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Starling Flock Networks Manage Uncertainty in Consensus at Low Cost

Overview of attention for article published in PLoS Computational Biology, January 2013
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Title
Starling Flock Networks Manage Uncertainty in Consensus at Low Cost
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002894
Pubmed ID
Authors

George F. Young, Luca Scardovi, Andrea Cavagna, Irene Giardina, Naomi E. Leonard

Abstract

Flocks of starlings exhibit a remarkable ability to maintain cohesion as a group in highly uncertain environments and with limited, noisy information. Recent work demonstrated that individual starlings within large flocks respond to a fixed number of nearest neighbors, but until now it was not understood why this number is seven. We analyze robustness to uncertainty of consensus in empirical data from multiple starling flocks and show that the flock interaction networks with six or seven neighbors optimize the trade-off between group cohesion and individual effort. We can distinguish these numbers of neighbors from fewer or greater numbers using our systems-theoretic approach to measuring robustness of interaction networks as a function of the network structure, i.e., who is sensing whom. The metric quantifies the disagreement within the network due to disturbances and noise during consensus behavior and can be evaluated over a parameterized family of hypothesized sensing strategies (here the parameter is number of neighbors). We use this approach to further show that for the range of flocks studied the optimal number of neighbors does not depend on the number of birds within a flock; rather, it depends on the shape, notably the thickness, of the flock. The results suggest that robustness to uncertainty may have been a factor in the evolution of flocking for starlings. More generally, our results elucidate the role of the interaction network on uncertainty management in collective behavior, and motivate the application of our approach to other biological networks.

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Geographical breakdown

Country Count As %
United States 3 2%
Hungary 1 <1%
Germany 1 <1%
Italy 1 <1%
Netherlands 1 <1%
China 1 <1%
United Kingdom 1 <1%
Unknown 176 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 57 31%
Researcher 27 15%
Student > Master 23 12%
Student > Bachelor 13 7%
Professor 10 5%
Other 27 15%
Unknown 28 15%
Readers by discipline Count As %
Engineering 43 23%
Agricultural and Biological Sciences 32 17%
Physics and Astronomy 24 13%
Computer Science 14 8%
Biochemistry, Genetics and Molecular Biology 6 3%
Other 33 18%
Unknown 33 18%