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Evaluating Voting Design Vulnerabilities for Retroactive Funding

Jay Yu, Austin Bennett, Billy Gao, Rebecca Joseph

TL;DR

This paper analyzes vulnerabilities in Optimism's RetroPGF voting designs by developing a formal model of round-based, token-backed governance and validating findings through extensive simulations. It provides proofs of vulnerabilities for Quadratic, Mean, and Median voting, complemented by a large-scale simulation framework that measures resilience via the Pairwise Manipulation Score. The results show Quadratic Voting offers the strongest resistance to manipulation, while Mean and Median are susceptible to phantom-vote and collusion attacks, especially in large-scale project manipulation. The work offers practical recommendations for future RetroPGF rounds and contributes an open-source dashboard to enable practitioners to explore attack scenarios and parameter sensitivity in real time.

Abstract

Retroactive Public Goods Funding (RetroPGF) rewards blockchain projects based on proven impact rather than future promises. This paper reviews voting mechanisms for Optimism's RetroPGF, where "badgeholders" allocate rewards to valuable projects. We explore Optimism's previous schemes for RetroPGF voting, including quadratic, mean, and median voting. We present a proof-based formal analysis for vulnerabilities in these voting schemes, empirically validate these vulnerabilities using voting simulations, and offer assessments and practical recommendations for future iterations of Optimism's system based on our findings.

Evaluating Voting Design Vulnerabilities for Retroactive Funding

TL;DR

This paper analyzes vulnerabilities in Optimism's RetroPGF voting designs by developing a formal model of round-based, token-backed governance and validating findings through extensive simulations. It provides proofs of vulnerabilities for Quadratic, Mean, and Median voting, complemented by a large-scale simulation framework that measures resilience via the Pairwise Manipulation Score. The results show Quadratic Voting offers the strongest resistance to manipulation, while Mean and Median are susceptible to phantom-vote and collusion attacks, especially in large-scale project manipulation. The work offers practical recommendations for future RetroPGF rounds and contributes an open-source dashboard to enable practitioners to explore attack scenarios and parameter sensitivity in real time.

Abstract

Retroactive Public Goods Funding (RetroPGF) rewards blockchain projects based on proven impact rather than future promises. This paper reviews voting mechanisms for Optimism's RetroPGF, where "badgeholders" allocate rewards to valuable projects. We explore Optimism's previous schemes for RetroPGF voting, including quadratic, mean, and median voting. We present a proof-based formal analysis for vulnerabilities in these voting schemes, empirically validate these vulnerabilities using voting simulations, and offer assessments and practical recommendations for future iterations of Optimism's system based on our findings.

Paper Structure

This paper contains 33 sections, 34 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Optimism's RetroPGF
  4. Contributions
  5. Literature Review: Social Choice Theory and DAOs
  6. Goals of Social Choice Theory
  7. Theoretical Foundations for RetroPGF Mechanisms
  8. Cross-Mechanism Comparison with DAOs
  9. Methodology
  10. Optimism RetroPGF Governance Assumptions
  11. Model Specification
  12. A Simplified Model of Aggregate Voting Mechanisms
  13. Mean Voting
  14. Median Voting
  15. Quadratic Voting
  16. ...and 18 more sections

Figures (1)

  • Figure 1: Distribution of manipulation scores across voting mechanisms. Each row shows a different voting mechanism (Quadratic, Mean, and Median), while columns represent Baseline, Voter Attack, and Project Attack scenarios. The x-axis shows manipulation scores and y-axis shows frequency counts across 10,000 simulation iterations.

Theorems & Definitions (4)

  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4