IDEAS home Printed from https://ideas.repec.org/a/eee/matsoc/v127y2024icp1-11.html
   My bibliography  Save this article

Ultimatum bargaining with envy under incomplete information

Author

Listed:
  • Gonzalez-Sanchez, Eric
  • Loyola, Gino

Abstract

We propose an ultimatum bargaining model in which the parties experience an envy-based externality that is private information. Our results indicate that there is a threshold for the proposer’s envy which determines whether there will be either a perfectly equitable, certain agreement or an uncertain, inequitable agreement, and that this threshold rises as the distribution of the responder’s envy level improves in a first-order stochastic-dominance sense. In addition, conditionally on the scenario ruling out a perfectly equitable agreement, we show that the proposer’s envy level plays a dual role: (i) it increases the probability of a negotiation breakdown, and (ii) it constitutes a source of bargaining power. Numerical simulations also allow us to explore some properties of the role played by the responder’s envy and by changes in the envy distributions of the two players. Overall, our theoretical results are consistent with the main evidence from ultimatum experiments conducted in behavioral and neuroscience settings. In addition, we provide testable implications of our model for future experiments.

Suggested Citation

  • Gonzalez-Sanchez, Eric & Loyola, Gino, 2024. "Ultimatum bargaining with envy under incomplete information," Mathematical Social Sciences, Elsevier, vol. 127(C), pages 1-11.
  • Handle: RePEc:eee:matsoc:v:127:y:2024:i:c:p:1-11
    DOI: 10.1016/j.mathsocsci.2023.11.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165489623000896
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.mathsocsci.2023.11.001?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.

    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:matsoc:v:127:y:2024:i:c:p:1-11. 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.elsevier.com/locate/inca/505565 .

    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.