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      1月8日 李泉林教授學術報告(數學與統計學院)


      報 告 人: 李泉林 教授

      報告題目:Markov Processes in Blockchain Systems





             李泉林,北京工業大學經管學院教授、博士生導師,研究領域包括隨機模型、隨機過程、博弈論、排隊論、計算機網絡、網絡安全、網絡資源管理、網絡信息理論、超市模型、負載調配模型、RFID技術、物聯網、大數據、云計算、區塊鏈、數據中心、醫療服務系統、共享經濟、制造系統、供應鏈管理等方面。他在重要的國際學術刊物上發表了60余篇SCI學術論文;在Springer出版專著《Constructive Computation in Stochastic Models with Applications:RG-Factorizations》;在Springer主編論文集《Editorial for the special issue: Retrial Queues (WRQ'2010), Guest Editors,Operational Research: An International Journal,2012》、《Queueing Theory and Network Applications. Lecture Notes in Computer Science, Volume 10591,2017》、《Stochastic Models in Reliability,Network Security and System Safety: Dedicated to Jinhua Cao on the Occasion of His 80th Birthday. Communications in Computer and Information Science, Volume1102, 2019》;30余次擔任排隊論、隨機模型與應用概率等領域重要國際學術會議的學術委員會委員(4次大會主席);獲得了2004年教育部新世紀優秀人才、2005年教育部自然科學一等獎、2007年北京市科學技術二等獎、2008年北京市精品課、2013年河北省科技領軍人才計劃、2014年河北省科學技術二等獎、2015年INFORMS優秀論文獎、2018年第7屆計算社會網絡國際會議(CSoNet2018)唯一最佳論文獎。


             This talk focuses on our recent research on Markov Processes in Blockchain Systems. The blockchain systems are established as multi-dimensional Markov processes by means of the longest chain rule of chain-fork structure. We address several interesting issues or topics related to the multi-dimensional Markov processes. This further sets up mathematical models and develops economic theory of blockchain. Here, we shall care for:

      (1) How to study the multi-dimensional Markov processes, for example, stable conditions, steady-state probability, first passage time, sojourn time and so forth. Perhaps the Markov processes bring you to enter a queer theoretical space from such an interesting practical technology.

      (2) Block reward, transaction fee and their allocation methods greatly motivate many miners in a blockchain to take shape some selfish mining alliances evolutionarily, while the selfish mining alliances will lead to various attacks on security of blockchain. As a first exploration, we provide a unified and comprehensive framework for expressing the attacks grown out of the selfish mining alliances, a physical structure of which is given a detailed observation and interpretation in terms of the Markov processes. This may be viewed as a key improvement in the study of blockchain mining management. On the other hand, our method can also be developed to analyze blochchain systems through some simple and intuitive applications of Markov decision processes and stochastic game modeling.

      (3) We show that the multi-dimensional Markov processes will play an important role in the study of blockchain systems and in the design of consensus mechanism of related distributed systems. Also, they can motivate a series of promising future research on development of blockchain technologies.