IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v531y2026ics0096300326002821.html

Peer pressure driven adaptive migration with Q-learning promotes cooperation in the spatial Prisoner’s dilemma

Author

Listed:
  • Cheng, Yue
  • Yang, Qianxi
  • Yang, Yanlong

Abstract

In both natural and human societies, individuals often migrate in search of more favorable living conditions. However, the mechanisms by which individuals make migration decisions through reinforcement learning based on their perceived social environment are not yet fully understood. In this paper, we propose an adaptive migration mechanism driven by peer pressure. In this mechanism, individuals experience peer pressure due to strategic inconsistencies with their neighbors and use a pressure threshold to classify their current state as high-pressure or low-pressure. Based on this state, they adopt Q-learning to adaptively choose between migrating and staying. Simulation results show that neither pure random migration nor intelligent decision-making lacking peer pressure guidance can overcome the constraints of the prisoner’s dilemma. In contrast, our proposed migration mechanism exhibits a significant synergistic effect in spatial networks, sustaining cooperation even under relatively strong temptation to defect.

Suggested Citation

  • Cheng, Yue & Yang, Qianxi & Yang, Yanlong, 2026. "Peer pressure driven adaptive migration with Q-learning promotes cooperation in the spatial Prisoner’s dilemma," Applied Mathematics and Computation, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:apmaco:v:531:y:2026:i:c:s0096300326002821
    DOI: 10.1016/j.amc.2026.130230
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300326002821
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2026.130230?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:eee:apmaco:v:531:y:2026:i:c:s0096300326002821. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.