  Ethereum Improvement Proposal (EIP) 1559 was recently implemented to
transform Ethereum's transaction fee market. EIP-1559 utilizes an algorithmic
update rule with a constant learning rate to estimate a base fee. The base fee
reflects prevailing network conditions and hence provides a more reliable
oracle for current gas prices.
  Using on-chain data from the period after its launch, we evaluate the impact
of EIP-1559 on the user experience and market performance. Our empirical
findings suggest that although EIP-1559 achieves its goals on average,
short-term behavior is marked by intense, chaotic oscillations in block sizes
(as predicted by our recent theoretical dynamical system analysis [1]) and slow
adjustments during periods of demand bursts (e.g., NFT drops). Both phenomena
lead to unwanted inter-block variability in mining rewards. To address this
issue, we propose an alternative base fee adjustment rule in which the learning
rate varies according to an additive increase, multiplicative decrease (AIMD)
update scheme. Our simulations show that the latter robustly outperforms the
EIP-1559 protocol under various demand scenarios. These results provide
evidence that variable learning rate mechanisms may constitute a promising
alternative to the default EIP-1559-based format and contribute to the ongoing
discussion on the design of more efficient transaction fee markets.
