Adaptive Fee Algorithm

The core mechanism that automatically increases or decreases the base fee per gas for the next block based on the utilization of the previous block. If a block is more than 50% full, the base fee increases; if it's less than 50% full, it decreases. This creates a negative feedback loop that targets a stable block size, preventing sustained congestion.

A separate, user-specified fee paid directly to the block producer (miner or validator) on top of the base fee. This tip incentivizes them to include a transaction in the next block. Users compete for priority by setting higher tips during high congestion, while the base fee is algorithmically burned to regulate supply.

The canonical implementation on Ethereum, which introduced the adaptive fee model. Key components include:

• Base Fee: Dynamically adjusted and burned (removed from supply).
• Priority Fee: Optional tip to validators.
• Fee Cap: The maximum total fee (base + priority) a user is willing to pay.
• Gas Target: The ideal block size (e.g., 15 million gas on Ethereum) that the base fee targets.

By providing users with a clear view of the current base fee for the pending block, adaptive algorithms improve price predictability. Wallets can estimate costs more accurately, reducing the guesswork and failed transactions associated with first-price auctions. Users can also set fee caps to avoid overpaying.

Unlike fixed-size block models, adaptive fee systems allow blocks to expand (up to a limit, e.g., 2x the target) when demand spikes, for a higher base fee. This provides elastic block space, increasing throughput during temporary surges without requiring a hard fork to change permanent block size limits.

Adaptive fees replace the traditional first-price auction model where users blindly bid gas prices. In an auction, fees are highly volatile and inefficient, often leading to overpayment. The adaptive model's burned base fee reduces speculative fee bidding and creates a more efficient, stable, and user-friendly fee market.

The base fee is the minimum gas price required for a transaction to be included in a block, burned by the protocol. It is the foundational, mandatory component of the total fee. Algorithms adjust this value per block based on the target block size and the utilization of the previous block, creating predictable, protocol-controlled fee pressure.

A priority fee (or tip) is a voluntary payment made by users to validators/miners to incentivize faster transaction inclusion. It is added on top of the base fee. Adaptive algorithms often provide suggested tip ranges based on the urgency of inclusion, measured by how quickly a user wants their transaction confirmed.

This is the primary signal for fee adjustment. It measures demand for block space by comparing gas used in the last block to the network's gas target (ideal block size).

• If gas used > gas target: Base fee increases.
• If gas used < gas target: Base fee decreases. This creates a negative feedback loop that regulates congestion.

The mempool (memory pool) is the set of all pending transactions. Algorithms analyze its size and the fee distribution of queued transactions to forecast future demand. A large mempool with high-fee transactions signals sustained congestion, potentially leading to more aggressive base fee increases.

Algorithms look at a rolling window of past blocks (e.g., the last 10-100 blocks) to smooth out volatility and identify trends. This prevents the base fee from overreacting to a single full or empty block, ensuring fee adjustments are stable and reflect longer-term network conditions.

The behavior of block producers influences fee markets. Algorithms may account for Maximal Extractable Value (MEV) opportunities, which can cause spikes in priority fees. Monitoring the inclusion of complex transaction bundles helps predict atypical demand that distorts simple gas-based models.

The algorithm increases fees during periods of high volatility or impermanent loss risk, directly compensating LPs for the greater risk they assume. This creates a more sustainable yield model compared to static fee tiers. For example, a 0.3% fee pool might temporarily adjust to 0.5% during a market event.

By incentivizing deeper liquidity during volatile periods, the adaptive mechanism helps maintain tighter spreads. Traders benefit from lower effective costs (slippage + fees) even when the nominal fee rate is higher, as the pool can absorb larger orders without significant price impact.

The system reacts to on-chain metrics without manual intervention. Key triggers include:

• Volatility spikes (e.g., large price moves)
• Surge in trading volume
• Changes in pool composition (e.g., high asymmetry) This ensures the fee structure is always aligned with current risk and capital efficiency.

Adaptive fees create a self-regulating market for arbitrageurs. When fees are high, it requires a larger price discrepancy between venues to make arbitrage profitable, which can temporarily reduce front-running and miner extractable value (MEV) opportunities, leading to fairer execution for all traders.

The algorithm aligns the financial incentives of LPs, traders, and the protocol itself. Higher fee revenue during peak usage can be shared via fee tiering or governance token rewards, creating a positive feedback loop that attracts and retains capital in the protocol's liquidity pools.

Unlike static models (e.g., Uniswap v3's fixed 0.05%, 0.30%, 1.00% tiers), an adaptive model is not a one-size-fits-all solution. It provides:

• Dynamic risk pricing
• Pro-cyclical liquidity incentives
• Reduced need for manual LP management (e.g., frequently moving capital between fee tiers).

The fundamental unit of computational work on Ethereum. Adaptive fee algorithms primarily determine the price (in Gwei) for each unit of gas. Key metrics include:

• Gas Limit: Max gas per block.
• Gas Used: Actual gas consumed in a block.
• Base Fee: The algorithmically determined minimum gas price.

The memory pool where pending transactions wait to be included in a block. Adaptive fee algorithms analyze the mempool's size and composition (transaction fees) to gauge real-time demand and calculate the appropriate fee for timely inclusion.

The rate at which a blockchain processes transactions (e.g., transactions per second, TPS). A primary goal of adaptive fees is to maximize throughput by efficiently clearing the mempool and stabilizing transaction confirmation times during volatile demand.

A potential economic attack where a miner ignores the canonical chain to mine alternative blocks with higher total fees. Some adaptive fee designs, like EIP-1559's burned base fee, aim to mitigate this by reducing the variance in block reward from transaction fees.