Expected Revenue, Risk, and Grid Impact of Bitcoin Mining: A Decision-Theoretic Perspective

2025-12-23

AI Summary

Existing Bitcoin mining evaluations predominantly rely on ex-post proxy metrics, failing to adequately capture uncertainty and dynamic adjustments. This paper introduces the first ex-ante statistical model grounded in the fundamental premise that hash computations constitute Bernoulli trials. It establishes a closed-form analytical framework incorporating Bitcoin’s difficulty adjustment mechanism to jointly quantify—per unit of computational power—the expected revenue, downside risk (Value-at-Risk and Expected Shortfall), and upside profit probability. Methodologically, the approach integrates Bayesian modeling, stochastic process analysis, empirical calibration, and sensitivity analysis, enabling comparable assessments across hardware types, mining pools, and operational conditions. The model accurately reproduces historical mining performance and provides an analytically tractable risk–return trade-off tool. It delivers a robust quantitative foundation for grid load forecasting and miner behavioral modeling.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and upside potential profit from the first principles of mining: Each hash is a Bernoulli trial with a Bitcoin block difficulty-based success probability. The model yields closed-form expected revenue per hash-rate unit, risk metrics in different scenarios, and upside-profit probabilities for different fleet sizes. Empirical calibration closely matches previously reported observations, yielding a unified, faithful quantification across hardware, pools, and operating conditions. This foundation enables more reliable analysis of mining impacts and behavior.

Problem

• Develops a unified ex ante model for Bitcoin mining revenue and risk
• Quantifies expected return, downside risk, and upside profit probabilistically
• Enables reliable analysis of mining impacts across hardware and conditions

Innovation

• Ex ante statistical model for mining uncertainty
• Closed-form revenue and risk metrics
• Empirical calibration across hardware and pools