A Social Choice Analysis of Optimism’s Retroactive Project Funding
Eyal Briman (),
Nimrod Talmon (),
Angela Kreitenweis () and
Muhammad Idrees ()
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Eyal Briman: Ben-Gurion University
Nimrod Talmon: Ben-Gurion University
Angela Kreitenweis: GovXS
Muhammad Idrees: GovXS
A chapter in Decentralized Autonomous Organizations—Governance, Technology, and Legal Perspectives, 2026, pp 69-96 from Springer
Abstract:
Abstract The Optimism Retroactive Project Funding (RetroPGF) is a key initiative within the blockchain ecosystem that retroactively rewards projects deemed valuable to the Ethereum and Optimism communities. Managed by the Optimism Collective, a decentralized autonomous organization (DAO), RetroPGF represents a large-scale experiment in decentralized governance. Funding rewards are distributed in OP tokens, the native digital currency of the ecosystem. As of this writing, four funding rounds have been completed, collectively allocating over $100M, with an additional $1.3B reserved for future rounds. However, we identify significant shortcomings in the current allocation system, underscoring the need for improved governance mechanisms given the scale of funds involved. Leveraging computational social choice techniques and insights from multiagent systems, we propose improvements to the voting process by recommending the adoption of a utilitarian moving phantoms mechanism [1]. This mechanism was originally introduced by Freeman et al. [1], is designed to enhance social welfare (using the $$\ell _1$$ ℓ 1 norm) while satisfying strategyproofness–two key properties aligned with the application’s governance requirements. Our analysis provides a formal framework for designing improved funding mechanisms for DAOs, contributing to the broader discourse on decentralized governance and public goods allocation.
Keywords: DAOs; Computational Social Choice; Voting Mechanisms; Simulations (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-03273-7_5
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DOI: 10.1007/978-3-032-03273-7_5
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