Christopher Graser, Takako Fujiwara-Greve, Julián García, Matthijs van Veelen
{"title":"Repeated games with partner choice.","authors":"Christopher Graser, Takako Fujiwara-Greve, Julián García, Matthijs van Veelen","doi":"10.1371/journal.pcbi.1012810","DOIUrl":null,"url":null,"abstract":"<p><p>Repetition is a classic mechanism for the evolution of cooperation. The standard way to study repeated games is to assume that there is an exogenous probability with which every interaction is repeated. If it is sufficiently likely that interactions are repeated, then reciprocity and cooperation can evolve together in repeated prisoner's dilemmas. Who individuals interact with can however also be under their control, or at least to some degree. If we change the standard model so that it allows for individuals to terminate the interaction with their current partner, and find someone else to play their prisoner's dilemmas with, then this limits the effectiveness of disciplining each other within the partnership, as one can always leave to escape punishment. The option to leave can however also be used to get away from someone who is not cooperating, which also has a disciplining effect. We find that the net effect of introducing the option to leave on cooperation is positive; with the option to leave, the average amount of cooperation that evolves in simulations is substantially higher than without. One of the reasons for this increase in cooperation is that partner choice creates endogenous phenotypic assortment. Compared to the standard models for the co-evolution of reciprocity and cooperation, and models of kin selection, our model thereby produces a better match with many forms of human cooperation in repeated settings. Individuals in our model end up interacting, not with random others that they cannot separate from, once matched, or with others that they are genetically related to, but with partners that they choose to stay with, and that are similarly dependable not to play defect as they are themselves.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 2","pages":"e1012810"},"PeriodicalIF":3.8000,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS Computational Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1371/journal.pcbi.1012810","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Repetition is a classic mechanism for the evolution of cooperation. The standard way to study repeated games is to assume that there is an exogenous probability with which every interaction is repeated. If it is sufficiently likely that interactions are repeated, then reciprocity and cooperation can evolve together in repeated prisoner's dilemmas. Who individuals interact with can however also be under their control, or at least to some degree. If we change the standard model so that it allows for individuals to terminate the interaction with their current partner, and find someone else to play their prisoner's dilemmas with, then this limits the effectiveness of disciplining each other within the partnership, as one can always leave to escape punishment. The option to leave can however also be used to get away from someone who is not cooperating, which also has a disciplining effect. We find that the net effect of introducing the option to leave on cooperation is positive; with the option to leave, the average amount of cooperation that evolves in simulations is substantially higher than without. One of the reasons for this increase in cooperation is that partner choice creates endogenous phenotypic assortment. Compared to the standard models for the co-evolution of reciprocity and cooperation, and models of kin selection, our model thereby produces a better match with many forms of human cooperation in repeated settings. Individuals in our model end up interacting, not with random others that they cannot separate from, once matched, or with others that they are genetically related to, but with partners that they choose to stay with, and that are similarly dependable not to play defect as they are themselves.
期刊介绍：
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery.
Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines.
Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights.
Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology.
Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
