Assignment Overview
Core Learning Objectives
- Mechanism Design: Students understand how voting mechanisms affect outcomes and power distribution
- Governance Analysis: Students can evaluate real DAO structures and identify vulnerabilities
- Proposal Writing: Students develop realistic governance proposals with proper justification
- Quantitative Analysis: Students calculate voting outcomes across different mechanisms
- Critical Thinking: Students recognize trade-offs between decentralization, efficiency, and fairness
This assignment bridges theoretical mechanism design with real-world decentralized governance. Students experience the challenges DAOs face: low participation, whale influence, information asymmetry, and coordination problems. By simulating voting outcomes, students viscerally understand how mechanism choice shapes collective decision-making.
Time commitment: 8-10 hours (2 hours proposal drafting, 3-4 hours voting simulations, 2-3 hours analysis, 1 hour presentation prep)
DAO Profile Background
The three DAO profiles are based on real organizations with data anonymized for educational purposes:
DAO A: Based on Uniswap
| Real treasury: | $2.5B+ in UNI tokens and stablecoins |
| Governance forum: | gov.uniswap.org |
| Token distribution: | Top addresses visible on Etherscan |
| Key proposals: | Polygon deployment, grant programs, fee switches |
| Governance docs: | docs.uniswap.org/protocol/concepts/governance |
Characteristics: High treasury, low participation (~4-6%), extreme concentration (top 10 holders ~45%), direct token voting.
DAO B: Based on Aave
| Real treasury: | $400M+ ecosystem reserve |
| Governance forum: | governance.aave.com |
| Safety Module: | $400M+ staked AAVE as insurance |
| Key proposals: | Risk parameter changes, new asset listings, incentive programs |
| Governance docs: | docs.aave.com/governance |
Characteristics: Staking-aligned incentives, higher participation (~15-20%), technical focus, two-tier governance (AIPs vs risk parameters).
DAO C: Based on ENS (Ethereum Name Service)
| Real treasury: | $800M+ in ENS tokens and ETH |
| Governance forum: | discuss.ens.domains |
| Delegation: | ~60% of tokens delegated, 500+ active delegates |
| Key proposals: | Working group budgets, ENS ecosystem fund, protocol upgrades |
| Governance docs: | docs.ens.domains/dao |
Characteristics: Delegation-first design, working group structure, moderate concentration (top delegate ~8%), accountable representatives.
Using Real DAO Data in Class
Consider showing live governance forums during class discussions. Students can see actual proposals, voting participation, delegate rationales, and community debates. This grounds abstract concepts in reality.
Suggested Teaching Timeline
| Week | Instructor Activities | Student Activities |
|---|---|---|
| Week 1 |
|
|
| Week 2 |
|
|
| Week 3 |
|
|
| Week 4 |
|
|
Discussion Prompts & Key Questions
During Proposal Presentations
Mechanism Favoritism
Ask: "Which proposals benefit most from token voting? Which benefit from quadratic voting?"
Expected insight: Technical proposals favored by whales pass under token voting. Community-focused proposals gain support under quadratic voting.
Whale Perspectives
Ask: "If you held 5% of the DAO's tokens, how would you vote on this proposal? Does your personal interest align with the community's?"
Expected insight: Large holders optimize for token price and protocol safety, sometimes at odds with community preferences for risky innovation.
Information Asymmetry
Ask: "How many small token holders truly understand the technical details of this proposal? Should DAOs restrict voting to informed participants?"
Expected insight: Tension between inclusive democracy and expert decision-making. Leads to discussion of delegation benefits.
Low Turnout Problem
Ask: "Why is turnout only 5% in DAO A? What are the costs and benefits of voting? How could the DAO increase participation?"
Expected insight: Rational apathy (small holders have little influence), gas costs, attention costs. Solutions include delegation, gasless voting, incentives.
Mechanism Vulnerabilities
Ask: "How could a malicious actor exploit quadratic voting? What about conviction voting?"
Expected insight: Quadratic voting vulnerable to Sybil attacks (split holdings across addresses). Conviction voting vulnerable to patient attackers who stake early.
Comparative Analysis Questions
DAO Structure Comparison
Ask: "Compare DAO A's direct voting with DAO C's delegation system. What are the trade-offs?"
Expected insight: Direct voting is more democratic but suffers from low participation and uninformed voting. Delegation concentrates knowledge but risks capture.
Aligned vs Unaligned Incentives
Ask: "How does DAO B's Safety Module staking change voter incentives compared to DAO A?"
Expected insight: Stakers have long-term alignment and prioritize protocol safety. But they may resist changes that reduce their rewards or increase risk.
Proposal Type Effects
Ask: "Would a controversial fee increase proposal have the same voting dynamics as a grant program?"
Expected insight: Controversial proposals see higher turnout. Voters who usually abstain participate when stakes are high. This can change the effective voter distribution.
Common Student Mistakes & How to Address
Mistake 1: Unrealistic Budget Requests
Problem: Students request 10%+ of treasury without justification.
Solution: Emphasize the 0.5-3% guideline. Show real DAO proposals that passed with modest budgets. Discuss why large requests face skepticism.
Mistake 2: Vague Success Metrics
Problem: "Increase awareness" or "improve the ecosystem" without specifics.
Solution: Require numerical targets. Practice converting vague goals into KPIs: awareness → 10k social media followers, ecosystem → 50 new projects.
Mistake 3: Math Errors in Voting Calculations
Problem: Quadratic voting calculated as N² instead of √N, or conviction formula applied incorrectly.
Solution: Dedicate class time to worked examples. Provide Excel/Python templates with formulas pre-built. Check calculations during office hours.
Mistake 4: Ignoring Token Distribution
Problem: Students assume uniform voting without considering whale concentration.
Solution: Require explicit voter group breakdowns (top 10, top 100, small holders) in analysis. Emphasize that distribution matters more than mechanism in some cases.
Mistake 5: Surface-Level Analysis
Problem: "Quadratic voting is fairer" without explaining why or quantifying the difference.
Solution: Push students to quantify: "Fairer by how much? Show me the percentage change in whale influence." Require specific examples and calculations.
Office Hours Strategy
Schedule two office hours sessions: one during proposal drafting (Week 1) to catch unrealistic budgets/metrics early, and one during voting simulation (Week 2) to check calculations. This prevents last-minute scrambling.
Grading Tips & Calibration
Quick Assessment Heuristics
Excellent proposals (18-20/20):
- Cite specific data or DAO precedents
- Budget breakdown sums correctly and includes contingency
- Success metrics have numbers and timeframes
- Risk section identifies 3+ risks with specific mitigation
- Reads like a real DAO proposal
Good proposals (14-17/20):
- Adequate justification but lacks depth
- Budget is reasonable but some line items vague
- Metrics present but could be more specific
- Risks mentioned but mitigation is generic
Excellent analysis (14-15/15):
- All calculations correct with work shown
- Quantifies whale influence as percentages
- Identifies non-obvious vulnerabilities (Sybil attacks, delegation capture)
- Explains why outcomes differ across mechanisms with game theory
Calibration Exercises
Before Grading the Class
Grade the three sample proposals yourself using the rubric. This helps internalize the criteria. Expected scores:
- Sample 1 (L2 Deployment): 48/50 (excellent technical proposal)
- Sample 2 (Grant Program): 44/50 (good community proposal, less analytical depth)
- Sample 3 (Safety Module): 46/50 (strong alignment analysis, good quantification)
Common Grading Pitfalls
Avoid These Grading Errors
- Creativity bias: Don't reward novelty over rigor. A boring but well-executed proposal deserves full points.
- Penalizing wrong outcomes: If a proposal fails in all three voting mechanisms, that's not necessarily wrong—it might reveal design flaws worth exploring.
- Inconsistent turnout assumptions: Some students assume 5% turnout, others 15%. Both are reasonable; grade the analysis, not the assumption.
- Expecting perfect proposals: Real DAOs pass imperfect proposals all the time. Grade against the rubric, not an idealized standard.
Assignment Extensions & Variations
For Advanced Students
Extension 1: Code the Voting Simulation
Challenge students to write Python or JavaScript code that simulates voting outcomes given any token distribution and turnout. Bonus points for Monte Carlo simulations exploring outcome variance.
Extension 2: Post to Real DAO Forum
Students post their proposal to the actual DAO's governance forum (clearly labeled as educational exercise). Bonus points for receiving substantive community feedback. This is powerful real-world engagement.
Extension 3: Game-Theoretic Analysis
Students model the proposal as a voting game, identifying Nash equilibria under each mechanism. When is it rational to vote? When do whales have dominant strategies?
Alternative Assignment Formats
Group Version
Teams of 3-4 draft competing proposals for the same DAO. Class votes using each mechanism. Winning proposal gets bonus points. This creates competitive dynamics and demonstrates voting in practice.
Simulation Game
Assign each student a token holding amount (distributed according to real DAO data). Students campaign for their proposals and vote on others. Debrief on what strategies emerged.
DAO Comparison Essay
Instead of proposing, students compare governance across 5+ real DAOs and recommend best practices. More analytical, less creative.
Additional Teaching Resources
Academic Papers
- Weyl, E. Glen, and Vitalik Buterin. "Quadratic Voting as Efficient Corporate Governance." (2019)
- Buterin, Vitalik, et al. "Flexible Design for Funding Public Goods." (2019) - Conviction voting
- Lessig, Lawrence. "Code Is Law: On Liberty in Cyberspace." (2000) - Governance philosophy
Real DAO Resources
- Governance Forums: gov.uniswap.org, governance.aave.com, discuss.ens.domains
- Analytics: Dune Analytics dashboards for DAO metrics
- Voter Behavior: Snapshot.org for voting patterns
- Delegate Platforms: Tally.xyz for delegate profiles and voting records
Videos & Talks
- "DAO Governance and Coordination Failures" - Vitalik Buterin (Devcon)
- "Quadratic Voting Explained" - RadicalxChange Foundation
- "Building Effective DAOs" - a16z Crypto School
Tools Students Can Use
- Simulation: Spreadsheet templates for voting calculations (create or share)
- Visualization: Desmos for graphing conviction accumulation
- Real voting: Snapshot.org for gasless test voting
Assessment Philosophy
This assignment rewards rigor over novelty. A well-researched, thoroughly justified proposal for a boring but necessary initiative (e.g., security audit funding) should score higher than a flashy, poorly justified moonshot.
The voting simulation is where students demonstrate quantitative thinking. Excellent students don't just calculate—they interpret. They explain why quadratic voting reduces whale influence by 40%, not just that it does.
The critical thinking section rewards systems thinking. Students who identify second-order effects ("if we implement quadratic voting, whales will split holdings across addresses") demonstrate deeper understanding than those who stay at surface level.
Ultimately, this assignment asks: Can students think like DAO designers? Can they balance competing values—decentralization vs efficiency, inclusivity vs expertise, transparency vs privacy? There are no perfect answers, only thoughtful trade-offs.
© Joerg Osterrieder 2025-2026. All rights reserved.