Bruno Mazorra

Burak Öz, Fei Wu, Luis Correia et al.

Decentralized block building mechanisms replace the monopoly of a single proposer with multiple builders. However, their censorship-resistance and fair-access benefits depend not only on the number of builders, but also on whether builders are geographically positioned to provide timely transaction coverage. We study this tension between builder location choice, user transaction coverage, and reward concentration by modeling decentralized block building as a stochastic coverage game. Builders choose regions, information sources emit transactions over a block construction round, and latency determines whether each transaction is received before the deadline. We show that the builder region game is an exact potential game and therefore admits a pure Nash equilibrium. We prove an asymptotically tight factor-2 Price of Anarchy bound, quantifying the price of decentralization from uncoordinated builder placement, and derive tight bounds on builder utility concentration, showing that the lowest-utility builder earns at least half of the highest-utility builder's payoff, and the utility-share HHI is at most 12.5% above the egalitarian benchmark. We complement the theory with simulations, studying the builder region game under richer latency and source environments. We find that welfare losses are most pronounced in intermediate regimes where peripheral sources are reachable and valuable, but selfish incentives still favor regions with strong access to high-value sources. We also find that geographic and utility concentration need not align: planner allocations can improve coverage by assigning builders to lower-payoff peripheral regions, while equilibrium outcomes can be more geographically concentrated but more utility-balanced. We connect our findings to protocol design and discuss future directions on location-market modeling and alternative reward-sharing rules.

Bruno Mazorra, Victor Adan, Vanesa Daza

Uniswap, like other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also makes it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already existed in traditional finance but has become more relevant in DeFi. Various projects such as [34,37] have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made in [44]. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their data set by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in Uniswap protocol. We propose various machine-learning-based algorithms with new relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained an accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver.