Evidence map›Paper›PMID 29070844›Full record

ArticleScientific reports2017

Individual mobility promotes punishment in evolutionary public goods games.

Rui Cong, Qianchuan Zhao, Kun Li, Long Wang

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Rui CongCenter for Intelligent and Networked Systems, Department of Automation and TNList, Tsinghua University, Beijing, 100084, China.ORCID 0000-0003-4159-9681
Qianchuan ZhaoCenter for Intelligent and Networked Systems, Department of Automation and TNList, Tsinghua University, Beijing, 100084, China. zhaoqc@tsinghua.edu.cn.
Kun LiCenter for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.
Long WangCenter for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In explaining the pressing issue in biology and social sciences how cooperation emerges in a population of self-interested individuals, researchers recently pay intensive attentions to the role altruistic punishment plays. However, as higher-order cooperators, survival of punishers is puzzling due to their extra cost in regulating norm violators. Previous works have highlighted the importance of individual mobility in promoting cooperation. Yet its effect on punishers remains to be explored. In this work we incorporate this feature into modeling the behavior of punishers, who are endowed with a choice between leaving current place or staying and punishing defectors. Results indicate that optimal mobility level of punishers is closely related to the cost of punishing. For considerably large cost, there exists medium tendency of migration which favors the survival of punishers. This holds for both the direct competition between punishers and defectors and the case where cooperators are involved, and can also be observed when various types of punishers with different mobility tendencies fight against defectors simultaneously. For cheap punishment, mobility does not provide with punishers more advantage even when they are initially rare. We hope our work provide more insight into understanding the role individual mobility plays in promoting public cooperation.

Indexed as

Biological EvolutionCooperative BehaviorGame TheoryPunishmentAltruismHumansInterpersonal RelationsModels, PsychologicalMonte Carlo MethodSocial Behavior

Identifiers

PMID29070844
PMCPMC5656631

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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.