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Cited by 16Evolutionary Game Theory and CooperationExperimental Behavioral Economics StudiesGame Theory and Applications

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

A remarkable effect of the combination of probabilistic peer-punishment and coevolutionary mechanism on the evolution of cooperation

Open access
Ohdaira, Tetsushi·2017·Scientific Reports
DOI 10.1038/s41598-017-12742-4PMC PMC5622126
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Abstract

In the previous studies, the author has proposed the probabilistic peer-punishment based on the difference of payoff, and presented that the proposed peer-punishment utilizes its mechanism for preventing antisocial punishment like retaliation of a defector on a cooperator, effectively enhances the evolution of cooperation, and greatly increases the average payoff of all players in various parameters regarding static three types of topology of connections. Here, this study introduces both activities of breaking and creating connections of every player based on his/her preference to the model of the proposed peer-punishment. Every player will keep connections with his/her preferable players, whereas he/she will frequently break connections with his/her dissatisfied other players. Therefore, the new model of this study is the combination of probabilistic peer-punishment and coevolutionary mechanism that not only strategy of players but also connections between players evolve. This study discovers new knowledge that such combination induces high-level evolution of cooperation and great increase of the average payoff of all players in the condition where cooperation is hard to evolve.

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Crossref · 10.1038/s41598-017-12742-4

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39 of 39 references verified0 citation errors · 0 not checkable

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  • Punishment-based mechanisms only solve the free-rider problem under restrictive conditions.

    “Unfortunately, numerous studies have shown that punishment-based mechanisms only solve the free-rider problem under restrictive conditions34–38.”

    From Cooperation without punishment (2023) · cites this paper as [37]

    The Introduction says the author's earlier peer-punishment model enhanced cooperation in a limited condition. But the Results report that adding coevolving connections widened the range of punishment strengths under which high-level cooperation emerged. The paper therefore supports a limitation of one earlier model, not the broad claim that punishment-based mechanisms only work under restrictive conditions. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Pool punishment is ineffective when the cost of maintaining central authority outweighs the benefits of cooperation.

    “Specifically, pool punishment is ineffective when the cost of maintaining central authority outweighs the benefits of cooperation.”

    From Cooperation without punishment (2023) · cites this paper as [37]

    The Introduction discusses probabilistic peer-punishment and the problem of costly punishment, while the Discussion reports effects on cooperation and payoff. Searches for pool punishment, central authority, and maintenance costs found no passage saying that pool punishment becomes ineffective when authority costs outweigh cooperation benefits. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Altruistic punishment is ineffective when social interactions are noisy.

    “However, this solution is ineffective when counter-punishments are possible and when social interactions are noisy.”

    From Cooperation without punishment (2023) · cites this paper as [37]

    The Discussion states that the study includes no additive random payoff disturbances, so it does not test whether punishment is ineffective under noisy social interactions. Searches for noise and random payoff disturbances found no passage in our copy making that claim. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

  • Altruistic punishment is ineffective when counter-punishments are possible.

    “However, this solution is ineffective when counter-punishments are possible and when social interactions are noisy.”

    From Cooperation without punishment (2023) · cites this paper as [37]

    The Introduction mentions retaliation by a defector against a cooperator, but says the proposed probabilistic peer-punishment prevents it; it does not say altruistic punishment becomes ineffective when counter-punishment is possible. The Discussion says the study has no additive random payoff disturbances, so it also does not establish ineffectiveness under noisy interactions (pages unavailable in our copy). (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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Metadata

Authors
Ohdaira, T.
Affiliations
Aoyama Gakuin University (JP)
Corresponding author
Ohdaira, Tetsushi
ORCID
Ohdaira, Tetsushi
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2017-09-29
Volume
7
Issue
1
Pages
12448-12448
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-017-12742-4
PMC
PMC5622126
OpenAlex
W2756679083
Cited by
16
References
39
Citations by year
2026: 1 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 3 · 2019: 2 · 2018: 3
FWCI
3.50
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Japan Society for the Promotion of Science (17K18074)
Topics
  • Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Experimental Behavioral Economics Studies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
Keywords
evolution of cooperation, coevolution, antisocial punishment, network topology, social dilemmas
MeSH terms
Peer Influence, Adult, Computer Simulation, Cooperative Behavior, Female, Game Theory, Humans, Male, Models, Psychological, Peer Group, Punishment, Models, Statistical, Social Skills

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