Mobility can promote the evolution of cooperation via emergent self-assortment dynamics

dc.contributor.authorJoshi, Jaideep
dc.contributor.authorCouzin, Iain D.
dc.contributor.authorLevin, Simon A.
dc.contributor.authorGuttal, Vishwesha
dc.date.accessioned2017-11-17T10:32:26Z
dc.date.available2017-11-17T10:32:26Z
dc.date.issued2017-09eng
dc.description.abstractThe evolution of costly cooperation, where cooperators pay a personal cost to benefit others, requires that cooperators interact more frequently with other cooperators. This condition, called positive assortment, is known to occur in spatially-structured viscous populations, where individuals typically have low mobility and limited dispersal. However many social organisms across taxa, from cells and bacteria, to birds, fish and ungulates, are mobile, and live in populations with considerable inter-group mixing. In the absence of information regarding others' traits or conditional strategies, such mixing may inhibit assortment and limit the potential for cooperation to evolve. Here we employ spatially-explicit individual-based evolutionary simulations to incorporate costs and benefits of two coevolving costly traits: cooperative and local cohesive tendencies. We demonstrate that, despite possessing no information about others' traits or payoffs, mobility (via self-propulsion or environmental forcing) facilitates assortment of cooperators via a dynamically evolving difference in the cohesive tendencies of cooperators and defectors. We show analytically that this assortment can also be viewed in a multilevel selection framework, where selection for cooperation among emergent groups can overcome selection against cooperators within the groups. As a result of these dynamics, we find an oscillatory pattern of cooperation and defection that maintains cooperation even in the absence of well known mechanisms such as kin interactions, reciprocity, local dispersal or conditional strategies that require information on others' strategies or payoffs. Our results offer insights into differential adhesion based mechanisms for positive assortment and reveal the possibility of cooperative aggregations in dynamic fission-fusion populations.eng
dc.description.versionpublishedeng
dc.identifier.doi10.1371/journal.pcbi.1005732eng
dc.identifier.pmid28886010eng
dc.identifier.ppn49546130X
dc.identifier.urihttps://kops.uni-konstanz.de/handle/123456789/40658
dc.language.isoengeng
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.ddc570eng
dc.titleMobility can promote the evolution of cooperation via emergent self-assortment dynamicseng
dc.typeJOURNAL_ARTICLEeng
dspace.entity.typePublication
kops.citation.bibtex
@article{Joshi2017-09Mobil-40658,
  year={2017},
  doi={10.1371/journal.pcbi.1005732},
  title={Mobility can promote the evolution of cooperation via emergent self-assortment dynamics},
  number={9},
  volume={13},
  journal={PLoS Computational Biology},
  author={Joshi, Jaideep and Couzin, Iain D. and Levin, Simon A. and Guttal, Vishwesha},
  note={Article Number: e1005732}
}
kops.citation.iso690JOSHI, Jaideep, Iain D. COUZIN, Simon A. LEVIN, Vishwesha GUTTAL, 2017. Mobility can promote the evolution of cooperation via emergent self-assortment dynamics. In: PLoS Computational Biology. 2017, 13(9), e1005732. eISSN 1553-7358. Available under: doi: 10.1371/journal.pcbi.1005732deu
kops.citation.iso690JOSHI, Jaideep, Iain D. COUZIN, Simon A. LEVIN, Vishwesha GUTTAL, 2017. Mobility can promote the evolution of cooperation via emergent self-assortment dynamics. In: PLoS Computational Biology. 2017, 13(9), e1005732. eISSN 1553-7358. Available under: doi: 10.1371/journal.pcbi.1005732eng
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kops.sourcefieldPLoS Computational Biology. 2017, <b>13</b>(9), e1005732. eISSN 1553-7358. Available under: doi: 10.1371/journal.pcbi.1005732deu
kops.sourcefield.plainPLoS Computational Biology. 2017, 13(9), e1005732. eISSN 1553-7358. Available under: doi: 10.1371/journal.pcbi.1005732deu
kops.sourcefield.plainPLoS Computational Biology. 2017, 13(9), e1005732. eISSN 1553-7358. Available under: doi: 10.1371/journal.pcbi.1005732eng
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source.periodicalTitlePLoS Computational Biologyeng

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