Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6235
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Title: Generating self-organizing collective behavior using separation dynamics from experimental data
Authors: Dieck Kattas, G
Xu, X
Small, M
Issue Date: Sep-2012
Source: Chaos, Sept. 2012, v. 22, 033113, p. 1-10
Abstract: We propose a simple dynamical model of collective behavior that attempts to follow a rule of local neighbor interactions, which was abstracted from experimental flight data of pigeon flocks. This rule is consistent with the previous hypothesis of the basic mechanisms affecting collective motion: short range repulsion to avoid collisions, longer range attraction to keep the group together, and velocity alignment to maintain the same navigational direction. The local interactions of our model consist of using naive neighbor estimates: essentially assuming that nearest neighbors will move with the same velocity as in the previous time interval. The dynamics of our model try to follow the experimental rule by using a simple adjustment mechanism with respect to the naive neighbor estimates. From our simulations, we show that by changing the initial conditions or the number of individuals involved in the interactions, the model is capable of exhibiting a wide range of realistic behaviors. Our study emphasizes the importance of using experimental data for making better models of complex systems, and that this should contribute to a better understanding of nonlinear collective dynamics.
Keywords: Mathematical analysis
Multi-agent systems
Pattern formation
Self-organised criticality
Publisher: American Institute of Physics
Journal: Chaos 
ISSN: 1054-1500
EISSN: 1089-7682
DOI: 10.1063/1.4737203
Rights: © 2012 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in G. D. Kattas et al., Chaos: an interdisciplinary journal of nonlinear science 22, 033113 (2012) and may be found at http://link.aip.org/link/?cha/22/033113
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