Satoshi Nakamoto (2008) invented a new kind of economic system that does not need the support of government or rule of law. Trust and security instead arise from a combination of cryptography and economic incentives, all in a completely anonymous and decentralized system. This article shows that Nakamoto’s novel form of trust, while undeniably ingenious, is deeply economically limited. The core argument is three equations. A zero-profit condition on the quantity of honest blockchain “trust support” (work, stake, etc.) and an incentive-compatibility condition on the system’s security against majority attack (the Achilles heel of all forms of permissionless consensus) together imply an equilibrium constraint, which says that the “flow” cost of blockchain trust has to be large at all times relative to the benefits of attacking the system. This is extremely expensive relative to traditional forms of trust and scales linearly with the value of attack. In scenarios that represent Nakamoto trust becoming a more significant part of the global financial system, the cost of trust would exceed global GDP. Nakamoto trust would become more attractive if an attacker lost the stock value of their capital in addition to paying the flow cost of attack, but this requires either collapse of the system (hardly reassuring) or external support from rule of law. The key difference between Nakamoto trust and traditional trust grounded in rule of law and complementary sources, such as reputations, relationships, and collateral, is economies of scale: society or a firm pays a fixed cost to enjoy trust over a large quantity of economic activity at low or zero marginal cost.

This paper proposes a new mechanism for combinatorial assignment—for example, assigning schedules of courses to students—based on an approximation to competitive equilibrium from equal incomes (CEEI) in which incomes are unequal but arbitrarily close together. The main technical result is an existence theorem for approximate CEEI. The mechanism is approximately efficient, satisfies two new criteria of outcome fairness, and is strategyproof in large markets. Its performance is explored on real data, and it is compared to alternatives from theory and practice: all other known mechanisms are either unfair ex post or manipulable even in large markets, and most are both manipulable and unfair.

We propose a criterion of approximate incentive compatibility, strategy-proofness in the large (SP-L), and argue that it is a useful second-best to exact strategy-proofness (SP) for market design. Conceptually, SP-L requires that an agent who regards a mechanism’s “prices” as exogenous to her report—be they traditional prices as in an auction mechanism, or price-like statistics in an assignment or matching mechanism—has a dominant strategy to report truthfully. Mathematically, SP-L weakens SP in two ways: (1) truth-telling is required to be approximately optimal (within epsilon in a large enough market) rather than exactly optimal, and (2) incentive compatibility is evaluated ex interim, with respect to all full-support i.i.d. probability distributions of play, rather than ex post with respect to all possible realizations of play. This places SP-L in between the traditional notion of approximate SP, which evaluates incentives to manipulate ex post and as a result is too strong to obtain our main results in support of SP-L, and the traditional notion of approximate Bayes-Nash incentive compatibility, which, like SP-L, evaluates incentives to manipulate ex interim, but which imposes common knowledge and strategic sophistication assumptions that are often viewed as unrealistic.

We use theory and field data to study the draft mechanism used to allocate courses at Harvard Business School. We show that the draft is manipulable in theory, manipulated in practice, and that these manipulations cause significant welfare loss. Nevertheless, we find that welfare is higher than under its widely studied strategyproof alternative. We identify a new link between fairness and welfare that explains why the draft performs well despite the costs of strategic behavior, and then design a new draft that reduces these costs. We draw several broader lessons for market design, regarding Pareto efficiency, fairness, and strategyproofness.

Will stock exchanges innovate to address latency arbitrage and the arms race for speed? This paper models how exchanges compete in the modern electronic era and how this shapes incentives for market-design innovation. In the status quo, exchange trading fees are competitive, but exchanges earn economic rents from selling speed. These rents create a wedge between private and social incentives to innovate and support the persistence of an inefficient market design in equilibrium of a market-design adoption game. We discuss implications for policy and insights for the literatures on market design, innovation, and platforms.

The high-frequency trading arms race is a symptom of flawed market design. Instead of the continuous limit order book market design that is currently predominant, we argue that financial exchanges should use frequent batch auctions: uniform price double auctions conducted, for example, every tenth of a second. That is, time should be treated as discrete instead of continuous, and orders should be processed in a batch auction instead of serially. Our argument has three parts. First, we use millisecond-level direct-feed data from exchanges to document a series of stylized facts about how the continuous market works at high-frequency time horizons: (i) correlations completely break down; which (ii) leads to obvious mechanical arbitrage opportunities; and (iii) competition has not affected the size or frequency of the arbitrage opportunities, it has only raised the bar for how fast one has to be to capture them. Second, we introduce a simple theory model which is motivated by and helps explain the empirical facts. The key insight is that obvious mechanical arbitrage opportunities, like those observed in the data, are built into the market design—continuous-time serial-processing implies that even symmetrically observed public information creates arbitrage rents. These rents harm liquidity provision and induce a never-ending socially wasteful arms race for speed. Last, we show that frequent batch auctions directly address the flaws of the continuous limit order book. Discrete time reduces the value of tiny speed advantages, and the auction transforms competition on speed into competition on price. Consequently, frequent batch auctions eliminate the mechanical arbitrage rents, enhance liquidity for investors, and stop the high-frequency trading arms race.

Our recent research (Budish, Cramton, and Shim 2013) proposes frequent batch auctions - uniform-price sealed-bid double auctions conducted at frequent but discrete time intervals - as a market design alternative to continuous-time trading in financial markets. This short paper discusses the implementation details of frequent batch auctions. We outline the process flow for frequent batch auctions, discuss a modification to the market design that accommodates market fragmentation and Reg NMS, and discuss the engineering and economic considerations relevant for determining the batch interval. Open questions are discussed throughout.

A well-developed theoretical literature - dating back at least to Nordhaus (1969) - has analyzed optimal patent policy design. We re-present the core trade-off of the Nordhaus model and highlight an empirical question which emerges from the Nordhaus framework as a key input into optimal patent policy design: namely, what is the elasticity of R&D investment with respect to the patent term? We then review the - surprisingly small - body of empirical evidence that has been developed on this question over the nearly half century since the publication of Nordhaus's book.

We investigate whether private research investments are distorted away from long-term projects. Our theoretical model highlights two potential sources of this distortion: short-termism and the fixed patent term. Our empirical context is cancer research, where clinical trials--and hence, project durations--are shorter for late-stage cancer treatments relative to early-stage treatments or cancer prevention. Using newly constructed data, we document several sources of evidence that together show private research investments are distorted away from long-term projects. The value of life-years at stake appears large. We analyze three potential policy responses: surrogate (non-mortality) clinical-trial endpoints, targeted R&D subsidies, and patent design.

We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The key difference between message data and widely familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency arbitrage races are very frequent (about one per minute per symbol for FTSE 100 stocks), extremely fast (the modal race lasts 5–10 millionths of a second), and account for a remarkably large portion of overall trading volume (about 20%). Race participation is concentrated, with the top six firms accounting for over 80% of all race wins and losses. The average race is worth just a small amount (about half a price tick), but because of the large volumes the stakes add up. Our main estimates suggest that races constitute roughly one-third of price impact and the effective spread (key microstructure measures of the cost of liquidity), that latency arbitrage imposes a roughly 0.5 basis point tax on trading, that market designs that eliminate latency arbitrage would reduce the market’s cost of liquidity by 17%, and that the total sums at stake are on the order of $5 billion per year in global equity markets alone.