Transaction Fees, Block Size Limit and Auctions in Bitcoin

Confirmation of Bitcoin transactions is executed in blocks, which are then stored in the Blockchain. As compared to the number of transactions in the mempool, the set of transactions which are verified but not yet confirmed, available space for inclusion in a block is typically limited. For this reason, successful miners can only process a subset of such transactions, and users compete with each other to enter the next block by offering confirmation fees. Assuming that successful miners pursue revenue maximization, they will include in the block those mempool transactions that maximize earnings from related fees. In the paper we model transaction fees as a Nash Equilibrium outcome of an auction game with complete information. In the game the successful miner acts as an auctioneer selling block space, and users bid for shares of such space to confirm their transactions. Moreover, based on expected fees we also discuss what the optimal, revenue maximizing, block size limit should be for the successful miner. Consistently with the intuition, the optimal block size limit resolves the trade-off between including additional transactions (which possibly lower the unit fees collected) and keeping the block capacity limited (with, however, higher unit fees)

A Systematic Approach to Cryptocurrency Fees

This paper is devoted to the study of transaction fees in massively replicated open blockchain systems. In such systems, like Bitcoin, a snapshot of current state required for the validation of transactions is being held in the memory, which eventually becomes a scarce resource. Uncontrolled state growth can lead to security issues. We propose a modification of a transaction fee scheme based on how much additional space will be needed for the objects created as a result of transaction processing and for how long will they live in the state. We also work out the way to combine fees charged for different resources spent (bandwidth, random-access state memory, processor cycles) in a composite fee and demonstrate consistency of the approach by analyzing the statistics from Ethereum network. We show a possible implementation for state-related fee in a form of regular payments to miners