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Although Bitcoin was intended to be a decentralized digital currency, in practice, mining power is quite concentrated. This fact is a persistent source of concern for the Bitcoin community. We provide an explanation using a simple model to capture miners' incentives to invest in equipment. In our model, n miners compete for a prize of fixed size. Each miner chooses an investment q_i, incurring cost c_iq_i, and then receives reward q^{\alpha}∑_j q_j^{\alpha}, for some \alpha≥1. When c_i = c+j for all i,j, and α=1, there is a unique equilibrium where all miners invest equally. However, we prove that under seemingly mild deviations from this model, equilibrium outcomes become drastically more centralized. In particular, (a) When costs are asymmetric, if miner i chooses to invest, then miner j has market share at least 1−c_j/c_i. That is, if miner j has costs that are (e.g.) 20% lower than those of miner i, then miner j must control at least 20% of the \emph{total} mining power. (b) In the presence of economies of scale (α>1), every market participant has a market share of at least 1−1/α, implying that the market features at most α/(α−1) miners in total. We discuss the implications of our results for the future design of cryptocurrencies. In particular, our work further motivates the study of protocols that minimize "orphaned" blocks, proof-of-stake protocols, and incentive compatible protocols. 

We study the menu complexity of optimal and approximately-optimal auctions in the context of the ``FedEx'' problem, a so-called ``one-and-a-half-dimensional'' setting where a single bidder has both a value and a deadline for receiving an item [FGKK 16]. The menu complexity of an auction is equal to the number of distinct (allocation, price) pairs that a bidder might receive [HN 13]. We show the following when the bidder has $n$ possible deadlines: 1) Exponential menu complexity is necessary to be exactly optimal: There exist instances where the optimal mechanism has menu complexity $\geq 2^n-1$. This matches exactly the upper bound provided by Fiat et al.'s algorithm, and resolves one of their open questions [FGKK 16]. 2) Fully polynomial menu complexity is necessary and sufficient for approximation: For all instances, there exists a mechanism guaranteeing a multiplicative $(1-\epsilon)$-approximation to the optimal revenue with menu complexity $O(n^{3/2}\sqrt{\frac{\min\{n/\epsilon,\ln(v_{\max})\}}{\epsilon}}) = O(n^2/\epsilon)$, where $v_{\max}$ denotes the largest value in the support of integral distributions. \item There exist instances where any mechanism guaranteeing a multiplicative $(1-O(1/n^2))$-approximation to the optimal revenue requires menu complexity $\Omega(n^2)$. Our main technique is the polygon approximation of concave functions [Rote 91], and our results here should be of independent interest. We further show how our techniques can be used to resolve an open question of [DW 17] on the menu complexity of optimal auctions for a budget-constrained buyer.