IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i4p1753-1758.html
   My bibliography  Save this article

Three-state majority-vote model on square lattice

Author

Listed:
  • Lima, F.W.S.

Abstract

Here, a non-equilibrium model with two states (−1,+1) and a noise q on simple square lattices proposed for M.J. Oliveira (1992) following the conjecture of up-down symmetry of Grinstein and colleagues (1985) is studied and generalized. This model is well-known, today, as the majority-vote model. They showed, through Monte Carlo simulations, that their obtained results fall into the universality class of the equilibrium Ising model on a square lattice. In this work, we generalize the majority-vote model for a version with three states, now including the zero state, (−1,0,+1) in two dimensions. Using Monte Carlo simulations, we showed that our model falls into the universality class of the spin-1 (−1,0,+1) and spin-1/2 Ising model and also agree with majority-vote model proposed for M.J. Oliveira (1992). The exponent ratio obtained for our model was γ/ν=1.77(3), β/ν=0.121(5), and 1/ν=1.03(5). The critical noise obtained and the fourth-order cumulant were qc=0.106(5) and U∗=0.62(3).

Suggested Citation

  • Lima, F.W.S., 2012. "Three-state majority-vote model on square lattice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1753-1758.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1753-1758
    DOI: 10.1016/j.physa.2011.10.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437111008314
    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.2011.10.033?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. Takamitsu Watanabe, 2020. "A numerical study on efficient jury size," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-7, December.
    2. Pickhardt, Michael & Seibold, Goetz, 2014. "Income tax evasion dynamics: Evidence from an agent-based econophysics model," Journal of Economic Psychology, Elsevier, vol. 40(C), pages 147-160.

    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:391:y:2012:i:4:p:1753-1758. 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.