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Quantifying and Tracing Information Cascades in Swarms

Overview of attention for article published in PLOS ONE, July 2012
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Title
Quantifying and Tracing Information Cascades in Swarms
Published in
PLOS ONE, July 2012
DOI 10.1371/journal.pone.0040084
Pubmed ID
Authors

X. Rosalind Wang, Jennifer M. Miller, Joseph T. Lizier, Mikhail Prokopenko, Louis F. Rossi

Abstract

We propose a novel, information-theoretic, characterisation of cascades within the spatiotemporal dynamics of swarms, explicitly measuring the extent of collective communications. This is complemented by dynamic tracing of collective memory, as another element of distributed computation, which represents capacity for swarm coherence. The approach deals with both global and local information dynamics, ultimately discovering diverse ways in which an individual's spatial position is related to its information processing role. It also allows us to contrast cascades that propagate conflicting information with waves of coordinated motion. Most importantly, our simulation experiments provide the first direct information-theoretic evidence (verified in a simulation setting) for the long-held conjecture that the information cascades occur in waves rippling through the swarm. Our experiments also exemplify how features of swarm dynamics, such as cascades' wavefronts, can be filtered and predicted. We observed that maximal information transfer tends to follow the stage with maximal collective memory, and principles like this may be generalised in wider biological and social contexts.

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Geographical breakdown

Country Count As %
United Kingdom 2 2%
Germany 1 1%
Indonesia 1 1%
Switzerland 1 1%
China 1 1%
Japan 1 1%
United States 1 1%
Luxembourg 1 1%
Serbia 1 1%
Other 0 0%
Unknown 75 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 26%
Researcher 11 13%
Professor > Associate Professor 10 12%
Professor 10 12%
Student > Doctoral Student 7 8%
Other 19 22%
Unknown 6 7%
Readers by discipline Count As %
Computer Science 21 25%
Agricultural and Biological Sciences 17 20%
Physics and Astronomy 15 18%
Engineering 12 14%
Mathematics 4 5%
Other 8 9%
Unknown 8 9%