Strategic Bidding Wars in On-chain Auctions
- URL: http://arxiv.org/abs/2312.14510v3
- Date: Sun, 17 Mar 2024 21:10:24 GMT
- Title: Strategic Bidding Wars in On-chain Auctions
- Authors: Fei Wu, Thomas Thiery, Stefanos Leonardos, Carmine Ventre,
- Abstract summary: We introduce a game-theoretic model for MEV-Boost auctions and use simulations to study different builders' bidding strategies observed in practice.
Our results demonstrate the importance of latency on the effectiveness of builders' strategies and the overall auction outcome from the proposer's perspective.
- Score: 14.64404722196177
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Ethereum block-building process has changed significantly since the emergence of Proposer-Builder Separation. Validators access blocks through a marketplace, where block builders bid for the right to construct the block and earn MEV (Maximal Extractable Value) rewards in an on-chain competition, known as the MEV-boost auction. While more than 90% of blocks are currently built via MEV-Boost, trade-offs between builders' strategic behaviors and auction design remain poorly understood. In this paper we address this gap. We introduce a game-theoretic model for MEV-Boost auctions and use simulations to study different builders' bidding strategies observed in practice. We study various strategic interactions and auction setups and evaluate how the interplay between critical elements such as access to MEV opportunities and improved connectivity to relays impact bidding performance. Our results demonstrate the importance of latency on the effectiveness of builders' strategies and the overall auction outcome from the proposer's perspective.
Related papers
- Who Wins Ethereum Block Building Auctions and Why? [2.762397703396294]
The MEV-Boost block auction contributes approximately 90% of all blocks.
Between October 2023 and March 2024, only three builders produced 80% of them.
We identify features that play a significant role in builders' ability to win blocks and earn profits.
arXiv Detail & Related papers (2024-07-18T22:49:37Z) - Remeasuring the Arbitrage and Sandwich Attacks of Maximal Extractable Value in Ethereum [7.381773144616746]
Maximal Extractable Value (MEV) drives the prosperity of the blockchain ecosystem.
We propose a profitability identification algorithm to identify MEV activities on our collected largest-ever dataset.
We have characterized the overall landscape of the MEV ecosystem, the impact the private transaction architectures bring in, and the adoption of back-running mechanisms.
arXiv Detail & Related papers (2024-05-28T08:17:15Z) - Flashback: Enhancing Proposer-Builder Design with Future-Block Auctions in Proof-of-Stake Ethereum [27.386337024680245]
Auction mechanisms used between searchers, builders and proposers are crucial to the overall health of the blockchain.
In this paper, we consider PBS design in as a game between searchers, builders and proposers.
A key novelty in our design is the inclusion of future block proposers, as all proposers of an epoch are decided ahead of time in proof-of-stake (PoS)
Our analysis shows the existence of alternative auction mechanisms that result in a better equilibrium to players compared to state-of-the-art.
arXiv Detail & Related papers (2024-05-15T15:58:21Z) - Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning [10.41350502488723]
We investigate whether multi-agent reinforcement learning algorithms can be used to understand iterative auctions.
We find that MARL can indeed benefit auction analysis, but that deploying it effectively is nontrivial.
We illustrate the promise of our resulting approach by using it to evaluate a specific rule change to a clock auction.
arXiv Detail & Related papers (2024-02-29T18:16:13Z) - Demystifying DeFi MEV Activities in Flashbots Bundle [36.64508078443365]
Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity.
Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV)
The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction.
arXiv Detail & Related papers (2023-12-02T09:46:39Z) - Semantic Information Marketing in The Metaverse: A Learning-Based
Contract Theory Framework [68.8725783112254]
We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data.
Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices.
We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
arXiv Detail & Related papers (2023-02-22T15:52:37Z) - Off-Beat Multi-Agent Reinforcement Learning [62.833358249873704]
We investigate model-free multi-agent reinforcement learning (MARL) in environments where off-beat actions are prevalent.
We propose a novel episodic memory, LeGEM, for model-free MARL algorithms.
We evaluate LeGEM on various multi-agent scenarios with off-beat actions, including Stag-Hunter Game, Quarry Game, Afforestation Game, and StarCraft II micromanagement tasks.
arXiv Detail & Related papers (2022-05-27T02:21:04Z) - A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in
Online Advertising [53.636153252400945]
We propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies.
Our approach outperforms several baseline methods in terms of social welfare and guarantees the ad platform's revenue.
arXiv Detail & Related papers (2021-06-11T08:07:14Z) - ProportionNet: Balancing Fairness and Revenue for Auction Design with
Deep Learning [55.76903822619047]
We study the design of revenue-maximizing auctions with strong incentive guarantees.
We extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.
arXiv Detail & Related papers (2020-10-13T13:54:21Z) - Certifying Strategyproof Auction Networks [53.37051312298459]
We focus on the RegretNet architecture, which can represent auctions with arbitrary numbers of items and participants.
We propose ways to explicitly verify strategyproofness under a particular valuation profile using techniques from the neural network verification literature.
arXiv Detail & Related papers (2020-06-15T20:22:48Z) - MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding [47.555870679348416]
We propose a Multi-ecTive Actor-Critics algorithm named MoTiAC for the problem of bidding optimization with various goals.
Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments.
arXiv Detail & Related papers (2020-02-18T07:16:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.