Designing Multidimensional Blockchain Fee Markets

Authors Theo Diamandis, Alex Evans, Tarun Chitra, Guillermo Angeris

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Author Details

Theo Diamandis
  • MIT CSAIL, Cambridge, MA, USA
Alex Evans
  • Bain Capital Crypto, San Francisco, CA, USA
Tarun Chitra
  • Gauntlet, New York, NY, USA
Guillermo Angeris
  • Bain Capital Crypto, San Francisco, CA, USA


We would like to thank John Adler, Vitalik Buterin, Dev Ojha, Kshitij Kulkarni, Matheus Ferreira, Barnabé Monnot, and Dinesh Pinto for helpful conversations, insights, and edits. We're especially appreciative to John Adler for bearing with us through many drafts of this work and consistently providing valuable feedback.

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Theo Diamandis, Alex Evans, Tarun Chitra, and Guillermo Angeris. Designing Multidimensional Blockchain Fee Markets. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 4:1-4:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Public blockchains implement a fee mechanism to allocate scarce computational resources across competing transactions. Most existing fee market designs utilize a joint, fungible unit of account (e.g., gas in Ethereum) to price otherwise non-fungible resources such as bandwidth, computation, and storage, by hardcoding their relative prices. Fixing the relative price of each resource in this way inhibits granular price discovery, limiting scalability and opening up the possibility of denial-of-service attacks. As a result, many prominent networks such as Ethereum and Solana have proposed multidimensional fee markets. In this paper, we provide a principled way to design fee markets that efficiently price multiple non-fungible resources. Starting from a loss function specified by the network designer, we show how to dynamically compute prices that align the network’s incentives (to minimize the loss) with those of the users and miners (to maximize their welfare), even as demand for these resources changes. We derive an EIP-1559-like mechanism from first principles as an example. Our pricing mechanism follows from a natural decomposition of the network designer’s problem into two parts that are related to each other via the resource prices. These results can be used to efficiently set fees in order to improve network performance.

Subject Classification

ACM Subject Classification
  • Theory of computation → Convex optimization
  • Information systems → Digital cash
  • Theory of computation → Algorithmic mechanism design
  • Blockchains
  • transaction fees
  • convex optimization
  • mechanism design


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