Simulation modeling of the maximal extractable value in Ethereum blockchain network
Abstract
Proposed a simulation environment for modeling the phenomenon of Maximal Extractable Value (MEV) with implementation in the Python programming language using the Gymnasium library. The environment reproduces the interaction between the mempool, the block builder, the MEV extractor agent, and the automated market maker (AMM) pool of a decentralized exchange. Formally, the environment is described as an extended partially observable decision-making process in which the agent interacts with a discrete-time episodic model reflecting the sequence of transaction arrivals, block construction, and the execution of swap operations on a decentralized cryptocurrency exchange. Reinforcement learning methods are used to model the adaptive behavior of the agent, while a counterfactual evaluation approach is applied to quantitatively analyze user losses by comparing transaction execution outcomes under different ordering regimes while keeping the input conditions unchanged. The study employs a previously proposed method for mitigating the negative effects of MEV extraction based on Lamport logical timestamps. This method implements local causal transaction ordering within an individual smart contract without modifying the global consensus mechanism of the Ethereum blockchain network. To assess the practical effectiveness of this approach, three simulation scenarios are defined: a baseline scenario without systematic MEV attacks to estimate the overhead associated with the protection mechanism, a systematic sandwich-attack scenario to analyze the methods ability to reduce user losses and constrain the capabilities of the MEV extractor, and a parametric analysis scenario aimed at investigating the trade-off between the level of protection and the "cost" of its application. The obtained results show that the proposed MEV-protected ordering method can reduce user price losses caused by sandwich attacks while also affecting transaction rejection rates and the associated fee expenditures, indicating the presence of a controllable trade-off between protection effectiveness and the overhead of its use. The practical value of the work lies in the creation of a reproducible simulation environment for studying the strategic behavior of MEV agents and evaluating mechanisms for mitigating the negative effects of MEV under controlled conditions. Such an environment can be used for further security analysis of decentralized finance protocols and for the design of new transaction ordering methods.
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