IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-3-031-68974-1_8.html
   My bibliography  Save this book chapter

Discrete and Bayesian Transaction Fee Mechanisms

In: Mathematical Research for Blockchain Economy

Author

Listed:
  • Yotam Gafni

    (Weizmann Institute of Science)

  • Aviv Yaish

    (The Hebrew University)

Abstract

Cryptocurrencies employ auction-esque transaction fee mechanisms (TFMs) to allocate transactions to blocks, and to determine how much fees miners can collect from transactions. Several impossibility results show that TFMs that satisfy a standard set of “good” properties obtain low revenue, and in certain cases, no revenue at all. In this work, we circumvent previous impossibilities by showing that when desired TFM properties are reasonably relaxed, simple mechanisms can obtain strictly positive revenue. By discretizing fees, we design a TFM that satisfies the extended TFM desiderata: it is dominant strategy incentive-compatible (DSIC), myopic miner incentive-compatible (MMIC), side-contract-proof (SCP) and obtains asymptotically optimal revenue (i.e., linear in the number of allocated bids), and optimal revenue when considering separable TFMs. If instead of discretizing fees we relax the DSIC and SCP properties, we show that Bitcoin’s TFM, after applying the revelation principle, is Bayesian incentive-compatible (BIC), MMIC, off-chain-agreement (OCA) proof, and approximately revenue-optimal. We reach our results by characterizing the class of multi-item OCA-proof mechanisms, which may be of independent interest.

Suggested Citation

  • Yotam Gafni & Aviv Yaish, 2024. "Discrete and Bayesian Transaction Fee Mechanisms," Lecture Notes in Operations Research, in: Stefanos Leonardos & Elise Alfieri & William J. Knottenbelt & Panos Pardalos (ed.), Mathematical Research for Blockchain Economy, chapter 0, pages 145-171, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-68974-1_8
    DOI: 10.1007/978-3-031-68974-1_8
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:lnopch:978-3-031-68974-1_8. 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.