IDEAS home Printed from https://ideas.repec.org/a/hin/complx/8500709.html

Effects of Punitive Measures on Free Riding and Collective Immunity Under Q-Learning–Driven Epidemic Dynamics

Author

Listed:
  • K. M. Ariful Kabir
  • Israt Jahan
  • Jun Tanimoto

Abstract

Free-riding behavior poses a critical challenge to achieving collective immunity in vaccination campaigns, particularly when individuals make decisions based on short-term self-interest. This study investigates how punitive interventions can mitigate free-riding and enhance vaccination uptake under adaptive decision-making. We develop an integrated evolutionary epidemic framework that combines evolutionary game theory with Q-learning, where individuals update their vaccination strategies by weighing vaccination cost, infection risk, vaccine effectiveness, and penalties for noncompliance. The model incorporates spatial interactions on structured populations, allowing local learning feedback and epidemic spreading to coevolve dynamically. Analytical results and numerical simulations reveal that punitive measures reshape the reinforcement learning reward structure, progressively discouraging free-riding and promoting cooperative vaccination behavior over repeated seasons. Vaccination effectiveness is a critical determinant: punishment alone is insufficient under low efficacy, whereas moderate-to-high efficacy regimes enable punitive incentives to align individual learning with collective welfare. Spatial clustering emerges as vaccinated individuals form stable blocks that impede transmission, reducing epidemic size and preventing resurgence. Extending the analysis across multiple network topologies demonstrates that while punitive intervention is broadly effective, its system-level impact is strongly modulated by network structure through local reinforcement, hub-driven dynamics, and information propagation. These findings highlight the importance of integrating adaptive learning dynamics, network heterogeneity, and behaviorally informed incentives into epidemic modeling, offering practical guidance for designing robust and resilient vaccination policies that mitigate free-riding and strengthen population-level immunity.

Suggested Citation

  • K. M. Ariful Kabir & Israt Jahan & Jun Tanimoto, 2026. "Effects of Punitive Measures on Free Riding and Collective Immunity Under Q-Learning–Driven Epidemic Dynamics," Complexity, Hindawi, vol. 2026, pages 1-21, March.
  • Handle: RePEc:hin:complx:8500709
    DOI: 10.1155/cplx/8500709
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/complexity/2026/8500709.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/complexity/2026/8500709.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/cplx/8500709?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
    ---><---

    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:hin:complx:8500709. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.