We design TimeBoost: a practical transaction ordering policy for rollup sequencers that takes into account both transaction timestamps and bids; it works by creating a score from timestamps and bids, and orders transactions based on this score. TimeBoost is transaction-data-independent (i.e., can work with encrypted transactions) and supports low transaction finalization times similar to a first-come first-serve (FCFS or pure-latency) ordering policy. At the same time, it avoids the inefficient latency competition created by an FCFS policy. It further satisfies useful economic properties of first-price auctions that come with a pure-bidding policy. We show through rigorous economic analyses how TimeBoost allows players to compete on arbitrage opportunities in a way that results in better guarantees compared to both pure-latency and pure-bidding approaches.

One of the first and so far most successful applications of Decentralized Finance (DeFi), financial applications run on permissionless blockchains, are so-called Automated Market Makers (AMMs). They are used to trade cryptocurrencies algorithmically without relying on a custodian or trusted third party. The state of a typical AMM used in DeFi consists of the current inventories of the traded tokens. Trades are made such that some invariant of these inventories is kept constant. Traders who want to exchange tokens of type A for tokens of another type B, add A tokens to the inventory and in return obtain an amount of B tokens from the inventory so that the invariant is maintained. While these Constant Function Market Makers (CFMMs) proved to be very popular and reliable, the construction of invariants to define them seems in many ways ad-hoc and not founded in much theory. In this paper, we fill this gap and propose an axiomatic approach to constructing CFMMs. The approach is, as in any axiomatic theory, to formalize simple principles that are implicitly or explicitly used when constructing trading functions in practice and to check which classes of functions satisfy these principles, beyond those functions already used in practice.

We study conditions for the existence of stable, strategy-proof mechanisms in a many-to-one matching model with discrete salary space (the discrete Kelso-Crawford model). Workers and firms want to match many-to-one and agree on the terms of their match. Firms demand different sets of workers at different salaries. Workers have preferences over different firm-salary combinations. Workers' preferences are monotone in salaries. We show that for this model, a descending auction mechanism is the only candidate for a stable mechanism that is strategy-proof for workers. Moreover, we identify a maximal domain of demand functions for firms, such that the mechanism is stable and strategy-proof.