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Mitigating the Risks of Maximal Extractable Value in Blockchain Ecosystems

Published on Wed Jul 12 2023Cryptocurrency Tokens | Bybit on Flickr Cryptocurrency Tokens | Bybit on Flickr

A new preprint paper titled "Towards Optimal Prior-Free Permissionless Rebate Mechanisms, with applications to Automated Market Makers & Combinatorial Orderflow Auctions" delves into the pressing issue of Maximal Extractable Value (MEV) in blockchain ecosystems. MEV refers to the ability of validators or block proposers to exploit users' transactions for personal gain. The paper aims to address this problem by proposing a formal approach to determine fair compensation for users whose transactions are executed in bundles, rather than individually. By exploring the concept of MEV rebates and auctions, the researchers seek to undermine the power of block producers and create a more equitable system for users.

Highlights from the Paper:

The paper begins by examining the role of liquidity providers (LP) in Constant Function Market Makers (CFMM). LPs contribute value to the system through their liquidity provision, which generates a premium for the entire market. However, LPs often face adverse selection issues and are not adequately compensated for their contributions. The researchers propose introducing MEV rebates to address this disparity and incentivize LPs.

Various solutions to address MEV extraction in Automated Market Maker (AMM) transactions have been proposed, including preserving transaction privacy, auctioning the right to execute transactions, and ordering transactions based on timestamps. The authors discuss the concept of Order Flow Auctions (OFA), where users benefit from the value they generate by participating in auctions for the right to execute orders.

The paper explores the limitations of existing mechanisms and proposes a new prior-free optimal rebate mechanism. Using the Shapley value as a benchmark, the researchers demonstrate that it is weak against Sybil strategies, which refers to the manipulation of identities. They present an algorithm for determining the optimal Sybil strategy against this mechanism.

Additionally, the authors analyze the limitations of symmetric Sybil-proof mechanisms for liquidity provision in CFMM. They find that such mechanisms face intrinsic limitations in the prior-free setting, where the value that can be rebated decreases exponentially. However, they propose a cooperative utility game solution in the Bayesian setting that overcomes these limitations.

Value and Implications:

The findings of this paper have significant implications for blockchain ecosystems and their users. By introducing MEV rebates and auction mechanisms, the researchers aim to create a fairer system where users receive compensation for their contributions. This not only addresses the issue of MEV extraction but also provides better incentives for liquidity providers and encourages the growth of decentralized finance platforms.

Furthermore, the paper highlights the limitations of existing mechanisms, emphasizing the need for innovative solutions to safeguard against Sybil strategies and ensure fair compensation. The proposed prior-free optimal rebate mechanism offers a promising approach to address the challenges associated with rebating bundled transactions.

While this research provides valuable insights into rebates and the allocation of MEV, there are still open questions that need further exploration. Future work should focus on finding Pareto-optimal operators with desirable properties and analyzing Sybil-proof mechanisms in settings with interdependent valuations and preferences in the order of execution.

In conclusion, this paper represents an important step towards optimizing permissionless rebate mechanisms and mitigating the risks associated with Maximal Extractable Value in blockchain ecosystems. By developing fair and efficient mechanisms, the researchers aim to create a more inclusive and transparent environment for all users.

Tags: Computer Science | Economics | Economics:Finance

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