Evidence map›Paper›PMID 40758755›Full record

ArticlePLoS computational biology2025

Emergence of cooperation promoted by higher-order strategy updates.

Dini Wang, Peng Yi, Yiguang Hong, Jie Chen, Gang Yan

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Dini WangCollege of Electronic and Information Engineering, Tongji University, Shanghai, People's Republic of China.
Peng YiCollege of Electronic and Information Engineering, Tongji University, Shanghai, People's Republic of China.ORCID 0000-0002-2494-1505
Yiguang HongCollege of Electronic and Information Engineering, Tongji University, Shanghai, People's Republic of China.
Jie ChenCollege of Electronic and Information Engineering, Tongji University, Shanghai, People's Republic of China.
Gang YanShanghai Research Institute for Intelligent Autonomous Systems, State Key Laboratory of Autonomous Intelligent Unmanned Systems, MOE Frontiers Science Center for Intelligent Autonomous Systems, and Shanghai Key Laboratory of Intelligent Autonomous Systems, Tongji University, Shanghai, People's Republic of China.

Funding

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaShanghai Municipal Science and Technology Major Project
6 · The paper itself

Abstract

Cooperation is fundamental to human societies, and the interaction structure among individuals profoundly shapes its emergence and evolution. In real-world scenarios, cooperation prevails in multi-group (higher-order) populations, beyond just dyadic behaviors. Despite recent studies on group dilemmas in higher-order networks, the exploration of cooperation driven by higher-order strategy updates remains limited due to the intricacy and indivisibility of group-wise interactions. Here we investigate four categories of higher-order mechanisms for strategy updates in public goods games and establish their mathematical conditions for the emergence of cooperation. Such conditions uncover the impact of both higher-order strategy updates and network properties on evolutionary outcomes, notably highlighting the enhancement of cooperation by overlaps between groups. Interestingly, we discover that the group-mutual comparison update - selecting a high-fitness group and then imitating a random individual within this group - can prominently promote cooperation. Our analyses further unveil that, compared to pairwise interactions, higher-order strategy updates generally improve cooperation in most higher-order networks. These findings underscore the pivotal role of higher-order strategy updates in fostering collective cooperation in complex social systems.

Indexed as

Cooperative BehaviorGroup ProcessesComputer SimulationDecision MakingGame Theory

Identifiers

PMID40758755
PMCPMC12321138

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LicenceCC BY
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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.