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

2026-05-08

AI Summary

This study addresses the low throughput, poor fairness, and insufficient validator incentives in DAG-based distributed ledgers caused by transaction selection strategies. For the first time, it introduces a one-shot game model into this setting and analyzes validator behavior under two fee-allocation mechanisms—Random Fee Assignment (RFA) and Collaborative Fee Sharing (CFS)—within a game-theoretic framework. By deriving symmetric Nash equilibria and designing optimization algorithms to compute them, the work demonstrates that under skewed fee distributions, CFS significantly outperforms RFA. Moreover, proportional selection strategies often surpass the equilibrium performance of RFA across diverse scenarios. The results indicate that the equilibrium strategy under CFS consistently dominates in both throughput and reward distribution, thereby challenging the efficacy of conventional heuristic approaches.

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.

Problem

• transaction selection
• DAG-based DLTs
• validator incentives
• throughput
• fairness

Innovation

• game theory
• transaction selection
• DAG-based DLT
• fee allocation mechanism
• Nash equilibrium