IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v531y2019ics0378437119310593.html
   My bibliography  Save this article

Incentive mechanism design for security investment with local exit equilibrium on structured populations

Author

Listed:
  • Zhu, Xiangbin
  • Zhao, Jiaying
  • Hu, Zhaolong

Abstract

The fact that most of cyber security problems are interdependent security problems, in which one agent’s security is affected not only by this agent’s security investment but also by the security decisions of his neighbors, has encouraged more and more people to study security games and security investment on large computer networks. In this paper, we study interdependent security problems by modeling the problems as public goods games on structured populations. We put forward an incentive mechanism named quantized externality mechanism to improve the overall social payoff in evolutionary public goods games of security investment on complex networks. Moreover, we introduce local exit equilibrium into our new mechanism to satisfy voluntary participation principle. Simulation results show that our mechanism can play an important role in increasing the level of security investment on complex networks. We also find that the participation rate of our new mechanism can converge to a stable level.

Suggested Citation

  • Zhu, Xiangbin & Zhao, Jiaying & Hu, Zhaolong, 2019. "Incentive mechanism design for security investment with local exit equilibrium on structured populations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
  • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119310593
    DOI: 10.1016/j.physa.2019.121801
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119310593
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2019.121801?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 search for a different version of it.

    Citations

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


    Cited by:

    1. Wang, Zhen & Li, Chaofan & Jin, Xing & Ding, Hong & Cui, Guanghai & Yu, Lanping, 2021. "Evolutionary dynamics of the interdependent security games on complex network," Applied Mathematics and Computation, Elsevier, vol. 399(C).

    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:phsmap:v:531:y:2019:i:c:s0378437119310593. 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: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.