The simulation of the movement of fish schools

Huth, Andreas; Wissel, Christian

doi:10.1016/s0022-5193(05)80681-2

Cited by 483 publications

(378 citation statements)

References 24 publications

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“…For example, models of fish schools and bird flocks are clearly based on multi-criteria model selection, having evolved over a long series of increasingly sophisticated field observations and simulation experiments that contrasted alternative submodels [37][38][39]. The highly predictive shorebird models of Stillman, Goss-Custard and co-workers [1,40] reproduce numerous patterns at multiple levels of organization, but these patterns have not been comprehensively listed and discussed.…”

Section: Examplesmentioning

confidence: 99%

Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology

Grimm

Railsback

2012

Phil. Trans. R. Soc. B

367

334

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Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.

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Section: Examplesmentioning

confidence: 99%

Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology

Grimm

Railsback

2012

Phil. Trans. R. Soc. B

367

334

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“…A range of theoretical models have been introduced to describe the collective motion of animal groups (see e.g., refs. 19-21); here we assume that the preferred direction results from local alignment, repulsion, and attraction (5,6,22,23). However, we assume the interaction zone over which an individual responds to neighbors varies as a function of current local conditions.…”

Section: Interaction Rulesmentioning

confidence: 99%

Context-dependent interaction leads to emergent search behavior in social aggregates

Torney

Neufeld

Couzin

2009

Proc. Natl. Acad. Sci. U.S.A.

103

134

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Locating the source of an advected chemical signal is a common challenge facing many living organisms. When the advecting medium is characterized by either high Reynolds number or high Peclet number, the task becomes highly nontrivial due to the generation of heterogeneous, dynamically changing filamental concentrations that do not decrease monotonically with distance to the source. Defining search strategies that are effective in these environments has important implications for the understanding of animal behavior and for the design of biologically inspired technology. Here we present a strategy that is able to solve this task without the higher intelligence required to assess spatial gradient direction, measure the diffusive properties of the flow field, or perform complex calculations. Instead, our method is based on the collective behavior of autonomous individuals following simple social interaction rules which are modified according to the local conditions they are experiencing. Through these context-dependent interactions, the group is able to locate the source of a chemical signal and in doing so displays an awareness of the environment not present at the individual level. This behavior illustrates an alternative pathway to the evolution of higher cognitive capacity via the emergent, group-level intelligence that can result from local interactions.collective intelligence | olfactory search | cooperation T hroughout the natural world, organisms are constantly faced with the challenge of locating the resources required for their survival. Often this means navigating their environment based on spatiotemporally varying information such as advected chemical cues, thermal gradients, or magnetic fields. It has been noted that collective behavior can greatly assist animal navigation. One explanation for this, known as the "many wrongs" principle (1), is that inherent noise in the environment is dampened due to multiple sampling by individuals within a group. A quantitative study of an effect of this type was made by Grünbaum (2) and the benefits of sociality clearly illustrated. However, this effect does not fully capture the potential emergent properties of social aggregations, which often display complex behaviors entirely absent at the individual level (3-6). In this context, complex systems, such as fish schools or animal herds, can be viewed as information-processing entities with a collective awareness of their environment (7). Understanding their capacity for performing search tasks will have important consequences for the development of distributed technologies, such as olfactory robot swarms, with applications in the detection of explosives, landmines, or locating people in search-and-rescue operations (8-10).The use of chemical signals by organisms in order to gain information about their environment is a ubiquitous behavior commonly seen in aquatic animals or flying insects and observed in a diverse range of species over a range of scales. For low Reynolds number, viscous environments chemotaxis in o...

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“…Our particular interest is in the models proposed by Aoki [8], Huth and Wissel [9], Couzin et al [10], and Reynolds [11]. To summarize, schooling and flocking was explained using three concentric zones: a zone of repulsion, zone of orientation, and zone of attraction.…”

Section: Drone Modelmentioning

confidence: 99%

“…With the growing desire of humans to create intelligent systems, these biosystems are being thoroughly inspected [3][4][5][6][7][8][9][10] and implemented [11][12][13][14] in various studies.…”

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confidence: 99%