Evidence map›Paper›PMID 38962249›Full record

ArticlePNAS nexus2024

Reverse social contagion as a mechanism for regulating mass behaviors in highly integrated social systems.

Maurizio Porfiri, Pietro De Lellis, Eighdi Aung, Santiago Meneses, Nicole Abaid, Jane S Waters, Simon Garnier

Abstract read
In one paragraph

Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Maurizio PorfiriCenter for Urban Science and Progress, New York University, Brooklyn, NY 11201, USA.ORCID https://orcid.org/0000-0002-1480-3539
Pietro De LellisDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Naples 80125, Italy.ORCID https://orcid.org/0000-0002-2656-6454
Eighdi AungDepartment of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24061, USA.ORCID https://orcid.org/0000-0003-2584-7817
Santiago MenesesDepartment of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA.ORCID https://orcid.org/0000-0002-9989-4002
Nicole AbaidDepartment of Mathematics, Virginia Tech, Blacksburg, VA 24061, USA.
Jane S WatersDepartment of Biology, Providence College, Providence, RI 02918, USA.
Simon GarnierDepartment of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA.ORCID https://orcid.org/0000-0002-3886-3974

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass behavior is the rapid adoption of similar conduct by all group members, with potentially catastrophic outcomes such as mass panic. Yet, these negative consequences are rare in integrated social systems such as social insect colonies, thanks to mechanisms of social regulation. Here, we test the hypothesis that behavioral deactivation between active individuals is a powerful social regulator that reduces energetic spending in groups. Borrowing from scaling theories for human settlements and using behavioral data on harvester ants, we derive ties between the hypermetric scaling of the interaction network and the hypometric scaling of activity levels, both relative to the colony size. We use elements of economics theory and metabolic measurements collected with the behavioral data to link activity and metabolic scalings with group size. Our results support the idea that metabolic scaling across social systems is the product of different balances between their social regulation mechanisms.

Indexed as

activity regulationmetabolismnetworksscaling theorysocial insects

Identifiers

PMID38962249
PMCPMC11220668

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.