Paper: arXiv 2603.16333

Authors: Aleksei Adadurov, Sergey Barseghyan, Anton Chtepine, Antero Eloranta, Andrei Sebyakin, Arsenii Valitov

Abstract

We study optimal auction design for Maximum Extractable Value (MEV) auction markets on Ethereum. Using a dataset of 2.2 million transactions across three major orderflow providers, we establish three empirical regularities: extracted values follow a log-normal distribution with extreme right-tail concentration, competition intensity varies substantially across MEV types, and the standard Revenue Equivalence Theorem breaks down due to affiliation among searchers’ valuations. We model this affiliation through a Gaussian common factor, deriving equilibrium bidding strategies and expected revenues for five auction formats, first-price sealed-bid, second-price sealed-bid, English, Dutch, and all-pay, across a fine grid of bidder counts $n$ and affiliation parameters $ρ$. Our simulations confirm the Milgrom-Weber linkage principle: English and second-price sealed-bid auctions strictly dominate Dutch and first-price sealed-bid formats for any $ρ> 0$, with a linkage gap of 14-28% at moderate affiliation ($ρ=0.5$) and up to 30% for small bidder counts. Applied to observed bribe totals, this gap corresponds to $10-18 million in foregone revenue over the sample period. We also document a novel non-monotonicity: at large $n$ and high $ρ$, revenue peaks in the interior of the affiliation parameter space and declines thereafter, as near-perfect correlation collapses the order-statistic spread that drives competitive payments.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper combines theoretical auction design—modeling bidder valuation affiliation through Gaussian common factors—with extensive empirical analysis on 2.2 million Ethereum transactions. It provides a robust critique of existing MEV auction mechanisms and quantifies millions of dollars in foregone revenue due to sub-optimal auction choices.

Research Flowchart

  flowchart TD
    A[Goal: Optimal MEV Auction Design & Revenue Maximization] --> B[Data Input: 2.2M Ethereum Txns across 3 Orderflow Providers]
    B --> C[Empirical Analysis: Log-normal values, variable competition, non-equivalence]
    C --> D[Game-Theoretic Modeling: 5 Auction Formats with Gaussian Affiliation Parameter ρ]
    D --> E[Simulations: Equilibrium Bidding across Grid of Bidders n & Affiliation ρ]
    E --> F[Findings: English/2nd-Price Dominate 1st-Price/Dutch via Linkage Principle]
    E --> G[Outcomes: $10-18M Foregone Revenue & Non-Monotonic Revenue Peak at High n, ρ]