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Selfish Mining Under General Stochastic Rewards

Authors Maryam Bahrani , Michael Neuder , S. Matthew Weinberg

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ACM Subject Classification
  • Applied computing → Electronic commerce
  • Security and privacy → Distributed systems security
Keywords
  • Proof-of-Work
  • Selfish Mining
  • MEV

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Abstract

Selfish miners selectively withhold blocks to earn disproportionately high revenue. The vast majority of the selfish mining literature focuses exclusively on block rewards. [Carlsten et al., 2016] is a notable exception, observing that similar strategic behavior is profitable in a zero-block-reward regime (the endgame for Bitcoin’s quadrennial halving schedule) if miners are compensated with transaction fees alone. Neither model fully captures miner incentives today. The block reward remains 3.125 BTC, yet some blocks yield significantly higher revenue. For example, congestion during the launch of the Babylon protocol in August 2024 caused transaction fees to spike from 0.14 BTC to 9.52 BTC, a 68× increase in fees within two blocks.
Our results are both practical and theoretical. Of practical interest, we study selfish mining profitability under a combined reward function that more accurately models miner incentives. This analysis enables us to make quantitative claims about protocol risk (e.g., the mining power at which a selfish strategy becomes profitable is reduced by 22% when optimizing over the combined reward function versus block rewards alone) and qualitative observations (e.g., a miner considering both block rewards and transaction fees will mine more or less aggressively respectively than if they cared about either alone). These practical results follow from our novel model and methodology, which constitute our theoretical contributions. We model general, time-accruing stochastic rewards in the Nakamoto Consensus Game, which requires explicit treatment of difficult adjustment and randomness; we characterize reward function structure through a set of properties (e.g., that rewards accrue only as a function of time since the parent block). We present a new methodology to analytically calculate expected selfish miner rewards under a broad class of stochastic reward functions and validate our method numerically by comparing it with the existing literature and simulating the combined reward sources directly.

Cite As Get BibTex

Maryam Bahrani, Michael Neuder, and S. Matthew Weinberg. Selfish Mining Under General Stochastic Rewards. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 20:1-20:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.AFT.2025.20

Author Details

Maryam Bahrani
  • Ritual, Vancouver, BC, Canada
Michael Neuder
  • Ethereum Foundation, New York, NY, USA
S. Matthew Weinberg
  • Princeton University, NJ, USA

Funding

  • Weinberg, S. Matthew: Supported by NSF CAREER Award CCF-1942497.

References

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