AI 中文总结
研究经济激励下区块链的抗审查性，创建博弈论模型，提出求解混合均衡算法，通过模拟和实证数据研究多个并发提议者场景下交易费用机制对吞吐量和抗审查性的影响，发现惩罚重复的机制占优。
AI 中文摘要
抗审查性是区块链相对于其中心化对等物的决定性优势。然而，区块提议者出于多种原因审查交易，从法律后果到经济激励。我们研究经济激励审查，由贿赂提议者排除目标交易的对手建模，并将交易的经济抗审查性（eCR）定义为对手成功审查的预期成本除以用户包含交易的预期支付。单提议者系统在这方面结构薄弱：在首价拍卖下，对手只需匹配用户出价，且费用燃烧将eCR推至用户支付的百分之几。因此我们转向多个并发提议者（MCP），其中区块容量在n个提议者之间分配，区块是他们子区块的并集。虽然MCP可通过要求对手贿赂多个提议者大幅增加审查成本，但也会引入交易重复，降低吞吐量。由此产生的权衡关键取决于交易费用机制（TFM），它决定费用在竞争提议者之间如何分配。我们创建了一个博弈论模型，其中验证者从共享内存池中构建区块，面临对手的贿赂企图。我们提供了一种算法，用于求解给定内存池的混合均衡，其特征是包含每个交易的概率。该算法适用于广泛的TFM，并允许我们计算任何出价分布的预期吞吐量和抗审查性。然后我们使用模拟展示随着提议者数量增加eCR和吞吐量如何变化。我们比较了三种TFM，发现在许多设置中惩罚重复的TFM占主导地位。我们还使用以太坊实证数据验证了我们的发现。
英文摘要
Censorship resistance is the defining advantage of blockchains over their centralized counterparts. Yet block proposers censor transactions for many reasons, from legal consequences to economic incentives. We study economically-incentivized censorship, modeled by an adversary who bribes proposers to exclude a target transaction, and define the economic censorship resistance (eCR) of a transaction as the adversary's expected cost of successful censorship divided by the user's expected payment for inclusion. Single-proposer systems are structurally weak by this measure: under a first-price auction the adversary need only match the user's bid, and fee burning pushes eCR to a few percent of what the user pays. We therefore turn to multiple concurrent proposers (MCP), where block capacity is divided among $n$ proposers and the block is the union of their sub-blocks. While MCP can substantially increase the cost of censorship by requiring the adversary to bribe many proposers, it also introduces transaction duplication, reducing throughput. The resulting trade-off depends critically on the transaction fee mechanism (TFM), which determines how fees are shared among competing proposers. We create a game theoretic model where validators construct blocks from a shared mempool, subject to an adversary's bribery attempt. We provide an algorithm that solves for the mixed equilibrium of a given mempool, which is characterized by the probability of including each transaction. This algorithm works for a wide class of TFMs, and allows us to calculate the expected throughput and censorship resistance for any bid distribution. We then use simulations to show how the eCR and throughput vary as the number of proposers increases. We compare three TFMs, finding that the duplication-penalizing TFM dominates the others across many settings. We also validate our findings with empirical Ethereum data.
