通过关键场景发现量化比特币网络弹性：一种用于发现去中心化共识中争议性分叉条件的双层框架
Quantifying Bitcoin Network Resilience Through Critical Scenario Discovery: A Dual-Layer Framework for Discovering Contentious Fork Conditions in Decentralized Consensus
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中文总结 AI 辅助
该研究应用情景发现方法，通过Warnet运行模拟实验，确定比特币争议性分叉的参数阈值，发现经济权重分布等因素对分叉结果的关键影响，提出转化阈值的监测问题。
中文摘要 AI 辅助
比特币的共识不仅依赖于协议规则，还依赖于具有不同经济权益的异构节点网络的涌现行为。争议性软分叉是会顺利解决还是会分裂为持续的链分叉，难以通过分析预测，但可通过受控实验进行探索。本文应用情景发现（Scenario Discovery）方法，这是一种使用患者规则归纳法（PRIM）的集成模拟方法，以识别产生争议性分叉结果的配置区域。使用Warnet运行真实的bitcoind节点，覆盖经济权重、矿池承诺、矿池意识形态、算力和难度调整机制的1330个有效情景，我们确定了将顺利解决与争议性分叉分开的参数阈值。我们表明，在比特币的运行调整间隔和适度价格分歧下，经济权重分布而非算力多数是解决结果的主要决定因素。得出三个发现：经济支持下限（0.45-0.50）和上限（0.78-0.82）界定了争议空间，其间存在一个经济自维持点（0.74）；矿池承诺的“翻转点”（承诺算力的0.214），此时将最大矿池承诺到升级链上会反常地反转结果；结果在两个独立层（算力和经济采用率）上解决，受不同参数支配。在建模的经济权重下，单个用户节点对分叉结果无显著影响。最后，我们提出三个可从公开数据中回答的监测问题，将这些阈值转化为操作指南。
英文摘要
Bitcoin's consensus depends not only on protocol rules but on the emergent behavior of a heterogeneous network of nodes with divergent economic stakes. Whether a contentious soft fork resolves cleanly or fractures into a persistent chain split is difficult to predict analytically but can be explored through controlled experimentation. This paper applies Scenario Discovery, an ensemble-simulation methodology using the Patient Rule Induction Method (PRIM), to identify the configuration regions that produce contentious fork outcomes. Using Warnet to run real bitcoind nodes across 1,330 valid scenarios spanning economic weight, mining-pool commitment, pool ideology, hashrate, and difficulty-retarget regime, we identify the parameter thresholds separating clean resolution from contested split. We show that economic weight distribution across a partition, not hashrate majority, is the primary determinant of resolution under Bitcoin's operational retarget interval and moderate price divergence. Three findings follow: an economic-support floor (0.45-0.50) and override ceiling (0.78-0.82) bound the contested space, with an Economic Self-Sustaining Point (0.74) between them; a pool-commitment "flip-point" (0.214 of committed hashrate) at which committing the largest pool to the upgrading chain paradoxically reverses the outcome; and outcomes resolve on two independent layers, hashrate and economic adoption, governed by different parameters. Individual user nodes show no detectable influence on fork outcomes under the modeled economic weightings. We close with three monitoring questions, answerable from public data, that translate these thresholds into operational guidance.
