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This study employs a dual-layer framework using Scenario Discovery and the Patient Rule Induction Method (PRIM) to analyze the conditions leading to contentious forks in the Bitcoin network. By simulating 1,330 scenarios with real bitcoind nodes, the authors identify critical parameter thresholds that determine whether a soft fork resolves cleanly or leads to a chain split, highlighting the dominance of economic weight distribution over hashrate majority. Key findings reveal specific economic thresholds that bound contested outcomes and a surprising "flip-point" where increasing pool commitment can paradoxically reverse expected results, providing actionable insights for monitoring Bitcoin's consensus dynamics.
Economic weight, not hashrate, is the key factor determining whether Bitcoin forks resolve cleanly or lead to persistent splits.
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.
