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

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, distributing the revenue generated through such auctions among transaction creators (TCs) 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 distribution in matchmaking and present its potential possibilities and impossibilities. Precisely, we define a characteristic form game, referred to as RST-Game, for the TCs. We propose to distribute 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). Further, we propose a randomized algorithm for computing the approximate Shapley value in RST-Game and empirically demonstrate that the proposed RSYP estimates Shapley value that is very close to the actual Shapley value and also distributes the share amongst TCs fairly. RST-Game CCS CONCEPTS • Theory of computation → Solution concepts in game theory.