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

TL;DR

This paper introduces a multi-agent dynamic pricing algorithm based on Gaussian bandits for blockchain indexers, optimizing revenue through consumer budget discovery, with successful simulation and real-world deployment results.

Contribution

It presents a novel multi-agent bandit-based dynamic pricing algorithm tailored for decentralized blockchain indexers, including its design, implementation, and deployment insights.

Findings

Effective revenue maximization demonstrated in simulation.

Successful deployment on Ethereum Indexer.

Open-sourced tools adopted by other Indexers.

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.