勷勤数学•专家报告

题      目：What Distributional Reinforcement Learning is Learning? A Stochastic Control Perspective

报  告  人：秦玲 副教授  (邀请人：杨舟)

                                              上海科技大学

时      间：6月6日  15：00-16：00

地     点：数科院东楼401

报告人简介:

        Ling Qin is currently working in the Institute of Mathematical Sciences at ShanghaiTech University.  She earned her Ph.D. in Quantitative Finance from the Department of Mathematics at the National University of Singapore. Her research interests include stochastic control problems, portfolio selection, corporate finance, and climate finance.

摘      要：

       We propose a tractable dynamic equilibrium model to study the behavior of Bitcoin miners. To incorporate the endogenous entry and exit options of miners alongside technology innovation, a critical modeling step involves drawing on the idea of Dammon, Spatt, and Zhang (2001) and Ben Tahar, Soner, and Touzi (2010) to track the average operating costs rather than the exact operating costs, thus overcoming the difficulty of strong path-dependency incurred by the interaction among endogenous exit, entry, and technology innovation. The model can capture empirical co-movements of miners’ computing power and mining revenue. Additionally, it allows us to analyze the electricity consumption associated with Bitcoin mining and its impact on climate damage. We predict that both electricity consumption and climate damage will explode in the long run, indicating that Bitcoin mining is not economically sustainable in terms of long-term climate damage.

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