期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 109, 期 13, 页码 4786-4791出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1118633109
关键词
animal groups; statistical inference
资金
- National Science Foundation [IIS-0613435, PHY-0957573]
- IIT-Seed Artswarm, ERC-StG [257126, AFOSR-Z809101]
- Burroughs-Wellcome Fund
- Division Of Physics
- Direct For Mathematical & Physical Scien [0957573] Funding Source: National Science Foundation
Flocking is a typical example of emergent collective behavior, where interactions between individuals produce collective patterns on the large scale. Here we show how a quantitative microscopic theory for directional ordering in a flock can be derived directly from field data. We construct the minimally structured (maximum entropy) model consistent with experimental correlations in large flocks of starlings. The maximum entropy model shows that local, pairwise interactions between birds are sufficient to correctly predict the propagation of order throughout entire flocks of starlings, with no free parameters. We also find that the number of interacting neighbors is independent of flock density, confirming that interactions are ruled by topological rather than metric distance. Finally, by comparing flocks of different sizes, the model correctly accounts for the observed scale invariance of long-range correlations among the fluctuations in flight direction.
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