Bonding Curve Research Group Library ๐Ÿ“š
  • About the BCRG
  • About this Library
  • โ™ป๏ธFrom Static to Dynamic Supply Tokens
  • โžฐWhat are Bonding Curves?
  • ๐Ÿ—ƒ๏ธDifferentiating Primary & Secondary AMMs
  • ๐Ÿค–Modeling & Simulating Bonding Curves
  • ๐ŸŽ›๏ธBonding Curve Parameter Matrix & Trade-Off Decisions
    • Initial Supply
    • Initial Reserve
    • Initial Price
    • Reserve Ratio
    • Mint Fee
    • Burn Fee
    • Max Supply
  • โ˜ ๏ธAttack Vectors
    • Liquidations
    • Sandwich trading
    • Front Running
    • Backrunning
    • Solutions
  • ๐Ÿ““Case Studies
    • ๐Ÿค–Aavegotchi
      • Bonding Curve Design
      • Pricing Algorithm
      • Governance and Tokenomics
        • Avegotchi DAO Evoution
    • ๐Ÿ‘ฃCarbon
      • Asymmetric Liquidity
      • Adjustable Bonding Curves
      • Matching, Routing & Arbitrage in AMMs
      • MEV Resistance
    • ๐Ÿ“ˆContinuous Organization (cOrg)
      • cOrg Token Bonding Curve Model
        • The Decentralized Autonomous Trust
        • Bonding Curve Contract Dynamics in Investment and Sale Operations
    • ๐ŸฎCoW Protocol
      • Loss Versus Rebalancing (LVR)
        • Deep dive into Loss-Versus-Rebalancing (LVR)
      • Batch Trading & Function-Maximizing AMMs
      • Implementation - COW AMM
    • โš™๏ธDXDao
      • DXdao Bonding Curve
    • โš“Gyroscope
      • The Gyro Bonding Curve
      • Elliptic Concentrated Liquidity Pools (E-CLP)
      • Gyro Consolidated Price Feeds
        • Consolidated Price Feed Approach
    • ๐Ÿ•‰๏ธOlympus DAO
      • Range Bound Stability
    • ๐Ÿ’ธ Public Goods Token Performance Analysis
  • ๐Ÿ„ Engineering for Resilience with Primary Issuance Markets
  • ๐Ÿ’ปBCRG Github Repos
  • ๐Ÿ“ฝ๏ธBCRG Video Library
  • ๐Ÿ“–Glossary
  • ๐Ÿ”ŽToken Engineering Courses & Resources
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Attack Vectors

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Last updated 9 months ago

Overview of various sources of blockchain extractable value

Maximal Extractable Value

MEV is excess value captured by miners (or validators) from users in a cryptocurrency network. This excess value often comes from reordering usersโ€™ transactions to maximize fees or from inserting new transactions that front-run usersโ€™ transactions. Public blockchains allow any user to submit a transaction that modifies the shared state of the network. Miners (or validators in proof-of-stake networks) aggregate these transactions into blocks which they propose to the network. Each miner can propose blocks at a rate roughly proportional to the resources they have locked into the network. Thus, the fixed fees each miner earns (e.g., block rewards in Bitcoin or staking yields in Ethereum) are also approximately proportional to these resources.

There are three principal agents involved in MEV: miners, network users, and MEV searchers. Miners contribute resources to a network in order to win the chance to earn fees by validating transactions. Network users are ordinary users who submit financial transactions to miners to be validated and added to the blockchain. Finally, MEV searchers (or simply, โ€˜searchersโ€™) are agents who find profitable opportunities from reordering, inserting, or omitting transactions. Searchers design strategies: solutions to knapsack-like problems which find the most profitable sequence of transactions that fits within the block limit.

By viewing MEV as a multi-agent game between miners, searchers, and users, we can compare the equilibria that emerge from different forms of MEV. This perspective allows us to analyze the economic properties of systems with MEV. The profitability of MEV strategies often varies dramatically from one application to another. . Moreover, the observed types of MEV strategies have grown rapidly.

Since MEV was first defined in 2019, miners and searchers have extracted over $650 million
โ˜ ๏ธ
However, the transaction-dependent fees collected by the miner often vary dramatically from block to block.
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