Environmental reputation-based preference selection and strategy learning mechanisms promote cooperation
•A novel framework that integrates environmental reputation-based preference selection with an improved strategy learning mechanism is introduced.•Individuals select their neighbors based not only on payoffs but also on their reputational standing in relation to the environmental reputation.•Individ...
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| Published in: | Applied mathematics and computation Vol. 512; p. 129761 |
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| Abstract | •A novel framework that integrates environmental reputation-based preference selection with an improved strategy learning mechanism is introduced.•Individuals select their neighbors based not only on payoffs but also on their reputational standing in relation to the environmental reputation.•Individuals update their strategies through a compatible learning rule that incorporates both factors, moderated by a reputation intensity parameter w.•The results demonstrate that the model effectively enhances both cooperation density and group reputation.•When the reputation intensity parameter w is sufficiently large, cooperation can be maintained at a high level even under strong temptations to defect.•In square lattice networks, increasing the reputation intensity not only elevates the cooperation density but also improves the overall payoff of the population.•In Barabáasi-Albert scale-free networks, nodes with degrees above a certain threshold consistently emerge as high-reputation cooperators.
We introduce a novel framework that integrates environmental reputation-based preference selection with an improved strategy learning mechanism. Specifically, individuals select their neighbors based not only on payoffs but also on their reputational standing relative to the environmental reputation. They then update their strategies through a compatible learning rule that incorporates both factors, moderated by a reputation intensity parameter w. We carried out Prisoner’s Dilemma Game (PDG) simulations of the model on both a square lattice network and a Barabási–Albert scale-free network. The results demonstrate that the model effectively enhances both cooperation density and group reputation. Moreover, when the reputation intensity parameter w is sufficiently large, cooperation can be maintained at a high level even under strong temptations to defect, in sharp contrast to traditional models where cooperation rapidly collapses as defection temptation increases. Our findings further indicate that in square lattice networks, increasing the reputation intensity not only elevates the cooperation density but also improves the overall payoff of the population. In Barabási–Albert scale-free networks, nodes with degrees above a certain threshold consistently emerge as high-reputation cooperators. Overall, the model performs effectively on both network types, confirming its robustness. |
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| AbstractList | •A novel framework that integrates environmental reputation-based preference selection with an improved strategy learning mechanism is introduced.•Individuals select their neighbors based not only on payoffs but also on their reputational standing in relation to the environmental reputation.•Individuals update their strategies through a compatible learning rule that incorporates both factors, moderated by a reputation intensity parameter w.•The results demonstrate that the model effectively enhances both cooperation density and group reputation.•When the reputation intensity parameter w is sufficiently large, cooperation can be maintained at a high level even under strong temptations to defect.•In square lattice networks, increasing the reputation intensity not only elevates the cooperation density but also improves the overall payoff of the population.•In Barabáasi-Albert scale-free networks, nodes with degrees above a certain threshold consistently emerge as high-reputation cooperators.
We introduce a novel framework that integrates environmental reputation-based preference selection with an improved strategy learning mechanism. Specifically, individuals select their neighbors based not only on payoffs but also on their reputational standing relative to the environmental reputation. They then update their strategies through a compatible learning rule that incorporates both factors, moderated by a reputation intensity parameter w. We carried out Prisoner’s Dilemma Game (PDG) simulations of the model on both a square lattice network and a Barabási–Albert scale-free network. The results demonstrate that the model effectively enhances both cooperation density and group reputation. Moreover, when the reputation intensity parameter w is sufficiently large, cooperation can be maintained at a high level even under strong temptations to defect, in sharp contrast to traditional models where cooperation rapidly collapses as defection temptation increases. Our findings further indicate that in square lattice networks, increasing the reputation intensity not only elevates the cooperation density but also improves the overall payoff of the population. In Barabási–Albert scale-free networks, nodes with degrees above a certain threshold consistently emerge as high-reputation cooperators. Overall, the model performs effectively on both network types, confirming its robustness. |
| ArticleNumber | 129761 |
| Author | Liang, Rongqian Fan, Suohai |
| Author_xml | – sequence: 1 givenname: Rongqian surname: Liang fullname: Liang, Rongqian – sequence: 2 givenname: Suohai orcidid: 0000-0002-4211-9726 surname: Fan fullname: Fan, Suohai email: tfsh@jnu.edu.cn |
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| Cites_doi | 10.1038/359826a0 10.1103/PhysRevE.78.066101 10.1038/nature04131 10.1088/1367-2630/ad0171 10.1088/1742-5468/ac7a28 10.1209/0295-5075/92/38003 10.1371/journal.pbio.0040299 10.1016/j.jtbi.2010.09.034 10.1103/PhysRevE.86.031141 10.1371/journal.pcbi.1000758 10.1126/science.7466396 10.1103/PhysRevE.95.052316 10.1016/j.physleta.2023.128837 10.1038/31225 10.1140/epjb/e2008-00470-8 10.1109/TSMC.2023.3315963 10.1103/PhysRevE.108.024106 10.1103/PhysRevE.78.026117 10.1063/5.0139874 10.1126/science.1133755 10.1140/epjb/s10051-021-00212-w 10.1016/j.biosystems.2009.10.003 10.1098/rspa.2022.0290 10.1098/rspb.2003.2336 10.1126/science.288.5467.850 |
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| Keywords | Environmental reputation Reputation Preference selection Cooperation Reputation intensity |
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| Title | Environmental reputation-based preference selection and strategy learning mechanisms promote cooperation |
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