IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0180549.html
   My bibliography  Save this article

Suppressors of selection

Author

Listed:
  • Fernando Alcalde Cuesta
  • Pablo González Sequeiros
  • Álvaro Lozano Rojo

Abstract

Inspired by recent works on evolutionary graph theory, an area of growing interest in mathematical and computational biology, we present examples of undirected structures acting as suppressors of selection for any fitness value r > 1. This means that the average fixation probability of an advantageous mutant or invader individual placed at some node is strictly less than that of this individual placed in a well-mixed population. This leads the way to study more robust structures less prone to invasion, contrary to what happens with the amplifiers of selection where the fixation probability is increased on average for advantageous invader individuals. A few families of amplifiers are known, although some effort was required to prove it. Here, we use computer aided techniques to find an exact analytical expression of the fixation probability for some graphs of small order (equal to 6, 8 and 10) proving that selection is effectively reduced for r > 1. Some numerical experiments using Monte Carlo methods are also performed for larger graphs and some variants.

Suggested Citation

  • Fernando Alcalde Cuesta & Pablo González Sequeiros & Álvaro Lozano Rojo, 2017. "Suppressors of selection," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
  • Handle: RePEc:plo:pone00:0180549
    DOI: 10.1371/journal.pone.0180549
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180549
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0180549&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0180549?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Erez Lieberman & Christoph Hauert & Martin A. Nowak, 2005. "Evolutionary dynamics on graphs," Nature, Nature, vol. 433(7023), pages 312-316, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fernando Alcalde Cuesta & Pablo González Sequeiros & Álvaro Lozano Rojo, 2018. "Evolutionary regime transitions in structured populations," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
    2. Benjamin Allen & Christine Sample & Patricia Steinhagen & Julia Shapiro & Matthew King & Timothy Hedspeth & Megan Goncalves, 2021. "Fixation probabilities in graph-structured populations under weak selection," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-25, February.
    3. Benjamin Allen & Christine Sample & Robert Jencks & James Withers & Patricia Steinhagen & Lori Brizuela & Joshua Kolodny & Darren Parke & Gabor Lippner & Yulia A Dementieva, 2020. "Transient amplifiers of selection and reducers of fixation for death-Birth updating on graphs," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-20, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konno, Tomohiko, 2013. "An imperfect competition on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5453-5460.
    2. R. Bentley & Michael O’Brien & Paul Ormerod, 2011. "Quality versus mere popularity: a conceptual map for understanding human behavior," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 10(2), pages 181-191, December.
    3. Xiang Wei & Peng Xu & Shuiting Du & Guanghui Yan & Huayan Pei, 2021. "Reputational preference-based payoff punishment promotes cooperation in spatial social dilemmas," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(10), pages 1-7, October.
    4. Wang, Mengyao & Pan, Qiuhui & He, Mingfeng, 2020. "The effect of individual attitude on cooperation in social dilemma," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    5. Lv, Shaojie & Song, Feifei, 2022. "Particle swarm intelligence and the evolution of cooperation in the spatial public goods game with punishment," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    6. Wu, Jieyu & Shao, Xinyu & Li, Jinhang & Huang, Gang, 2012. "Scale-free properties of information flux networks in genetic algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1692-1701.
    7. Benjamin Allen & Christine Sample & Robert Jencks & James Withers & Patricia Steinhagen & Lori Brizuela & Joshua Kolodny & Darren Parke & Gabor Lippner & Yulia A Dementieva, 2020. "Transient amplifiers of selection and reducers of fixation for death-Birth updating on graphs," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-20, January.
    8. K. Kułakowski, 2009. "The norm game: punishing enemies and not friends," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 4(1), pages 27-37, June.
    9. Zhang, Hui & Wang, Li & Hou, Dongshuang, 2016. "Effect of the spatial autocorrelation of empty sites on the evolution of cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 296-308.
    10. Huo, Ran & Durrett, Rick, 2018. "Latent voter model on locally tree-like random graphs," Stochastic Processes and their Applications, Elsevier, vol. 128(5), pages 1590-1614.
    11. Antoine Nongaillard & Philippe Mathieu, 2011. "Reallocation Problems in Agent Societies: A Local Mechanism to Maximize Social Welfare," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(3), pages 1-5.
    12. Laura Schmid & Farbod Ekbatani & Christian Hilbe & Krishnendu Chatterjee, 2023. "Quantitative assessment can stabilize indirect reciprocity under imperfect information," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Feng, Sinan & Liu, Xuesong & Dong, Yida, 2022. "Limited punishment pool may promote cooperation in the public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    14. Wakano, Joe Yuichiro & Ohtsuki, Hisashi & Kobayashi, Yutaka, 2013. "A mathematical description of the inclusive fitness theory," Theoretical Population Biology, Elsevier, vol. 84(C), pages 46-55.
    15. Tetsushi Ohdaira, 2021. "Cooperation evolves by the payoff-difference-based probabilistic reward," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(11), pages 1-8, November.
    16. Bryant, Adam S. & Lavrentovich, Maxim O., 2022. "Survival in branching cellular populations," Theoretical Population Biology, Elsevier, vol. 144(C), pages 13-23.
    17. McAvoy, Alex & Fraiman, Nicolas & Hauert, Christoph & Wakeley, John & Nowak, Martin A., 2018. "Public goods games in populations with fluctuating size," Theoretical Population Biology, Elsevier, vol. 121(C), pages 72-84.
    18. Jonas I Liechti & Gabriel E Leventhal & Sebastian Bonhoeffer, 2017. "Host population structure impedes reversion to drug sensitivity after discontinuation of treatment," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-19, August.
    19. Qiguang An & Hongfeng Guo & Yating Zheng, 2022. "On Robust Stability and Stabilization of Networked Evolutionary Games with Time Delays," Mathematics, MDPI, vol. 10(15), pages 1-12, July.
    20. Bo Xianyu, 2010. "Social Preference, Incomplete Information, and the Evolution of Ultimatum Game in the Small World Networks: An Agent-Based Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(2), pages 1-7.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0180549. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.