Parallel Execution Fee Mechanisms

TL;DR

This paper explores pricing mechanisms in blockchain systems with multiple transaction queues to optimize efficiency and revenue, considering capacity constraints and demand characteristics.

Contribution

It models a capacity-constrained blockchain with multiple queues, analyzing how pricing schemes can optimize revenue and welfare across different demand scenarios.

Findings

Revenue maximization favors high-paying queues.

Welfare maximization tends to serve all queues.

Optimal pricing depends on market size, elasticity, and congestion balance.

Abstract

This paper investigates how pricing schemes can achieve efficient allocations in blockchain systems featuring multiple transaction queues under a global capacity constraint. I model a capacity-constrained blockchain where users submit transactions to different queues -- each representing a submarket with unique demand characteristics -- and decide to participate based on posted prices and expected delays. I find that revenue maximization tends to allocate capacity to the highest-paying queue, whereas welfare maximization generally serves all queues. Optimal relative pricing of different queues depends on factors such as market size, demand elasticity, and the balance between local and global congestion. My results have implications for the implementation of local congestion pricing for evolving blockchain architectures, including parallel transaction execution, directed acyclic graph (DAG)-based systems, and multiple concurrent proposers.