BuilderNet: Fair Block Production in Ethereum

• BuilderNet is a set of mechanisms designed to redistribute orderflow and minimize latency disparities in Ethereum’s auction-based block-building market.
• It proposes auction redesigns, such as randomized bid submissions and meta-strategy constraints, to counter oligopolistic bidding behaviors.
• By addressing exclusive orderflow and infrastructure gaps, BuilderNet aims to improve proposer revenue and foster a more decentralized market.

BuilderNet is a concept and evolving set of technologies situated at the intersection of distributed systems, blockchain consensus, Maximal Extractable Value (MEV) extraction, and fair market design for block production in Ethereum and related decentralized systems. The motivation and technical instantiation of BuilderNet arise in response to oligopolistic dynamics and centralization risks in Ethereum’s auction-based block-building market, particularly under the Proposer-Builder Separation (PBS) paradigm and MEV-Boost auction infrastructure (Wu et al., 24 Dec 2024).

1. Background: Block Production, MEV, and Market Structure

In Ethereum’s contemporary block production, PBS cleanly separates the roles of the block proposer (chosen via Proof-of-Stake consensus) and the block builder. Builders compete to construct blocks using both public mempool data and private orderflow opportunities, seeking to maximize the MEV extracted per block. They submit competitive bids in an English auction known as MEV-Boost, typically via trusted relays to proposers, with the highest bidder’s block being included in the chain.

The builder’s bid $s_i(x_{i, t}, m)$ reflects both public (observable to all) and private (builder-exclusive) value:

$$s_i(x_{i, t}, m) = P(t) + \lambda_i(m) \cdot E_i(t)$$

where $P(t)$ is the public MEV signal, $E_i(t)$ the private MEV, and $\lambda_i(m)$ denotes the fraction of private signal included, a function of the builder’s meta-strategy $m$ (conservative, moderate, aggressive).

This structure is intended to create a competitive, revenue-maximizing environment for proposers, while allowing specialized entities (builders) to optimize for MEV extraction and block assembly. However, empirical and analytical evidence indicates the emergence of strong oligopolistic tendencies, with a small cohort of builders (e.g., beaverbuild, Titan) consistently dominating auction win rates due to structural advantages.

2. Strategic Bidding Behaviors and the Formation of Oligopolies

A builder’s utility function in a given slot is:

$u_i(m) = L_i(t_v) - s_i(t_v)$

where $L_i(t) = P(t) + E_i(t)$ is the aggregate MEV signal and $t_v$ the auction close time. Builders select $m$ to maximize expected utility, weighing the probability of winning (influenced by speed and unique orderflow) against the proportion of MEV they cede in the auction.

The auction meta-game is characterized by three canonical builder strategies:

• Aggressive: Large $\lambda_i(m)$, i.e., exposing a high proportion of private MEV to increase win probability, but sacrificing builder profit margin.
• Moderate: Intermediate $\lambda_i(m)$.
• Conservative: Small $\lambda_i(m)$, retaining more private surplus but facing greater risk of losing.

Under symmetric conditions (equal network latency and orderflow access), Nash equilibrium analysis reveals convergence to aggressive bidding: competitive pressure forces all builders to expose most of their private MEV to minimize opportunity cost. However, practical market asymmetries—primarily in network latency ($\Delta_i$) and private orderflow access ($\theta_i$)—skew the payoff landscape.

• Latency: Lower latency grants builders fresher MEV signals and more time to update bids before auction closure, enabling them to undercut others with slightly less aggressive bids.
• Private Orderflow Access: Builders with high $\theta_i$ (often via exclusive agreements with orderflow providers) face less risk when bidding conservatively, as they remain more likely to win via unique, non-public MEV.

This leads to vertical integration, with dominant builders leveraging economies of scale, technical superiority, and exclusive partnerships to establish durable competitive advantages.

3. Market Centralization and Auction Efficiency

Quantitative analysis of market power is performed via the Herfindahl–Hirschman Index (HHI):

$\mathrm{HHI} = \sum_{i=1}^n (w_i)^2$

where $w_i$ is the market share (win rate) of builder $i$. Empirical measurements show elevated HHI compared to an ideal competitive market, confirming the presence of oligopoly.

Auction efficiency $\eta$ is defined as the ratio of the winning bid to the total extractable MEV:

$$\eta = \sum_k \pi_k \cdot (\text{average } \eta_k), \quad \text{with} \quad \eta_k = \frac{\text{Winning bid}_k}{L(t)_k}$$

Observed auction efficiency decreases as dominant builders, able to win with less aggressive bids, effectively bid less than the available MEV. This reduces proposer revenue and attenuates the intended alignment between competitive block building and optimal MEV capture.

4. MEV Distribution and Decentralization Challenges

A fair MEV distribution is a prerequisite for market competitiveness. In an equitable regime, builders’ win probabilities align with their operational merit (infrastructure, algorithms), not access to exclusive or proprietary orderflow. However, vertical integration and structural information asymmetries present formidable obstacles:

• Exclusive Orderflow: Builders partnered with large wallets or aggregators preferentially receive private MEV, reducing opportunities available to others.
• Infrastructure Disparities: Capital-intensive investments in relays and connectivity further entrench incumbents.

This disincentivizes market entry and undermines the ethos of decentralization.

5. BuilderNet: Proposals for Market Fairness and Orderflow Allocation

BuilderNet, as referenced in discussions of block-building market reform (Wu et al., 24 Dec 2024), denotes a class of mechanisms intended to (a) provide more equitable access to orderflow for builders, and (b) narrow latency and information gaps, thus restoring fair competitive conditions. BuilderNet approaches may encompass:

• Orderflow Redistribution: Allocating private transactions across multiple builders via trusted intermediaries or probabilistic assignment, mitigating exclusive access.
• Auction Redesign: Modifying bid submission protocols (e.g., randomization, delayed reveals) to weaken the link between private orderflow and guaranteed auction victory.
• Relay System Improvements: Standardizing infrastructure to reduce latency variance among builders.
• Meta-Strategy Constraints: Capping the fraction of private MEV that can be included in bids, or introducing protocol-level randomness to strategy selection.

Table: Central Features of BuilderNet Approaches

Challenge	BuilderNet Mechanism	Intended Effect
Exclusive Orderflow	Shared/intermediated distribution	Reduce builder asymmetry
Latency Gaps	Relay standardization	Equalize signal freshness
Bid Function Skew	Randomized/limited MEV inclusion	Restore competitive equilibrium
Market Centralization	Equitable protocol-level constraints	Foster decentralization

A plausible implication is that, by decoupling private orderflow from builder identity and limiting structural advantages, BuilderNet can ameliorate market concentration and improve both auction efficiency and proposer revenue.

6. Broader Impact and Ongoing Directions

BuilderNet is aligned with a family of initiatives (e.g., MEV-Share, MEVBlocker) and research efforts seeking to underpin the economics of the block-building market with resilient fairness and anti-centralization properties. Unresolved questions concern the tradeoffs between privacy, liveness, and economic optimality in orderflow redistribution, and the integration of BuilderNet-like protocols with existing PBS frameworks.

The evolution of block production auctions in Ethereum and similar systems is likely to be shaped by the effectiveness of BuilderNet-style solutions in countering oligopolistic market convergence and maintaining decentralization at scale. Further analytical, empirical, and protocol engineering work is ongoing to quantify these effects and determine optimal market architecture (Wu et al., 24 Dec 2024).