MEV Tax Design Overview

• MEV Tax Design is a mechanism in blockchains that allocates and rebalances surplus extractable value using dynamic extraction rates and cooperative game theory.
• It employs a feedback-controlled update process ensuring stability and market liveness by targeting equilibrium participation between validators and users.
• Auction-theoretic and Sybil-proof approaches are integrated to optimize revenue allocation and safeguard against frontrunning and collusion in decentralized markets.

Maximal Extractable Value (MEV) tax design refers to mechanisms within blockchain systems that strategically allocate, collect, or rebalance the surplus MEV generated by transaction ordering, inclusion, and execution. Contemporary research formalizes MEV tax as either a dynamic protocol-level extraction rate (“MEV tax parameterization”) or as algorithmic fee/rebate mechanisms atop cooperative game and auction theory. The principal objective is to balance economic incentives, mitigate harmful externalities, enforce Sybil-robustness, and efficiently partition MEV among validators, users, and public goods—all within the constraints of anonymous, permissionless blockchain settings (Braga et al., 2024, Mazorra et al., 2023, Adadurov et al., 17 Mar 2026).

1. MEV Tax Parameterization and Feedback Control

Modern MEV tax design introduces the MEV extraction rate $\lambda\in[0,1]$ as a first-class protocol variable. Here, $\lambda$ denotes the fraction of a block’s MEV allocated to miners (validators), while $1-\lambda$ is rebated to users. Users and miners possess heterogeneous tolerance to extraction rates, modeled by strictly increasing, differentiable cumulative distribution functions (CDFs) $F$ (users) and $G$ (miners) over $[0,1]$.

The total active user stake is $U\cdot[1-F(\lambda)]$, and the total active miner stake is $M\cdot G(\lambda)$. The protocol designer sets a monetary participation ratio target $T$ such that the equilibrium rate $\lambda^*$ solves

$\lambda$0

A feedback-controlled process, closely mirroring EIP-1559, updates $\lambda$1 after each block:

$\lambda$2

with $\lambda$3 the adjustment strength, $\lambda$4, and $\lambda$5.

This dynamic mechanism provably stabilizes $\lambda$6 near $\lambda$7 for a wide range of parameters, achieves protocol-specified participation splits, and prevents system collapse for appropriate $\lambda$8 (Braga et al., 2024).

2. Fixed Points, Stability, and Regimes of MEV Dynamics

The update map $\lambda$9 has three fixed points: $1-\lambda$0 (no users), $1-\lambda$1 (no miners), and an interior solution $1-\lambda$2.

• Directional Stability: $1-\lambda$3 increases for $1-\lambda$4 and decreases for $1-\lambda$5, guaranteeing that trajectories are drawn toward the target [(Braga et al., 2024), Lemma 3.2].
• Market Liveness: Sufficiently small $1-\lambda$6 ensures neither $1-\lambda$7 nor $1-\lambda$8 is reachable from any $1-\lambda$9. Formally,

$F$0

guarantees non-degeneracy [(Braga et al., 2024), Theorem 3.3].

• Convergence vs. Chaos: For small $F$1, all $F$2. For larger $F$3 (beyond explicit bounds derived from the Taylor expansion of $F$4), period-doubling and Li–Yorke chaos emerge but orbits remain bounded near $F$5 [(Braga et al., 2024), Theorems 3.4 & 3.6].

3. Cooperative-Game Approaches: Rebates, Sybil-Proofness, and Taxes

MEV tax mechanisms can equivalently be modeled as rebate mechanisms, assigning each agent’s bundle a value based on the outcome of a characteristic function $F$6, where $F$7 is the set of transaction agents, and $F$8 quantifies the extractable surplus from any subset $F$9.

Several desiderata are formalized:

• Efficiency (E): Full allocation of surplus.
• Symmetry (S), Null-player (N), Additivity (A), Marginality (M), Strong monotonicity (SM), No-deficit (ND), Sybil-proofness (SP), Separability ($G$0-SE).

The Shapley value uniquely satisfies (E,S,N,A) but is not Sybil-proof: agents can split themselves to increase rebates. For Sybil-robustness, rebates must be shrunk. The Banzhaf index, as an additive Sybil-proof optimal (SPO) operator, and a non-additive operator $G$1 attain worst-case optimality for SP and ND constraints, albeit with strictly bounded user welfare [(Mazorra et al., 2023), Theorems 3.2, 3.6, 3.10].

The translation from rebates to MEV tax is direct: tax $G$2, with the builder retaining the difference. Protocols can flexibly adopt Shapley-style, Banzhaf, or prior-optimal Sybil-proof taxes depending on application-specific Sybil/collusion risk (Mazorra et al., 2023).

4. Auction-Theoretic MEV Tax: Empirical Realities and Mechanism Choice

MEV auction studies establish that the per-bundle MEV distribution is right-skewed, with log-transformed values approximately normal: $G$3. The top 1% of bundles account for 68% of total MEV, and extracted-value markets display high concentration ($G$4) (Adadurov et al., 17 Mar 2026).

Competing searchers’ valuations are affiliated through common Gaussian factors ($G$5), violating independent-value assumptions and breaking conventional revenue-equivalence results. In this context:

• Second-Price Sealed-Bid (SPSB)/English auctions dominate in expected revenue over First-Price or Dutch auctions as soon as $G$6 (“linkage principle”).
• Revenue Uplifts: For moderate affiliation ($G$7, $G$8), linkage gaps for SPSB/English over FPSB/Dutch reach 14–28%; for small $G$9 (liquidation/arbitrage MEV), up to 30%. Aggregate annual impact at observed volumes: \$10–18 million.
• Mechanism Recommendation: Deploy SPSB/English auctions for MEV types with moderate $[0,1]$0 and $[0,1]$1 (liquidations, backruns); use Dutch for high-$[0,1]$2 (sandwich) markets where the revenue gap closes. All-Pay auctions are suboptimal under affiliation due to severe bid-shading (Adadurov et al., 17 Mar 2026).

5. Protocol Design Guidelines and Practical Implementation

Effective MEV tax mechanisms must coordinate stabilization, responsiveness, and robustness:

• Parameter Selection: Choose $[0,1]$3 to achieve the target equilibrium split; solve for $[0,1]$4 via the participation curve. Select $[0,1]$5 to balance convergence speed with mitigation of oscillation or collapse, always observing the explicit liveness and convergence thresholds (Braga et al., 2024).
• Sybil-Proofing Tradeoff: Sybil-robust mechanisms (e.g., $[0,1]$6 or prior-optimized variants) inherently constrain attainable user welfare to at most $[0,1]$7 of the total surplus, as shown via tight bounds (Mazorra et al., 2023).
• Simulation and Stress-Test: Empirical calibration with plausible tolerance distributions (e.g., Beta laws) is critical to confirm that orbit bands around $[0,1]$8 are sufficiently narrow and that parameters maintain market liveness (Braga et al., 2024).
• Auction Segmentation: Segment MEV categories by empirical ($[0,1]$9), tailoring the auction format and extraction logic accordingly for maximal protocol-side MEV capture (Adadurov et al., 17 Mar 2026).

6. Welfare Bounds, Economic Impacts, and Theoretical Limits

The equilibrium participation ratio at $U\cdot[1-F(\lambda)]$0 achieves the desired user:miner split. Welfare losses from periodic/chaotic regimes remain $U\cdot[1-F(\lambda)]$1 and are explicitly bounded for known tolerance laws. No mechanism can simultaneously guarantee efficiency, symmetry, no-deficit, and Sybil-proofness; the resulting trilemma is resolved by sacrificing full efficiency in exchange for strategic robustness (Mazorra et al., 2023).

Implemented MEV taxes:

• Internalize externalities by imposing charges proportional to agents’ marginal contribution to MEV.
• Deter frontrunning and Sybil exploits via inherent mechanism design properties.
• Distribute MEV revenues to builders, users, and public-goods budgets according to policy-adjustable operator choice.
• Allow governance flexibility: the choice of $U\cdot[1-F(\lambda)]$2 (e.g., $U\cdot[1-F(\lambda)]$3 vs. Banzhaf) may be dynamically adjusted with evolving risk preference and observed market conditions.

7. Empirical Illustrations and Stress-Tested Dynamics

Numerical bifurcation analyses reveal three dynamic regimes depending on $U\cdot[1-F(\lambda)]$4:

• $U\cdot[1-F(\lambda)]$5: Clean convergence to $U\cdot[1-F(\lambda)]$6.
• $U\cdot[1-F(\lambda)]$7: Chaotic or periodic oscillations, but always within a tight band around $U\cdot[1-F(\lambda)]$8.
• $U\cdot[1-F(\lambda)]$9: System collapse, with $M\cdot G(\lambda)$0 falling to $M\cdot G(\lambda)$1 or $M\cdot G(\lambda)$2 (market shutdown).

Deviations from the target remain tightly bounded until $M\cdot G(\lambda)$3 approaches the liveness boundary. Stress-testing—including adversarial parameter shifts and abrupt regime changes—demonstrates that the dynamic MEV tax approach prevents degeneration under the prescribed operating regime (Braga et al., 2024).

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In sum, MEV tax design encompasses a rigorous toolbox: dynamic protocol-level extraction rates, Sybil-proof cooperative-game-based taxes, and auction-theoretic format selection. These enable blockchain systems to enforce robust participation splits, maximize welfare subject to strategic constraints, and adapt to empirical MEV market structures (Braga et al., 2024, Mazorra et al., 2023, Adadurov et al., 17 Mar 2026).