Wednesday, February 7, 2018 at 6:00pm
Cornell Tech, 2 W Loop Rd, New York, NY 10044, Bloomberg 061
Please register at https://cornell.qualtrics.com/jfe/form/SV_9Z9YdZruzw9a2vr.
Owned by nobody and controlled by an almost immutable protocol, the Bitcoin cryptocurrency payment system is a platform with two main constituencies: users, who make and receive payments; and
profit-seeking miners, who maintain the system's infrastructure. We seek to understand the economics of the system: How does the system raise revenue to pay for its infrastructure? How are usage fees determined? How much infrastructure is deployed? What are the implications of changing parameters in the protocol?
To address these questions, we offer and analyze an economic model of a cryptocurrency system featuring user-generated transaction fees, and focus on Bitcoin as the leading example. These fees, as well as the system’s infrastructure level (number of servers in use), are determined in an equilibrium of a congestion-queueing game derived from the system's limited throughput. The system eliminates dead-weight loss from monopoly, but introduces other inefficiencies. Namely, the system requires significant congestion to raise revenue and fund infrastructure. In the absence of significant congestion, meaningful transaction-fee generation will cease, and in the long-term the cryptocurrency system will risk collapse. Moreover, the current design of the system, specifically the processing of large but infrequent blocks of transactions, makes the system less efficient at
The analysis leads to design suggestions for future cryptocurrencies.
This is joint work with Gur Huberman and Jacob D. Leshno.
Bio: Ciamac C. Moallemi is an Associate Professor of Business in the Decision, Risk, and Operations Division of the Graduate School of Business at Columbia University, where he has been since
2007. He received S.B. degrees in electrical engineering & computer science and in mathematics from the Massachusetts Institute of Technology (1996). He studied at the University of Cambridge,
where he earned a Master of Advanced Study degree in mathematics (Part III of the Mathematical Tripos), with distinction (1997). He received a Ph.D. in electrical engineering from Stanford University (2007). Prior to his doctoral studies, he developed quantitative methods in a number of entrepreneurial ventures: as a partner in a $200 million fixed-income arbitrage hedge fund and as the director of scientific computing at an early-stage drug discovery start-up. He holds editorial positions at the journals Operations Research and Management Science. He is a member of INFORMS. He is a past recipient of the British Marshall Scholarship (1996), the Benchmark Stanford Graduate Fellowship (2003), first place in the INFORMS Junior Faculty Paper Competition (2011), and the Best Simulation Publication Award of the INFORMS Simulation Society (2014). His research interests are in the area of the optimization and control of large-scale stochastic systems and decision-making under uncertainty, with an emphasis on applications in financial engineering.