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Multi-Agent Dynamic Pricing in a Blockchain Protocol Using Gaussian Bandits

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Listed:
  • Alexis Asseman
  • Tomasz Kornuta
  • Anirudh Patel
  • Matt Deible
  • Sam Green

Abstract

The Graph Protocol indexes historical blockchain transaction data and makes it available for querying. As the protocol is decentralized, there are many independent Indexers that index and compete with each other for serving queries to the Consumers. One dimension along which Indexers compete is pricing. In this paper, we propose a bandit-based algorithm for maximization of Indexers' revenue via Consumer budget discovery. We present the design and the considerations we had to make for a dynamic pricing algorithm being used by multiple agents simultaneously. We discuss the results achieved by our dynamic pricing bandits both in simulation and deployed into production on one of the Indexers operating on Ethereum. We have open-sourced both the simulation framework and tools we created, which other Indexers have since started to adapt into their own workflows.

Suggested Citation

  • Alexis Asseman & Tomasz Kornuta & Anirudh Patel & Matt Deible & Sam Green, 2022. "Multi-Agent Dynamic Pricing in a Blockchain Protocol Using Gaussian Bandits," Papers 2212.07942, arXiv.org, revised Jan 2023.
    • Handle: RePEc:arx:papers:2212.07942
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    References listed on IDEAS

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    1. Wang Chi Cheung & David Simchi-Levi & He Wang, 2017. "Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation," Operations Research, INFORMS, vol. 65(6), pages 1722-1731, December.
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