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.

翻译：在并行或基于有向无环图（DAG）的分布式账本技术（DLTs）中，交易选择是一项直接影响吞吐量、公平性和验证者激励的关键挑战。在这些系统中，验证者独立选择交易纳入其区块，通常依赖于朴素启发式方法，如均匀或比例选择。当验证者仅优先考虑自身奖励而忽视集体影响时，这可能导致低效结果。我们分析了实践中使用的两种费用分配机制：随机费用分配（RFA），即交易费用随机分配给一个验证者；以及协作费用共享（CFS），即费用在所有验证者间均等分配。利用单次博弈论框架，我们推导了两种机制下选择交易时的对称纳什均衡（NE），并提出了基于优化的方法来计算这些均衡。数值模拟表明，相较于RFA的NE，CFS的NE在吞吐量和奖励方面始终表现更优，尤其在偏斜的费用分布下。此外，我们将这些均衡策略与朴素基准（均匀和比例选择）进行比较，显示比例策略在多数情况下优于RSA的NE。这些发现可为交易选择与激励机制设计提供可行见解，助力构建更稳健、高性能的DAG型DLT系统。