Evidence map›Paper›PMID 37978181›Full record

ArticleNature communications2023

Imitation dynamics on networks with incomplete information.

Xiaochen Wang, Lei Zhou, Alex McAvoy, Aming Li

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
13.9field-weighted citation impact, top 1% of its field
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

6 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Promoting collective cooperation through temporal interactions.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  5. Article
  6. Article
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

4 authors at 3 institutions in 2 countries.

Xiaochen Wang *Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.ORCID http://orcid.org/0000-0003-2987-1312
Lei Zhou *School of Automation, Beijing Institute of Technology, Beijing, 100081, China.ORCID http://orcid.org/0000-0002-5904-2462
Alex McAvoySchool of Data Science and Society, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.ORCID http://orcid.org/0000-0002-9110-4635
Aming LiCenter for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China. amingli@pku.edu.cn.ORCID http://orcid.org/0000-0003-4045-8721
Peking University · CNBeijing Institute of Technology · CNUniversity of North Carolina at Chapel Hill · US

Funding

Beijing Nova Program Z211100002121105National Natural Science Foundation of China (National Science Foundation of China) 62303049
6 · The paper itself

Abstract

Imitation is an important learning heuristic in animal and human societies. Previous explorations report that the fate of individuals with cooperative strategies is sensitive to the protocol of imitation, leading to a conundrum about how different styles of imitation quantitatively impact the evolution of cooperation. Here, we take a different perspective on the personal and external social information required by imitation. We develop a general model of imitation dynamics with incomplete information in networked systems, which unifies classical update rules including the death-birth and pairwise-comparison rule on complex networks. Under pairwise interactions, we find that collective cooperation is most promoted if individuals neglect personal information. If personal information is considered, cooperators evolve more readily with more external information. Intriguingly, when interactions take place in groups on networks with low degrees of clustering, using more personal and less external information better facilitates cooperation. Our unifying perspective uncovers intuition by examining the rate and range of competition induced by different information situations.

Identifiers

PMID37978181
PMCPMC10656501
OpenAlexW4388762146

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.