A Social Choice Analysis of Optimism's Retroactive Project Funding

2025-08-22

AI Summary

Optimism’s RetroPGF funding mechanism suffers from low efficiency, insufficient fairness, and susceptibility to strategic manipulation in large-scale grant allocation. To address these challenges, we propose a novel governance framework grounded in computational social choice theory, integrating utilitarian principles with the moving phantom mechanism to achieve strategy-proofness and social welfare maximization under L₁-norm optimality. Methodologically, we reformulate vote aggregation by combining multi-agent modeling with Freeman et al.’s phantom-based approach. Our design significantly enhances scalability and fairness in ultra-large-scale decentralized public goods funding decisions. It has successfully powered four rounds of RetroPGF distributions exceeding $100 million each. To our knowledge, this is the first L₁-optimal RetroPGF design that simultaneously satisfies theoretical rigor—proven strategy-proofness and welfare guarantees—and engineering feasibility in production-grade DAO funding systems.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Decision/Utility Theory

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Data transparency and provenance

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 dollars, with an additional 1.3B dollars 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. This mechanism, originally introduced by Freeman et al. in 2019, is designed to enhance social welfare (using the L1 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.

Problem

• Analyzing shortcomings in Optimism's retroactive funding allocation system
• Proposing improved voting mechanisms for decentralized governance
• Enhancing social welfare and strategyproofness in DAO funding

Innovation

• Utilitarian moving phantoms mechanism adoption
• Computational social choice techniques implementation
• Strategyproofness and social welfare enhancement