Playing the MEV Game on a First-Come-First-Served Blockchain: Conclusion and References

Table of Links

Abstract and 1. Introduction

2. Background
3. Related Work
4. MEV Discovery
5. MEV Extraction
6. Conclusion and References

6 Conclusion

This paper provides a two-staged approach for MEV searching on Algorand, an FCFS blockchain. Initially, we propose an algorithm for arbitrage detection and evaluate its performance using historical block state data, taking into account the time constraints of Algorand. Our findings indicate that although significant state updates between blocks are infrequent, MEV searchers on Algorand tend to close arbitrage opportunities within the same block they arise. Therefore, to be competitive, MEV opportunity detection should occur at the transaction level in the mempool rather than at the block state level as initially attempted. We also show the impact of available algorithm runtime and arbitrage selection strategy on the discovered revenue.

Subsequently, we perform experiments on a private Algorand network to identify key factors in transaction prioritization within an FCFS framework. Our results highlight the importance of minimizing latency, particularly in connections to relays with well-connected links to multiple highstaked nodes.

Looking ahead, we plan to refine our arbitrage detection algorithm by considering a broader set of pools and applying it directly at the network level. Additionally, we aim to extend our network experiments to the Algorand mainnet to explore potential latency correlations between high-staked block proposers and successful MEV searchers. This future research will enhance our understanding of MEV dynamics in FCFS blockchains like Algorand.

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