Game-Theoretic Analysis of Transaction Selection in DAG-Based Distributed Ledgers

TL;DR

This paper uses game theory to analyze transaction selection mechanisms in DAG-based distributed ledgers, revealing how different fee-sharing schemes influence validator behavior, throughput, and fairness.

Contribution

It introduces a game-theoretic framework to derive Nash equilibria for transaction selection under two fee allocation mechanisms, providing insights for system design.

Findings

CFS achieves higher throughput and rewards than RFA at equilibrium.

Proportional selection often outperforms naive strategies like uniform selection.

Numerical simulations validate the advantages of CFS and proportional strategies.

Abstract

Transaction selection in parallel or DAG-based distributed ledger technologies (DLTs) is a crucial challenge that directly impacts throughput, fairness, and validator incentives. In these systems, validators independently choose transactions to include in their blocks, often relying on naive heuristics like uniform or proportional selection. This can lead to inefficient outcomes when validators prioritize their own rewards without considering collective impacts. We analyze two fee allocation mechanisms used in practice: Random Fee Allocation (RFA), where transaction fees are randomly assigned to one validator, and Collaborative Fee Sharing (CFS), where fees are distributed equally among all validators. Using a single-shot game-theoretic framework, we derive symmetric Nash equilibria (NE) for selecting transactions for both mechanisms and propose an optimization-based method to compute these equilibria. Numerical simulations demonstrate that the NE of CFS consistently achieves higher throughput and rewards compared to the NE of RFA, particularly under skewed fee distributions. Additionally, we compare these equilibrium strategies to naive benchmarks (uniform and proportional selection), showing that the proportional strategy outperforms the NE of RSA in many situations. These findings may provide actionable insights into the design of transaction selection and incentive mechanisms, enabling more robust and high-performance DAG-based DLTs.