论文标题
比特币采矿的平均现场游戏方法
Mean Field Game Approach to Bitcoin Mining
论文作者
论文摘要
我们对使用平均现场游戏框架在比特币区块链上使用的工作证明共识算法进行了分析。使用主方程，我们提供了用于挖掘区块链（Hashrate）的总计算能力的平衡表征。从简单的设置中，我们显示主方程方法如何通过放松大多数简化的假设来丰富模型。游戏的基本结构在所有富集中都保存。在确定性的环境中，哈希拉特最终达到了以技术进步速度增加的稳态。在随机设置中，存在每个可能的随机状态的哈希酸盐的目标。结果，我们表明，在均衡中，基础区块链的安全性要么是$ i）$ constand，又是$ ii）$随着对基础加密货币的需求而增加。
We present an analysis of the Proof-of-Work consensus algorithm, used on the Bitcoin blockchain, using a Mean Field Game framework. Using a master equation, we provide an equilibrium characterization of the total computational power devoted to mining the blockchain (hashrate). From a simple setting we show how the master equation approach allows us to enrich the model by relaxing most of the simplifying assumptions. The essential structure of the game is preserved across all the enrichments. In deterministic settings, the hashrate ultimately reaches a steady state in which it increases at the rate of technological progress. In stochastic settings, there exists a target for the hashrate for every possible random state. As a consequence, we show that in equilibrium the security of the underlying blockchain is either $i)$ constant, or $ii)$ increases with the demand for the underlying cryptocurrency.
