Evolution of altruistic punishments among heterogeneous conditional cooperators
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Abstract
It has been known that altruistic punishments solve the free rider problem in public goods games. Considering spatial structure and considering pure strategies significant advances have been made in understanding the evolution of altruistic punishments. However, these models have not considered key behavior regularities observed in experimental and field settings, where the individuals behave like conditional cooperators who are more willing to donate and are also more willing to punish free riders. Considering these behavioral regularities, without imposing a spatial structure on the population, I propose an evolutionary agent-based model in which agents behave like conditional cooperators, each agent’s donation conditional on the difference between the number of donations in the past and the threshold value and the propensity value of the agent. Altruistic punishment depends on the difference between the threshold value of the focal agent and the randomly matched another agent. The simulations show that, for certain inflicted costs of punishments, generous altruistic punishments evolve and stabilize cooperation. The results show that, unlike previous models, it is not necessary to punish all free riders equally; it is necessary to do so in the case of the selfish free riders but not in the case of negative reciprocators.
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Altruistic punishment does not distinguish between those who free ride due to selfish interests and those who free ride only because others are doing so.
“Moreover, this mechanism does not distinguish between those who free ride due to selfish interests and those who free ride only because others are doing so44.”
From Cooperation without punishment (2023) · cites this paper as [44]
The Method (‘Altruistic punishments’; page unavailable) says the model punishes free riders more frequently than occasional free riders. The Discussion likewise says effective punishment targets free riders more frequently and occasional free riders less frequently, and distinguishes selfish free riders from negative reciprocators. That is contrary to the claim that the mechanism does not distinguish between them. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredNegative reciprocity can be switched to positive reciprocity given sufficient cooperation levels.
“Our novel alternative to punishment builds on the reciprocal nature of the trust—the fact that people reciprocate voluntary trust68—as well as the conditional nature of human cooperation—the fact that negative reciprocity can be switched to positive reciprocity given sufficient cooperation levels44,69–71.”
From Cooperation without punishment (2023) · cites this paper as [44]
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- Authors
- Battu, B.
- Affiliations
- European University Institute (IT)
- Corresponding author
- Battu, Balaraju
- ORCID
- Battu, Balaraju
- Venue
- Scientific Reports
- Published
- 2021-05-18
- Volume
- 11
- Issue
- 1
- Pages
- 10502-10502
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8131352
- OpenAlex
- W3134048386
- Cited by
- 5
- References
- 55
- Citations by year
- 2026: 1 · 2023: 3 · 2021: 1
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Cadmus - EUI Research Repository (European University Institute) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Evolutionary Psychology and Human Behavior
- Keywords
- altruistic punishment, conditional cooperation, public goods game, free-rider problem, evolution of cooperation, negative reciprocity, cost of punishment, agent-based modeling
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