# Optimal Selfish Mining Strategies in Bitcoin Source: [Optimal Selfish Mining Strategies in Bitcoin]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:10:28.856Z Authors: Ayelet Sapirshtein, Yonatan Sompolinsky, Aviv Zohar Published: 2015-07-22T13:43:20.000Z ## Abstract Bitcoin is a decentralized crypto-currency, and an accompanying protocol, created in 2008. Bitcoin nodes continuously generate and propagate blocks---collections of newly approved transactions that are added to Bitcoin's ledger. Block creation requires nodes to invest computational resources, but also carries a reward in the form of bitcoins that are paid to the creator. While the protocol requires nodes to quickly distribute newly created blocks, strong nodes can in fact gain higher payoffs by withholding blocks they create and selectively postponing their publication. The existence of such selfish mining attacks was first reported by Eyal and Sirer, who have demonstrated a specific deviation from the standard protocol (a strategy that we name SM1). In this paper we extend the underlying model for selfish mining attacks, and provide an algorithm to find $\\epsilon$-optimal policies for attackers within the model, as well as tight upper bounds on the revenue of optimal policies. As a consequence, we are able to provide lower bounds on the computational power an attacker needs in order to benefit from selfish mining. We find that the profit threshold -- the minimal fraction of resources required for a profitable attack -- is strictly lower than the one induced by the SM1 scheme. Indeed, the policies given by our algorithm dominate SM1, by better regulating attack-withdrawals. Using our algorithm, we show that Eyal and Sirer's suggested countermeasure to selfish mining is slightly less effective than previously conjectured. Next, we gain insight into selfish mining in the presence of communication delays, and show that, under a model that accounts for delays, the profit threshold vanishes, and even small attackers have incentive to occasionally deviate from the protocol. We conclude with observations regarding the combined power of selfish mining and double spending attacks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Mining Revenue Ratio Estimation on Blockchain Network Simulation γ=0.2](): Mining Revenue Ratio Estimation; Blockchain Network Simulation γ=0.2; Revenue Ratio (ρ): 70. - [Mining Revenue Ratio Estimation on Blockchain Network Simulation γ=0.8](): Mining Revenue Ratio Estimation; Blockchain Network Simulation γ=0.8; Revenue Ratio (ρ): 80. - [Mining Revenue Ratio Estimation on Blockchain Network Simulation γ=0.4](): Mining Revenue Ratio Estimation; Blockchain Network Simulation γ=0.4; Revenue Ratio ($ ho$): 74. - [Mining Revenue Ratio Estimation on Blockchain Network Simulation (γ=0.6)](): Mining Revenue Ratio Estimation; Blockchain Network Simulation (γ=0.6); Revenue Ratio (ρ): 77.6. - [Mining Revenue Ratio Estimation on Blockchain Network Simulation γ=0.5](): Mining Revenue Ratio Estimation; Blockchain Network Simulation γ=0.5; Revenue Ratio (ρ): 76. - [Revenue Ratio Analysis on Blockchain Mining MDP Model gamma=0](): Revenue Ratio Analysis; Blockchain Mining MDP Model gamma=0 (Evaluation); Revenue Ratio: 0.80172. ## Original sources and implementation [Original paper]() [PDF]()