Essays on Blockchain Technology and Digital Economics

In this first essay, together with my co-authors, we examine the economic mechanism of cryptocurrency mining. By presenting a profit function, a maximization equilibrium is obtained. The model provides a formal approach to the demand for hashing power as a function of revenues, mining costs and the number of miners. We consider how the equilibrium is affected by passive miners. We use these results to introduce a formulation of the price elasticity of the demand for hashing power with respect to the cost of energy. The model is simulated using Reinforcement Learning algorithms that arrive at similar equilibrium results. The article concludes with the implications of the model for policymaking. The Bitcoin Payment System (BPS) is the first and the most prominent decentralized protocol to send monetary transactions all around the world. The BPS does not use a predetermined fee mechanism. Instead, users propose fees they are willing to pay and the market determines the transactions that will be transmitted. In the second essay, I use a multi-unit share auction model to analyze the payoffs of the participants. I first use transaction data to non-parametrically estimate the marginal valuations of the bidders in the network and then estimate the payoff of the participants in every state of the system. As a result of the analysis, I realized that when the system is getting congested users bid closer to their marginal valuations so their payoffs would be decreased. On the other hand, miners would earn more since they are the service providers who collect fees at each step. Multiple cryptocurrencies suffer a bottleneck effect: blocks are limited in size and the protocols restrict their expected arrival rates. On the other hand, this congestion creates incentives to set transaction fees. We show that this incentive structure suffers from moral hazard, where miners have incentives to induce congestion to increase fees. In this third essay, together with my coauthor, we present this result using two approaches: Auction Theory and Reinforcement Learning. While Game Theory studies strategic behavior between rational players, Machine Learning is based on blind players finding optimal strategies by brute force iteration of trials. The Auction Theory model presented in this paper is a multiunit discriminatory (or pay-as-bid) auction with single-unit demand. We add to the standard model the element of supply reduction, characterize the symmetric equilibrium and present how to expand it as the number of players grows asymptotically. The Machine Learning part focuses on Q-learning, a well-known application of reinforcement learning algorithms. The main finding has significant policy implications: decentralization, one of the core strengths of proof-of-work protocols, doesn't necessarily apply to block-level incentives.