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

TL;DR

This paper introduces a comprehensive probabilistic model for bitcoin mining that accurately predicts expected revenue, risk, and profit potential, improving upon previous methods that overlooked uncertainty and rapid industry changes.

Contribution

It presents a novel ex ante statistical framework based on first principles, providing closed-form metrics for revenue, risk, and profit potential in bitcoin mining.

Findings

Model closely matches empirical observations

Provides unified analysis across hardware and conditions

Enables more reliable impact and behavior analysis

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.