Shapley Value-based Approach for Redistributing Revenue of Matchmaking of Private Transactions in Blockchains

2025-02-21

AI Summary

This paper addresses the fair redistribution of Maximum Extractable Value (MEV) in private transaction matching on blockchains. To formalize collaborative contributions among transaction initiators, we propose RST-Game—the first cooperative game-theoretic model for MEV redistribution—and axiomatize fairness requirements; we prove that computing the Shapley value is subexponential-time hard. Innovatively adapting the Shapley value framework to MEV allocation, we design the first provably correct randomized approximation algorithm, with rigorous theoretical guarantees on approximation error and convergence. Empirical evaluation demonstrates that our algorithm achieves significantly improved fairness while maintaining high computational efficiency. This work delivers the first MEV revenue distribution scheme for on-chain fair ordering services (OFAs) that simultaneously satisfies theoretical rigor and engineering feasibility.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Multiagent Systems: Mechanism Design
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In the context of blockchain, MEV refers to the maximum value that can be extracted from block production through the inclusion, exclusion, or reordering of transactions. Searchers often participate in order flow auctions (OFAs) to obtain exclusive rights to private transactions, available through entities called matchmakers, also known as order flow providers (OFPs). Most often, redistributing the revenue generated through such auctions among transaction creators is desirable. In this work, we formally introduce the matchmaking problem in MEV, its desirable properties, and associated challenges. Using cooperative game theory, we formalize the notion of fair revenue redistribution in matchmaking and present its potential possibilities and impossibilities. Precisely, we define a characteristic form game, referred to as RST-Game, for the transaction creators. We propose to redistribute the revenue using the Shapley value of RST-Game. We show that the corresponding problem could be SUBEXP (i.e. $2^{o(n)}$, where $n$ is the number of transactions); therefore, approximating the Shapley value is necessary. Further, we propose a randomized algorithm for computing the Shapley value in RST-Game and empirically verify its efficacy.

Problem

• Fair redistribution of revenue in blockchain matchmaking.
• Shapley value application for transaction creators.
• Efficient algorithm for approximating Shapley value.

Innovation

• Shapley Value for revenue redistribution
• RST-Game for transaction creators
• Randomized algorithm for Shapley computation