The Research on Maximum Profit Model for Bitcoin Mining Based on Markov

Abstract

Mining rewards are significantly influenced by consensus and incentive mechanisms, resulting in diverse returns based on the strategies employed by miners. While numerous studies have explored Bitcoin mining strategies, this paper focuses on the selfish mining strategy proposed by Eyal and Sirer. We establish a mathematical model to analyze the probability distributions of honest and selfish nodes. By transforming the problem of optimizing relative rewards into a decision-making problem within an infinite-state Markov process framework, this study aims to offer insights for future quantitative analyses of returns from selfish mining strategies and assist in optimizing mining strategies.