Evolution of cooperation driven by sampling punishment

被引：26

作者：

Xiao, Jiafeng ^{[1
]}

Liu, Linjie ^{[2
]}

Chen, Xiaojie ^{[1
]}

Szolnoki, Attila ^{[3
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China

[2] Northwest A&F Univ, Coll Sci, Yangling 712100, Peoples R China

[3] Inst Tech Phys & Mat Sci, Ctr Energy Res, POB 49, H-1525 Budapest, Hungary

来源：

PHYSICS LETTERS A | 2023年 / 475卷

基金：

中国国家自然科学基金;

关键词：

Sampling punishment; Cooperation; Public goods game; Collective -risk social dilemma; PROMOTES COOPERATION; REPLICATOR DYNAMICS; REPUTATION; EMERGENCE; TRAGEDY; SYSTEM; RISK;

D O I：

10.1016/j.physleta.2023.128879

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Punishment is a key incentive to maintain cooperation. But it is costly, hence its effectiveness is under permanent debate. To overcome this difficulty, we here introduce a sampling punishment method and explore its consequences on public goods game and collective-risk social dilemma. In our model, just a small portion of participants are sampled and defectors are punished only if their portion exceeds a certain threshold. We show that the cooperation level can be effectively improved under low punishment threshold and small sample size when the punishment intensity is considerable. To identify the optimal conditions of sampling punishment, we monitor the punishment probability and related cost. We find that the adoption of a low punishment threshold and a small sample size under a considerable punishment intensity is the best, which always leads to a high cooperation level with a low related cost. Our observations are both valid in these two games.(c) 2023 Elsevier B.V. All rights reserved.

引用

页数：8

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