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Quantifying the Value of Revert Protection

Brian Z. Zhu, Xin Wan, Ciamac C. Moallemi, Dan Robinson, Brad Bachu

TL;DR

The paper studies revert protection as a mechanism that shields users from fees on failed transactions within MEV auctions, and develops a unified, parameterized model that applies to both L1 bundle auctions and L2 priority ordering. It derives closed-form equilibria (including a symmetric mixed-strategy equilibrium) and conducts comparative statics to show how revert penalties on base and priority fees affect auction revenue, market efficiency, and blockspace usage; notably, revenue generally declines with higher base-fee revert penalties and is largely insensitive to priority-fee penalties. The work reveals that revert protection can increase value extraction and efficiency, but introduces spam risks under full protection, prompting design trade-offs. Extensions consider non-negligible implementation costs, analyzing two cost-allocation schemes and showing conditions under which passing costs to searchers or internalizing costs can improve sequencer profits. Overall, the results provide explicit guidance for sequencer design and MEV-tax design by quantifying how revert protection shifts revenue, participation, and mempool dynamics.

Abstract

Revert protection is a feature provided by some blockchain platforms that prevents users from incurring fees for failed transactions. We study the economic implications and benefits of revert protection in the context of priority gas auctions and maximal extractable value. We develop a model in which searchers bid for a top-of-block arbitrage opportunity under varying degrees of revert protection. This model applies to a broad range of settings, including bundle auctions on L1s and priority ordering sequencing rules on L2s. We quantify, in closed form, how revert protection improves equilibrium auction revenue, market efficiency, and blockspace efficiency.

Quantifying the Value of Revert Protection

TL;DR

The paper studies revert protection as a mechanism that shields users from fees on failed transactions within MEV auctions, and develops a unified, parameterized model that applies to both L1 bundle auctions and L2 priority ordering. It derives closed-form equilibria (including a symmetric mixed-strategy equilibrium) and conducts comparative statics to show how revert penalties on base and priority fees affect auction revenue, market efficiency, and blockspace usage; notably, revenue generally declines with higher base-fee revert penalties and is largely insensitive to priority-fee penalties. The work reveals that revert protection can increase value extraction and efficiency, but introduces spam risks under full protection, prompting design trade-offs. Extensions consider non-negligible implementation costs, analyzing two cost-allocation schemes and showing conditions under which passing costs to searchers or internalizing costs can improve sequencer profits. Overall, the results provide explicit guidance for sequencer design and MEV-tax design by quantifying how revert protection shifts revenue, participation, and mempool dynamics.

Abstract

Revert protection is a feature provided by some blockchain platforms that prevents users from incurring fees for failed transactions. We study the economic implications and benefits of revert protection in the context of priority gas auctions and maximal extractable value. We develop a model in which searchers bid for a top-of-block arbitrage opportunity under varying degrees of revert protection. This model applies to a broad range of settings, including bundle auctions on L1s and priority ordering sequencing rules on L2s. We quantify, in closed form, how revert protection improves equilibrium auction revenue, market efficiency, and blockspace efficiency.

Paper Structure

This paper contains 45 sections, 9 theorems, 36 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction and Background
  2. Revert Protection
  3. Block-Building Auctions
  4. Bundle Auctions.
  5. Priority Ordering.
  6. Considering All Cases.
  7. Implementation Costs of Revert Protection
  8. Our Contributions
  9. Literature Review
  10. Model
  11. Auction Description
  12. Strategy Spaces and Payoffs
  13. Payoffs Under Pure Strategies.
  14. Payoffs Under Mixed Strategies.
  15. Equilibrium
  16. ...and 30 more sections

Key Result

theorem 3.1

If $r_1=r_2=0$, then a pure strategy profile $b$ where bids are ordered such that $b_1 \leq b_2 \leq \dots \leq b_N$ (with abstentions represented by bids less than zero) is a Nash equilibrium if and only if $b_{N-1}=b_N=V-g$.

Figures (3)

  • Figure 1: Equilibrium CDF for Priority Fee Bids. We set $V=10$, $g=1$, $N=20$, $r_2=0.1$ for the left plot, and $r_1=0.1$ for the right plot.
  • Figure 2: Equilibrium CDF for Priority Fee Bids (left) and Abstention Probability (right). We set $V=10$, $g=1$, and $r_1=r_2=0.1$ for the left plot.
  • Figure 3: Expected Auction Revenue (left) and Transactions Submitted (right). We set $V=10$ and $g=1$.

Theorems & Definitions (9)

  • theorem 3.1
  • theorem 3.2
  • theorem 3.3
  • theorem 4.1
  • theorem 4.2
  • theorem 5.1
  • theorem 5.2
  • theorem 5.3
  • theorem B.1