Evidence map›Paper›PMID 40681680›Full record

ArticleScientific reports2025

Evolution of cooperation guided by the coexistence of imitation learning and reinforcement learning.

Wei Tang, Guoling Wang, Zhiyan Xing

Abstract read
In one paragraph

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

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

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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

3 authors.

Wei TangSchool of Information Engineering, Guizhou Open University, Guiyang, Guizhou, 550023, China.
Guoling WangSchool of Mathematics and Statistics, Guizhou University, Guiyang, Guizhou, 550025, China. 709256669@qq.com.
Zhiyan XingSchool of Mathematics and Statistics, Guizhou University, Guiyang, Guizhou, 550025, China.

Funding

Guizhou Provincial Science and Technology Projects ZK[2022] General 168National Science Foundation of China 11271098Scientific Research Projects for the Introduced Talents of Guizhou University Scientific Research Projects for the Introduced Talents of Guizhou UniversityUniversity-Level Research Project of Guizhou Open University General Project No. 2023YB26
6 · The paper itself

Abstract

Promoting cooperation remains a major challenge in natural science. While most studies focus on single strategy update rules, individuals in real-life often use multiple strategies in response to dynamic environments. This paper introduces a mixed update rule combining imitation and reinforcement learning (RL). In imitation learning (IL), individuals adopt strategies from higher-payoff opponents, while RL relies on personal experience. Simulations of the Prisoner's Dilemma Game (PDG), Coexistence Game (CG), and Coordination Game (CoG), both in well-mixed populations and square lattice networks, show that: (i) cooperation and defection coexist in the PDG, resolving the dilemma of universal defection; (ii) cooperation exceeds the mixed Nash equilibrium in the CG; and (iii) cooperators dominate in the CoG. The mixed update rule outperforms single strategy approaches in those games, highlighting its effectiveness in fostering cooperation.

Indexed as

Cooperative BehaviorImitative BehaviorLearningReinforcement, PsychologyComputer SimulationGame TheoryHumansPrisoner DilemmaCooperative evolutionEvolutionary gamesFinite homogeneous populationsImitation learningReinforcement learning

Identifiers

PMID40681680
PMCPMC12274395

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Registered trials

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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.