，并有可能为自然界中动物群体的涌现特性提供机制解释。(C) 2010 Elsevier Ltd.版权所有。
Individual-based models of self-propelled particles (SPPs) are a popular and promising approach to explain features of the collective motion of animal aggregations. Many models that capture some features of group motion have been suggested but a common framework has yet to emerge. Key to all of these models is the inclusion of "noise" or stochastic errors in the individual behaviour of the SPPs. Here, we present a fully stochastic SPP model in one dimension that demonstrates a new way of introducing noise into SPP models whilst preserving emergent behaviours of previous models such as coherent groups and spontaneous direction switching. This purely individual-to-individual, local model is related to previous models in the literature and can easily be extended to higher dimensions. Its coarse-grained behaviour qualitatively reproduces recently reported locust movement data. We suggest that our approach offers an alternative to current reasoning about model construction and has the potential to offer mechanistic explanations for emergent properties of animal groups in nature. (C) 2010 Elsevier Ltd. All rights reserved.