Proposer-Builder Separation, payment for order flows, and centralization in blockchain
Existing consensus protocols on blockchain, exhibit centralization phenomena as larger validators often secure disproportionate rewards. The proposer-builder-separation (PBS) framework proposed by Ethereum introduces a mechanism that decouples block proposing from block building, establishing a competitive market for block construction. Builders create blocks and bid fees they are willing to pay validators for including their blocks into the blockchain.
Agostino Capponi (Columbia) argues that PBS offers validators an outside option to accept blocks built by the highest-bidding builders, regardless of their size. This helps reduce and even eliminate centralization among validators, particularly in a highly competitive builders’ market. Despite its potential to reduce validator centralization, his analysis uncovers an emergent centralization trend within the builders’ market itself. This new form of centralization arises from certain builders possessing advanced internal search capabilities, which enhance their bargaining power with order flow providers and secure order flows at lower costs. This advantage increases their chances of dominating the block building market. He identifies this dynamic as leading to a “Proof-Of-MEV” paradigm, where the capacity to efficiently capture and utilize extractable value becomes a leading factor in the construction of valuable blocks.
About the presenter
Agostino is a Professor in the Department of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute and the founding director of the Columbia Center for Digital Finance and Technology. His current research interests are in financial technology, market microstructure, energy markets, and networks. Agostino’s research has been funded by major agencies, including NSF, DARPA, DOE, IBM, GRI, INET, Ripple, Stellar, and the Ethereum foundation. His research has been recognized with the 2018 NSF CAREER award, and with a JP Morgan AI Research Faculty award. Agostino is co-editor of the book Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices (Cambridge University Press, 2023).
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