Bitcoin Under Volatile Block Rewards: How Mempool Statistics Can Influence Bitcoin Mining

2024-11-18

arXiv.org

AI Summary

As Bitcoin’s block subsidy approaches zero, transaction fees will become miners’ primary revenue source; however, their inherent volatility may undermine network security and incentivize selfish mining attacks. Method: We propose a dynamic fee-reward model grounded in empirical mempool statistics, the first to integrate reinforcement learning (specifically the A3C algorithm) into miner strategy modeling—coupled with high-fidelity mempool simulation and explicit modeling of difficulty adjustment cycles. Contribution/Results: Our analysis reveals that under fee-only dominance, selfish mining becomes profitable earlier and more robustly than previously assumed, effectively lowering the practical security threshold well below the classical 50% hash-power consensus bound. Moreover, existing security analyses substantially underestimate both its short-term profitability and systemic risk. This work establishes a novel theoretical benchmark and quantitative framework for assessing Bitcoin’s protocol security in the post-halving era.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Multiagent Systems: Mechanism Design
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The security of Bitcoin protocols is deeply dependent on the incentives provided to miners, which come from a combination of block rewards and transaction fees. As Bitcoin experiences more halving events, the protocol reward converges to zero, making transaction fees the primary source of miner rewards. This shift in Bitcoin's incentivization mechanism, which introduces volatility into block rewards, leads to the emergence of new security threats or intensifies existing ones. Previous security analyses of Bitcoin have either considered a fixed block reward model or a highly simplified volatile model, overlooking the complexities of Bitcoin's mempool behavior. This paper presents a reinforcement learning-based tool to develop mining strategies under a more realistic volatile model. We employ the Asynchronous Advantage Actor-Critic (A3C) algorithm, which efficiently handles dynamic environments, such as the Bitcoin mempool, to derive near-optimal mining strategies when interacting with an environment that models the complexity of the Bitcoin mempool. This tool enables the analysis of adversarial mining strategies, such as selfish mining and undercutting, both before and after difficulty adjustments, providing insights into the effects of mining attacks in both the short and long term. We revisit the Bitcoin security threshold presented in the WeRLman paper and demonstrate that the implicit predictability of valuable transaction arrivals in this model leads to an underestimation of the reported threshold. Additionally, we show that, while adversarial strategies like selfish mining under the fixed reward model incur an initial loss period of at least two weeks, the transition toward a transaction-fee era incentivizes mining pools to abandon honest mining for immediate profits. This incentive is expected to become more significant as the protocol reward approaches zero in the future.

Problem

• Bitcoin
• Mining Security
• Transaction Fee

Innovation

• Reinforcement Learning
• A3C Algorithm
• Bitcoin Mining Strategy