模糊厌恶下基于区块链挖矿算力投资模型研究

Equilibrium Model of the Market for Bitcoin Mining Under Ambiguity Aversion

本文研究比特币挖矿市场中的动态均衡,假设挖矿这一不可逆投资中总的奖励满足肖-布朗(Choquet-Brown)过程,考虑矿工是模糊厌恶的,在此基础之上利用比特币兑美元的汇率建立比特币网络算力预测模型并运用金融观点下的随机分析方法展开分析。研究表明,矿工挖矿行为的决策受到技术更新、电力成本、市场环境等多种因素的影响,矿工的挖矿收入因自由进入而产生上限,且当其他条件不变时,模糊程度越高,算力的价值越低,挖矿回报也越低。矿工的行为与现有的传统行业动态经济学理论非常一致,矿工的挖矿投资可以近似看作一个具有不可逆投资的行业动态的标准模型。

This paper studies the dynamic equilibrium in the market for bitcoin mining,assuming that the total reward in the irreversible investment of mining meets Choquet-Brown process.Considering that miners are ambiguity averse,the bitcoin network computing power prediction model by using the exchange rate of bitcoin to the US dollar,and to analyzing by using the stochastic analysis method from the financial point of view.The research shows that the decision-making and behaviors of miners mining are affected by such factors as technology update,the cost of the electricity and market environment.The upper limit of miners mining income is generated due to free entry,and when other conditions remain unchanged,the higher the degree of ambiguity,the lower the value of computing power and the lower the mining return.The behaviors of miners are quite consistent with the existing traditional industry dynamic economic theory.The mining investment of miners can be approximated as a standard model of industry dynamics with irreversible investment.