IDEAS home Printed from https://ideas.repec.org/a/spr/joevec/v15y2005i4p365-391.html
   My bibliography  Save this article

Evolutionary game dynamics and distributed recency-weighted learning

Author

Listed:
  • Yuya Sasaki

Abstract

This paper presents a relationship between evolutionary game dynamics and distributed recency-weighted Monte Carlo learning. After reviewing some existing theories of replicator dynamics and agent-based Monte Carlo learning, we provide proofs of the formulation-level equivalence between these two models. The relationship will be revealed not only from a theoretical viewpoint, but also by computational simulations of the models. As a consequence, macro dynamic patterns generated by distributed micro-decisions can be explained by parameters defined at an individual level. In particular, given the equivalent formulations, we investigate how the rate of agents’ recency weighting in learning affects the emergent evolutionary game dynamic patterns. An increase in this rate negatively affects the inertia, making the evolutionary stability condition more strict, and positively affecting the evolutionary speed toward equilibrium. Copyright Springer-Verlag Berlin/Heidelberg 2005

Suggested Citation

  • Yuya Sasaki, 2005. "Evolutionary game dynamics and distributed recency-weighted learning," Journal of Evolutionary Economics, Springer, vol. 15(4), pages 365-391, October.
  • Handle: RePEc:spr:joevec:v:15:y:2005:i:4:p:365-391
    DOI: 10.1007/s00191-005-0254-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00191-005-0254-z
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00191-005-0254-z?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. Xiaoyang Zhao, 2019. "Patenting Or Secret? The Interaction Between Leading Firms And Following Firms Based On Evolutionary Game Theory And Multi-Agent Simulation," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 23(07), pages 1-22, October.

    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:spr:joevec:v:15:y:2005:i:4:p:365-391. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.