IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2508.00363.html
   My bibliography  Save this paper

Bayesian tit-for-tat fosters cooperation in evolutionary stochastic games

Author

Listed:
  • Arunava Patra
  • Supratim Sengupta
  • Sagar Chakraborty

Abstract

Learning from experience is a key feature of decision-making in cognitively complex organisms. Strategic interactions involving Bayesian inferential strategies can enable us to better understand how evolving individual choices to be altruistic or selfish can affect collective outcomes in social dilemmas. Bayesian strategies are distinguished, from their reactive opponents, in their ability to modulate their actions in the light of new evidence. We investigate whether such strategies can be resilient against reactive strategies when actions not only determine the immediate payoff but can affect future payoffs by changing the state of the environment. We use stochastic games to mimic the change in environment in a manner that is conditioned on the players' actions. By considering three distinct rules governing transitions between a resource-rich and a resource-poor states, we ascertain the conditions under which Bayesian tit-for-tat strategy can resist being invaded by reactive strategies. We find that the Bayesian strategy is resilient against a large class of reactive strategies and is more effective in fostering cooperation leading to sustenance of the resource-rich state. However, the extent of success of the Bayesian strategies depends on the other strategies in the pool and the rule governing transition between the two different resource states.

Suggested Citation

  • Arunava Patra & Supratim Sengupta & Sagar Chakraborty, 2025. "Bayesian tit-for-tat fosters cooperation in evolutionary stochastic games," Papers 2508.00363, arXiv.org.
  • Handle: RePEc:arx:papers:2508.00363
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2508.00363
    File Function: Latest version
    Download Restriction: no
    ---><---

    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:arx:papers:2508.00363. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.