MEV Tax Parameterization

• MEV tax parameterization is a framework that dynamically calibrates the extraction rate to share blockchain surplus between block producers and users.
• It employs logistic update rules and stability thresholds to maintain a safe extraction rate, preventing market collapse and ensuring protocol liveness.
• The method integrates cooperative game theory and auction-based models to promote fairness, incentive compatibility, and resistance to Sybil attacks.

Maximal Extractable Value (MEV) tax parameterization is the rigorous design and calibration of mechanisms for sharing MEV revenue between block producers and protocol users in blockchain systems. The formal parameterization problem centers on the selection and updating of the MEV extraction rate—the protocol-level fraction of extractable value allocated to block producers—and the rules for its redistribution, often via rebate mechanisms. This area integrates dynamic protocol design, cooperative game theory, and auction theory, with objectives including incentive compatibility, protocol liveness, fairness, Sybil-resistance, and optimal welfare allocation (Braga et al., 2024, Mazorra et al., 2023).

1. Formal Model of MEV Sharing and Mechanism Design

The MEV tax parameterization framework addresses settings where a blockchain protocol must specify how the surplus from MEV—value obtainable by optimizing transaction inclusion or ordering in a block—is divided between participants. The principal objects are:

• A set of users/players $N$ submitting transaction bundles $\{B_i\}$; the protocol’s state $st$ evolves with the application of sequences of these bundles via $st \circ B$.
• Each user $i$ has utility $u_i(st)$, and block producers (“builders,” “sequencers”) $P$ can select and order bundles from a candidate set $\mathcal{C}_P$ to maximize their own surplus.
• Total MEV for a state $st$ and bundle set $\mathcal{P}=\{B_1,\ldots,B_n\}$ is $\{B_i\}$0.
• MEV tax mechanisms extract a protocol-defined fraction, the “MEV extraction rate,” denoted $\{B_i\}$1 ($\{B_i\}$2 means producers retain all MEV, $\{B_i\}$3 means none).

The cooperative game perspective is used to define value allocation: for $\{B_i\}$4, $\{B_i\}$5 denotes the extractable value when only bundles $\{B_i\}$6 are available (Mazorra et al., 2023).

2. Dynamic Parameterization: Update Rules and Stability

A central innovation is treating the MEV extraction rate $\{B_i\}$7 as a protocol parameter, updated dynamically block-by-block using observed participation data. The protocol observes a market outcome,

$\{B_i\}$8

where $\{B_i\}$9 and $st$0 are cumulative distribution functions (CDFs) of users’ and miners’ $st$1-tolerances, $st$2, and $st$3 encodes the protocol’s desired ratio $st$4 of user- to miner-stake (Braga et al., 2024).

The protocol updates $st$5 by a logistic step: $st$6 with step size $st$7, where $st$8 is a designer parameter. The system evolves as $st$9. This yields a unique interior fixed point $st \circ B$0 defined by $st \circ B$1.

The stability of $st \circ B$2 is directional: For $st \circ B$3, $st \circ B$4 (pushing up toward $st \circ B$5); for $st \circ B$6, $st \circ B$7 (pushing down). The endpoints $st \circ B$8 are “collapse” states—either users or miners withdraw (Braga et al., 2024).

3. Liveness, Convergence, and Chaotic Dynamics

Ensuring liveness—that $st \circ B$9 never exits $i$0—is critical. The protocol achieves this by bounding $i$1: $i$2 For $i$3 below this threshold, $i$4 stays in the open interval $i$5 for all $i$6 starting from any non-degenerate state, preventing market collapse (Braga et al., 2024).

Convergence to $i$7 occurs for sufficiently small $i$8; specifically, for

$i$9

where $u_i(st)$0 is finite and continuous (Braga et al., 2024). For larger $u_i(st)$1, the system displays period-doubling bifurcations, high-order periodic orbits, and Li–Yorke chaos. However, even in the presence of periodic/chaotic dynamics (for $u_i(st)$2 below the liveness threshold), deviations from $u_i(st)$3 remain bounded: $u_i(st)$4 which ensures practical robustness (Braga et al., 2024).

4. Parameter Calibration and Deployment Considerations

Protocol designers must:

• Determine the target $u_i(st)$5 (solving $u_i(st)$6 for a given $u_i(st)$7).
• Compute liveness and convergence thresholds for $u_i(st)$8.
• Select $u_i(st)$9 just below the convergence bound for rapid, monotonic convergence, or in the periodic regime (below liveness) for greater sensitivity at the expense of bounded oscillations.
• Calibrate $P$0 on empirical transaction data.
• Monitor $P$1 in production; auto-tune (reduce) $P$2 if collapse is threatened.

A typical “best-practice” involves initial simulation with stress scenarios, setting $P$3 to balance speed and stability, and real-time adaptation in deployment (Braga et al., 2024).

Parameter	Role	Calibration Guidance
$P$4	Target extraction rate	Solve $P$5
$P$6	Target user/miner participation ratio	$P$7
$P$8	Update step size	$P$9

5. Tax and Rebate Operators: Static Sharing Approaches

Orthogonal to dynamic adjustment, protocols may implement static MEV tax-and-rebate mechanisms:

• A tax fraction $\mathcal{C}_P$0 of total MEV $\mathcal{C}_P$1 is withheld and rebated to users by an operator $\mathcal{C}_P$2.
• The Shapley value offers a canonical rebate formula:

$\mathcal{C}_P$3

ensuring efficiency, symmetry, and proportionality properties (Mazorra et al., 2023).

• However, Shapley rebates are not Sybil-proof. Sybil-robust mechanisms restrict $\mathcal{C}_P$4 and potentially modify $\mathcal{C}_P$5 to prevent manipulation; for $\mathcal{C}_P$6 users, the prior-free, worst-case optimal $\mathcal{C}_P$7 (Mazorra et al., 2023).

In practical applications, such as CFMM liquidity rebates or combinatorial order-flow auctions, careful parameterization of $\mathcal{C}_P$8 and $\mathcal{C}_P$9 is required to balance builder revenue, user welfare, incentive compatibility, and resistance to strategic splitting.

6. Auction-Based and Prior-Free Variants

MEV tax parameterization extends to settings with auctions for MEV rights:

• Users submit bundles and bids; allocation maximizes combined user bids and a tax on MEV.
• For “max-min” mechanisms, any rebate operator $st$0 (the space of symmetric, strongly-monotonic, Sybil-proof, no-deficit operators) can be implemented, guaranteeing Sybil-resistance and budget-balance (Mazorra et al., 2023).
• In cases with conflicts, the auctioned set $st$1 is determined by $st$2, and rebates are distributed accordingly.

Optimization objectives include maximizing user plus builder welfare or builder revenue. Mechanism parameterization involves solving optimization or linear programming problems depending on available prior knowledge about participant behavior (Mazorra et al., 2023).

7. Conclusion and Best-Practice Synthesis

MEV tax parameterization systematically determines the rules, targets, and update procedures for MEV sharing in blockchain protocols. Key findings and guidelines include:

• Dynamic mechanisms ensure robust, near-optimal long-term performance under wide conditions, even amid complex periodic or chaotic updating behavior, provided liveness thresholds are respected (Braga et al., 2024).
• Static tax-and-rebate structures require careful balancing of fairness, incentive compatibility, and Sybil-proofness, often necessitating sacrifices in maximal fairness for strategic robustness (Mazorra et al., 2023).
• Empirically informed calibration, continuous monitoring, and adaptability to exogenous participation shocks are essential for safe deployment.

This combination of dynamic and static parameterization constitutes a comprehensive toolkit for MEV sharing mechanism design, enabling trade-offs between user and builder welfare, protocol stability, and strategic resistance in decentralized systems (Braga et al., 2024, Mazorra et al., 2023).