A Critical Analysis of Punishment in Public Goods Games

Garrison W. Greenwood, Hussein Abbass, Eleni Petraki

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)

Abstract

Social dilemmas arise whenever individuals must choose between their own self-interests or the welfare of a group. Economic games such as Public Goods Games (PGG) provide a framework for studying human behavior in social dilemmas. Cooperators put their self-interests aside for the group benefit while defectors free ride by putting their self-interests first. Punishment has been shown to be an effective mechanism for countering free riding in both model-based and human PGG experiments. But researchers always assume, since this punishment is costly to the punisher, it must be altruistic. In this study we show costly punishment in a PGG has nothing to do with altruism. Replicator dynamics are used to evolve strategies in a PGG. Our results show even a minority of punishers can improve cooperation levels in a population if the cooperators who punish are trustworthy. Finally, we argue punishment as a strategy in social dilemmas is never altruistic.

Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-5
Number of pages5
ISBN (Electronic)9781538643594
ISBN (Print)9781538643594
DOIs
Publication statusPublished - 2018
Event14th IEEE Conference on Computational Intelligence and Games, CIG 2018 - Maastricht, Netherlands
Duration: 14 Aug 201817 Aug 2018

Publication series

NameIEEE Conference on Computatonal Intelligence and Games, CIG
Volume2018-August

Conference

Conference14th IEEE Conference on Computational Intelligence and Games, CIG 2018
CountryNetherlands
CityMaastricht
Period14/08/1817/08/18

Fingerprint

Economics
Experiments

Cite this

Greenwood, G. W., Abbass, H., & Petraki, E. (2018). A Critical Analysis of Punishment in Public Goods Games. In Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018 (pp. 1-5). (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2018-August). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CIG.2018.8490421
Greenwood, Garrison W. ; Abbass, Hussein ; Petraki, Eleni. / A Critical Analysis of Punishment in Public Goods Games. Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018. IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 1-5 (IEEE Conference on Computatonal Intelligence and Games, CIG).
@inproceedings{87f87b8fab1d4409bff23b43696bda7e,
title = "A Critical Analysis of Punishment in Public Goods Games",
abstract = "Social dilemmas arise whenever individuals must choose between their own self-interests or the welfare of a group. Economic games such as Public Goods Games (PGG) provide a framework for studying human behavior in social dilemmas. Cooperators put their self-interests aside for the group benefit while defectors free ride by putting their self-interests first. Punishment has been shown to be an effective mechanism for countering free riding in both model-based and human PGG experiments. But researchers always assume, since this punishment is costly to the punisher, it must be altruistic. In this study we show costly punishment in a PGG has nothing to do with altruism. Replicator dynamics are used to evolve strategies in a PGG. Our results show even a minority of punishers can improve cooperation levels in a population if the cooperators who punish are trustworthy. Finally, we argue punishment as a strategy in social dilemmas is never altruistic.",
keywords = "Altruism, Costly punishment, Public goods games, Social dilemma",
author = "Greenwood, {Garrison W.} and Hussein Abbass and Eleni Petraki",
year = "2018",
doi = "10.1109/CIG.2018.8490421",
language = "English",
isbn = "9781538643594",
series = "IEEE Conference on Computatonal Intelligence and Games, CIG",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1--5",
booktitle = "Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018",
address = "United States",

}

Greenwood, GW, Abbass, H & Petraki, E 2018, A Critical Analysis of Punishment in Public Goods Games. in Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018. IEEE Conference on Computatonal Intelligence and Games, CIG, vol. 2018-August, IEEE, Institute of Electrical and Electronics Engineers, pp. 1-5, 14th IEEE Conference on Computational Intelligence and Games, CIG 2018, Maastricht, Netherlands, 14/08/18. https://doi.org/10.1109/CIG.2018.8490421

A Critical Analysis of Punishment in Public Goods Games. / Greenwood, Garrison W.; Abbass, Hussein; Petraki, Eleni.

Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018. IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 1-5 (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2018-August).

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - A Critical Analysis of Punishment in Public Goods Games

AU - Greenwood, Garrison W.

AU - Abbass, Hussein

AU - Petraki, Eleni

PY - 2018

Y1 - 2018

N2 - Social dilemmas arise whenever individuals must choose between their own self-interests or the welfare of a group. Economic games such as Public Goods Games (PGG) provide a framework for studying human behavior in social dilemmas. Cooperators put their self-interests aside for the group benefit while defectors free ride by putting their self-interests first. Punishment has been shown to be an effective mechanism for countering free riding in both model-based and human PGG experiments. But researchers always assume, since this punishment is costly to the punisher, it must be altruistic. In this study we show costly punishment in a PGG has nothing to do with altruism. Replicator dynamics are used to evolve strategies in a PGG. Our results show even a minority of punishers can improve cooperation levels in a population if the cooperators who punish are trustworthy. Finally, we argue punishment as a strategy in social dilemmas is never altruistic.

AB - Social dilemmas arise whenever individuals must choose between their own self-interests or the welfare of a group. Economic games such as Public Goods Games (PGG) provide a framework for studying human behavior in social dilemmas. Cooperators put their self-interests aside for the group benefit while defectors free ride by putting their self-interests first. Punishment has been shown to be an effective mechanism for countering free riding in both model-based and human PGG experiments. But researchers always assume, since this punishment is costly to the punisher, it must be altruistic. In this study we show costly punishment in a PGG has nothing to do with altruism. Replicator dynamics are used to evolve strategies in a PGG. Our results show even a minority of punishers can improve cooperation levels in a population if the cooperators who punish are trustworthy. Finally, we argue punishment as a strategy in social dilemmas is never altruistic.

KW - Altruism

KW - Costly punishment

KW - Public goods games

KW - Social dilemma

UR - http://www.scopus.com/inward/record.url?scp=85056880679&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/critical-analysis-punishment-public-goods-games

U2 - 10.1109/CIG.2018.8490421

DO - 10.1109/CIG.2018.8490421

M3 - Conference contribution

SN - 9781538643594

T3 - IEEE Conference on Computatonal Intelligence and Games, CIG

SP - 1

EP - 5

BT - Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018

PB - IEEE, Institute of Electrical and Electronics Engineers

ER -

Greenwood GW, Abbass H, Petraki E. A Critical Analysis of Punishment in Public Goods Games. In Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018. IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 1-5. (IEEE Conference on Computatonal Intelligence and Games, CIG). https://doi.org/10.1109/CIG.2018.8490421