Evidence map›Paper›PMID 38881842›Full record

ArticlePNAS nexus2024

Enhancing social cohesion with cooperative bots in societies of greedy, mobile individuals.

Lei Shi, Zhixue He, Chen Shen, Jun Tanimoto

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Lei ShiSchool of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China.ORCID https://orcid.org/0000-0003-1203-9984
Zhixue HeSchool of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China.ORCID https://orcid.org/0000-0003-0964-5756
Chen ShenFaculty of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan.ORCID https://orcid.org/0000-0002-2725-9763
Jun TanimotoFaculty of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan.ORCID https://orcid.org/0000-0002-4092-3219

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Addressing collective issues in social development requires a high level of social cohesion, characterized by cooperation and close social connections. However, social cohesion is challenged by selfish, greedy individuals. With the advancement of artificial intelligence (AI), the dynamics of human-machine hybrid interactions introduce new complexities in fostering social cohesion. This study explores the impact of simple bots on social cohesion from the perspective of human-machine hybrid populations within network. By investigating collective self-organizing movement during migration, results indicate that cooperative bots can promote cooperation, facilitate individual aggregation, and thereby enhance social cohesion. The random exploration movement of bots can break the frozen state of greedy population, help to separate defectors in cooperative clusters, and promote the establishment of cooperative clusters. However, the presence of defective bots can weaken social cohesion, underscoring the importance of carefully designing bot behavior. Our research reveals the potential of bots in guiding social self-organization and provides insights for enhancing social cohesion in the era of human-machine interaction within social networks.

Indexed as

human–machine hybrid populationmigrationprisoner’s dilemmaself-organization movementsocial cohesion

Identifiers

PMID38881842
PMCPMC11179109

What OpenQuestion holds

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LicenceCC BY-NC
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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.