PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 16, 2025Analytics3 citationsOpen Access

Game-Theoretic Analysis of MEV Attacks and Mitigation Strategies in Decentralized Finance

View Full Paper
BABenjamin AppiahDCDaniel CommeyWBWinful Bagyl-Bac

Key Points

  • The analysis shows that current MEV market dynamics resemble Bertrand-style competition, leading to harmful outcomes.
  • Nash equilibria for primary attack vectors, including sandwich attacks, have been derived, characterizing attacker behavior.
  • Mechanism design solutions, such as commit-reveal schemes, are proposed and evaluated to reduce MEV's negative impact.
  • Empirical validation against Ethereum on-chain data indicates theoretical models align well with observed market behavior.

Abstract

Maximal Extractable Value (MEV) presents a significant challenge to the fairness and efficiency of decentralized finance (DeFi). This paper provides a game-theoretic analysis of the strategic interactions within the MEV supply chain, involving searchers, builders, and validators. A three-stage game of incomplete information is developed to model these interactions. The analysis derives the Perfect Bayesian Nash Equilibria for primary MEV attack vectors, such as sandwich attacks, and formally characterizes attacker behavior. The research demonstrates that the competitive dynamics of the current MEV market are best described as Bertrand-style competition, which compels rational actors to engage in aggressive extraction that reduces overall system welfare in a prisoner’s dilemma-like outcome. To address these issues, the paper proposes and evaluates mechanism design solutions, including commit–reveal schemes and threshold encryption. The potential of these solutions to mitigate harmful MEV is quantified. Theoretical models are validated against on-chain data from the Ethereum blockchain, showing a close alignment between theoretical predictions and empirically observed market behavior.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Appiah et al. (2025) studied this question.

synapsesocial.com/papers/68d453a431b076d99fa59975https://doi.org/10.3390/analytics4030023
Ask AI
Helpful
Bookmark
Share
View Full Paper