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Ant groups optimally amplify the effect of transiently informed individuals

Author

Listed:
  • Aviram Gelblum

    (Weizmann Institute of Science)

  • Itai Pinkoviezky

    (Weizmann Institute of Science)

  • Ehud Fonio

    (Weizmann Institute of Science)

  • Abhijit Ghosh

    (Weizmann Institute of Science)

  • Nir Gov

    (Weizmann Institute of Science)

  • Ofer Feinerman

    (Weizmann Institute of Science)

Abstract

To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group’s responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

Suggested Citation

  • Aviram Gelblum & Itai Pinkoviezky & Ehud Fonio & Abhijit Ghosh & Nir Gov & Ofer Feinerman, 2015. "Ant groups optimally amplify the effect of transiently informed individuals," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms8729
    DOI: 10.1038/ncomms8729
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    Cited by:

    1. Tsvetomira Radeva & Anna Dornhaus & Nancy Lynch & Radhika Nagpal & Hsin-Hao Su, 2017. "Costs of task allocation with local feedback: Effects of colony size and extra workers in social insects and other multi-agent systems," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-29, December.
    2. Zhongguo Ren & Chen Xin & Kaiwen Liang & Heming Wang & Dawei Wang & Liqun Xu & Yanlei Hu & Jiawen Li & Jiaru Chu & Dong Wu, 2024. "Femtosecond laser writing of ant-inspired reconfigurable microbot collectives," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Jonathan E Ron & Itai Pinkoviezky & Ehud Fonio & Ofer Feinerman & Nir S Gov, 2018. "Bi-stability in cooperative transport by ants in the presence of obstacles," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-21, May.
    4. Guibin Sun & Rui Zhou & Zhao Ma & Yongqi Li & Roderich Groß & Zhang Chen & Shiyu Zhao, 2023. "Mean-shift exploration in shape assembly of robot swarms," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Daniele Vilone & John Realpe-Gómez & Giulia Andrighetto, 2021. "Evolutionary advantages of turning points in human cooperative behaviour," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-15, February.
    6. Pascal P Klamser & Pawel Romanczuk, 2021. "Collective predator evasion: Putting the criticality hypothesis to the test," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-21, March.

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