Crypto marketplaces must trade off latency and fairness: ultra-low latency benefits some traders but creates an arms race that disadvantages slower participants. Empirical and theoretical work suggests that frequent batch auctions and carefully designed micro-batching or randomized matching best rebalance that trade-off without destroying price discovery.
Mechanisms and evidence
Economists including Eric Budish University of Chicago and Peter Cramton University of Maryland have advocated frequent batch auctions as a market-design response that replaces continuous first-come priority with discrete clearing intervals. This approach compresses the advantage of tiny speed differentials by matching orders in synchronized batches, preserving price-time priority within each batch while reducing incentives to invest in ever-faster connectivity. In practice, industry innovation such as the Investors Exchange led by Brad Katsuyama has shown that small, deterministic processing delays or "speed bumps" can materially reduce latency arms races and protect institutional order flow, illustrating an operational path between pure continuous matching and full batching.
Trade-offs, causes, and consequences
The central cause of unfair latency races is the continuous limit order book combined with strict price-time priority, which rewards microsecond advantages. Moving to micro-batching or randomized tie-breaking mitigates that reward structure. However, micro-batching can increase effective execution latency and temporarily widen spreads in very thin markets, and randomized matching introduces probabilistic outcomes that some liquidity providers find less predictable. For crypto markets, which are global and operate 24/7, these trade-offs play out across jurisdictions and participant types: retail traders, institutional funds, and automated market makers.
Culturally and environmentally, decentralized exchanges and on-chain protocols already experiment with alternative designs. Automated market makers such as Uniswap remove traditional order matching entirely, while on-chain batch settlement systems developed by teams at Gnosis implement periodic clearing that echoes academic recommendations. Those designs reduce the need for specialized low-latency infrastructure but shift complexity into smart-contract logic and gas-cost dynamics.
Balancing latency and fairness therefore favors hybrid solutions: short, frequent batch intervals or randomized execution windows combined with transparent rules and reliable time-stamping. Such designs lower the social cost of arms races, improve perceived fairness across participant groups, and retain much of the price discovery power of continuous markets, while requiring careful calibration to avoid harming liquidity or fragmenting order flow. The optimal choice depends on market depth, participant composition, and the technical constraints of the trading venue.