RFP 7 - Understanding & Minimizing Dead Weight Loss (Enabling Partial Blocks)

Researcher: TBD


The present initiative seeks to understand dead weight loss (DWL) of maximum extractable value (MEV) because of sub-optimal block construction. Once this is understood, we will create a model for how to optimize blocks to limit DWL and maximize MEV extraction, which we believe can be achieved by enabling partial block building, along with incorporating other optimizations.

This initiative will contribute the following:

  • An understanding of how full block building versus partial block building affects the MEV pipeline, including dead weight loss
  • A model/simulation for blocks that optimize MEV

As a result of this model, there will be a greater opportunity for MEV extraction, enabling more earnings for extractors.

Background & Problem Statement


Core background concepts/definitions are as follows:

Maximal Extractable Value (MEV):

MEV is a type of profit that can be extracted from pending transactions through various mechanisms such as including, excluding, or reordering the transactions in blocks.

In recent years, MEV has been explored and described in research, though with ever-changing technology, it is not fully understood. Daian et al., for example, were the first to coin the term “MEV” in their 2019 paper exploring a number of types of MEV extraction on Ethereum.

Some core types of MEV extraction are listed below:

  • Frontrunning: searchers use bots to scan mempools for profitable transactions, replicating user transactions with a higher gas price so that new transaction will be chosen over the initial user’s transaction
  • Sandwich attacks: a front-running strategy to manipulate crypto prices, accomplished by searchers placing a trade right before and after a large DEX trade to benefit from the artificial price change
  • Arbitrage: traders leverage a price difference between two exchanges (such as two DEXes or a CEX and a DEX), making a buy at the lower price and then selling at the higher price for a profit; this also aligns the prices of the exchanges and makes the market more efficient
  • Liquidation: when users do not repay their loans on a lending protocol, their collateral can often be liquidated by anyone to earn a liquidation fee

Dead Weight Loss (DWL):

Dead weight loss is the societal cost of market inefficiency (e.g. when supply and demand are mismatched). It can also be defined as the difference in production and consumption of a good or service.

Problem Statement

The problem here is that we do not understand, as an industry, how to optimize blocks for maximal extraction of MEV. Moreover, lack of block optimization likely results in dead weight loss, i.e. negative impact of the overall industry because of the mismatch between block supply and demand.

Thus, the question that this research aims to address is as follows:

  • How can we optimize blocks/blockbuilding to minimize DWL and maximize MEV?

Plan & Deliverables

Expected outputs/deliverables are as follows:

  • An understanding of how full block building versus partial block building affects the MEV pipeline, including dead weight loss
  • A model/simulation for blocks that optimize MEV

The plan for achieving this output is outlined below:

Experiment 1: Understanding what happens to blocks that do not win on Ethereum

When a block is proposed on Ethereum, there is an MEV-boost relay that selects the block with the highest amount of MEV (tips). There is one builder that wins. However, it is unclear what happens to blocks that do not win.

We thus will complete an experiment to better understand why these blocks do not win. Moreover, we will study what happens to the transactions in these blocks that were not in the winning block. Specifically, we seek to understand if they are included in the next block and when.

We will also calculate the potential MEV that could have been extracted from those losing blocks.

Data for this experiment could be taken from searcherbuilder.pics or a similar tool.

Experiment 2: Creating a model for block optimization

We will use the above data to create a model for optimizing blocks so that we minimize DWL and maximize MEV. This will likely involve the incorporation of partial block building, so that blockspace supply and demand can be optimally matched.


How to Participate

If you’re a researcher who believes that you would be a good fit to contribute to any of the Composable RFPs, please reach out to Composable’s Lead Research Associate, Sydney Sweck, at sydney@composable.finance. In the email, be sure to include:

  • The RFP number(s) you’d like to contribute to
  • Your relevant background experience
  • How you think you could contribute to the research