How to Design a Proposal Evaluation Framework

A proposal evaluation framework is a systematic process for reviewing, scoring, and deciding on governance proposals in a DAO or on-chain protocol. Its primary goal is to move beyond subjective sentiment by establishing objective criteria that assess a proposal's impact, feasibility, and alignment with the community's goals. A well-designed framework reduces governance fatigue, improves decision quality, and creates a transparent audit trail for all participants. Without it, governance can become chaotic, dominated by loud voices rather than sound reasoning.

The foundation of any framework is its evaluation criteria. These are the specific dimensions against which every proposal is measured. Common categories include:

• Impact & Value: What problem does it solve and what is the expected return (financial, social, utility)?
• Feasibility: Is the technical implementation sound, is the timeline realistic, and does the team have the capability?
• Cost & Resources: Is the requested budget justified and does it provide clear value for money?
• Risks & Mitigation: What are the potential downsides (security, financial, reputational) and how are they addressed?
• Alignment: How well does the proposal support the protocol's long-term vision and tokenholder interests?

To operationalize these criteria, you need a scoring system. This translates qualitative assessment into quantitative data. A simple approach is a weighted scoring model, where each criterion is assigned a percentage weight based on its importance (e.g., Impact: 40%, Feasibility: 30%, Cost: 20%, Risk: 10%). Evaluators then score each criterion on a scale (e.g., 1-5). The final score is a weighted sum, providing a clear, comparable metric. For example, a proposal scoring 4 on Impact (40% weight) and 3 on Feasibility (30% weight) would have a partial score of (4 * 0.4) + (3 * 0.3) = 2.5.

The evaluation process must be transparent and replicable. This means publishing the framework's criteria, weights, and scoring rubric publicly, often in the DAO's documentation or a dedicated forum post. Evaluators should provide written rationale for their scores, especially for outliers. Tools like Snapshot with custom strategies, Tally for proposal lifecycle management, or specialized platforms like Commonwealth or Boardroom can help formalize this process. The output is not just a "yes/no" vote, but a data-rich analysis that informs the broader electorate.

Finally, a framework must include a feedback and iteration loop. Post-implementation reviews of funded proposals are crucial for assessing real-world outcomes versus projections. This data should be used to refine the evaluation criteria and weights over time. For instance, if proposals scoring high on "Feasibility" consistently fail to deliver, that criterion's weight or definition may need adjustment. This creates a learning governance system that improves its decision-making accuracy with each funding cycle, ultimately stewarding community resources more effectively.

A proposal evaluation framework is a structured process for assessing the merit, feasibility, and impact of governance submissions. Its primary goal is to standardize decision-making and reduce information asymmetry among voters. A well-designed framework should be transparent, objective, and aligned with the protocol's long-term goals. It typically involves criteria like technical soundness, economic impact, and alignment with the community's values. Without such a framework, voting can become chaotic, driven by sentiment rather than analysis, leading to suboptimal outcomes for the protocol.

The core of any framework is its evaluation criteria. These should be specific, measurable, and weighted according to importance. Common categories include: - Technical Feasibility: Can the proposed code changes be implemented securely and on schedule? - Financial Impact: What are the expected costs, revenue implications, and tokenomics effects? - Community Alignment: Does the proposal support the stated mission and values of the DAO? - Risk Assessment: What are the potential security vulnerabilities or unintended consequences? Tools like Snapshot or Tally often host these criteria alongside proposals for voter reference.

To implement the framework, you need clear roles and processes. Designate evaluators, such as a technical committee for code audits or a treasury working group for budget analysis. Establish a review timeline with stages: initial submission, community feedback, expert evaluation, and final revision. Using smart contracts for on-chain attestations or platforms like SourceCred for quantifying contributor input can add objectivity. The process should be documented in the DAO's governance handbook, ensuring consistency and allowing new members to understand how decisions are made.

Quantitative metrics are crucial for objective assessment. For treasury requests, use financial modeling to project runway impact. For protocol upgrades, require testnet deployment and audit reports. Metrics like NPV (Net Present Value) for grants or expected APY change for incentive adjustments make proposals comparable. However, balance metrics with qualitative analysis. A proposal's impact on developer experience or community morale may not be easily quantified but is vital for long-term health. The framework should mandate that proposers provide data to support their claims.

Finally, the framework must be iterative and adaptable. Governance needs evolve; a rigid system will become obsolete. Implement a feedback loop where the evaluation process itself can be reviewed and upgraded via governance. Use post-implementation reviews to check if projected outcomes matched reality, creating a knowledge base for future decisions. Tools like Boardroom or Commonwealth can help track proposal lifecycles. By treating the framework as a living document, the DAO can continuously improve its decision-making quality and resilience.

An evaluation framework provides a structured methodology to assess proposals against predefined criteria. Its primary goals are to ensure consistency, transparency, and objectivity in decision-making. A well-designed framework moves beyond subjective opinions by establishing clear rubrics, assigning measurable weights to different criteria, and documenting the rationale for scores. This is essential for decentralized governance, grant programs like the Ethereum Foundation or Optimism RetroPGF, and internal project funding, as it builds trust and accountability within the community.

The foundation of any framework is its evaluation criteria. These should be specific, measurable, and aligned with the program's strategic goals. Common categories include:

• Impact & Value: The potential benefit to the ecosystem or target audience.
• Feasibility: The team's capability and the realism of the execution plan.
• Technical Merit: The quality, innovation, and security of the proposed solution.
• Community Alignment: How well the proposal serves the community's needs and values. Each criterion needs a clear description and a scoring scale (e.g., 1-5) to guide evaluators.

To implement these criteria, you need a scoring mechanism. This often involves assigning a weight to each criterion to reflect its relative importance. For example, a research grant might weight Technical Merit at 40% and Feasibility at 30%. The final score is a weighted sum: Final Score = (Impact Score * 0.3) + (Feasibility Score * 0.3) + (Technical Score * 0.4). Using a standardized template or a tool like Gitcoin Grants Stack or Prop House can automate this calculation and ensure all evaluators use the same scale.

Effective frameworks also define the evaluation process. This includes selecting qualified evaluators, providing them with clear guidelines and calibration sessions, and establishing a review timeline. A multi-stage process with checks and balances—such as initial screening, in-depth review, and a final panel discussion—helps mitigate individual bias. Transparency is key; publishing the evaluation rubric and, where possible, anonymized scores and feedback, allows proposers to understand decisions and improves the system over time.

Finally, the framework must be iterative. After each funding round or decision cycle, analyze the outcomes. Did the selected projects deliver the expected impact? Were certain criteria poor predictors of success? Use this data to refine the criteria, adjust weights, and improve evaluator training. This feedback loop, inspired by retroactive public goods funding (RetroPGF) models, ensures the framework evolves and remains effective in a dynamic ecosystem.

Start by defining your evaluation criteria. These are the measurable standards against which every proposal will be judged. Common categories include: feasibility (can the team execute this?), impact (what value does it create for the protocol?), cost-effectiveness (is the requested budget reasonable?), and alignment (does it support the DAO's long-term goals?). For a DeFi protocol, you might add specific technical criteria like security audit requirements or integration complexity. Clearly document each criterion and its weighting, as this forms the backbone of your framework.

Next, establish a clear review process. Determine who evaluates proposals—this could be a dedicated committee, a panel of subject matter experts, or a broader community working group. Define the review stages, such as an initial completeness check, a technical deep-dive, and a final community sentiment analysis. Tools like Snapshot for signaling, Discourse for discussion, and specialized platforms like Tally or Boardroom can orchestrate this flow. The process should be transparent, with all reviews and scores published on-chain or in public forums to build trust.

Implement a quantitative scoring system to reduce bias. Assign a numerical score (e.g., 1-5) to each evaluation criterion. For example, a proposal's impact might be scored based on projected user growth, while feasibility is scored on the team's proven track record. You can use a simple weighted average or a more complex formula. Consider implementing a bonding curve for voting power, where voters who align with the eventual consensus earn rewards, to incentivize careful evaluation. Smart contracts can automate the aggregation of these scores.

Integrate qualitative analysis and due diligence. Numbers alone don't tell the whole story. The framework must include steps for manual review: verifying contributor identities, analyzing code repositories for technical proposals, and assessing potential risks or externalities. For a grant proposal, this means checking the team's GitHub activity and prior deliverables. Create a standardized due diligence checklist that reviewers must complete, ensuring no critical aspect, like smart contract security or legal compliance, is overlooked.

Finally, create clear output and feedback loops. The framework should produce a decisive outcome: approve, reject, or request revisions. Every proposal, regardless of outcome, should receive a published evaluation report summarizing the scores and key feedback. This transparency educates the community and helps future proposers. Use the accumulated data to iteratively refine your criteria and weights. Analyze which scored criteria best predicted successful outcomes and adjust your framework accordingly in subsequent governance cycles.

The core of a proposal evaluation framework is a set of on-chain and off-chain checks that determine a proposal's validity and priority. Key on-chain checks include verifying the proposer's token balance for submission deposits, ensuring the proposal targets a valid contract address, and confirming the requested transaction calldata is safe (e.g., no self-destruct calls). Off-chain, you should validate proposal metadata format, run spell-check on descriptions, and check for spam or duplicate submissions. Tools like OpenZeppelin Defender can automate security checks, while The Graph can index historical data to flag similar past proposals.

For objective, quantitative scoring, implement a multi-criteria decision analysis (MCDA) system. Create a smart contract or off-chain service that assigns weighted scores to predefined criteria. Common criteria include: impact_score (estimated protocol effect), feasibility_score (based on team track record or code audit status), cost_score (treasury request amount), and alignment_score (with DAO manifesto or strategic goals). Scores can be calculated automatically using on-chain data (like TVL impact) or submitted by a designated reviewer committee. The final aggregate score determines the proposal's queue position or eligibility for a snapshot vote.

Automation is essential for scaling governance. Use keeper networks like Chainlink Automation or Gelato to trigger evaluation phases. For example, a keeper can automatically move a proposal from a 'Pending Review' to 'Ready for Vote' state once it receives a minimum number of approvals from a council multisig. You can also automate bounty payouts for reviewers using Sablier or Superfluid streams upon completion of their assessment. However, guardrails are crucial: implement timelocks on state transitions and maintain a circuit breaker function, often a DAO multisig, that can pause the automated system in case of an exploit or flawed logic.

To prevent gaming and ensure fairness, incorporate sybil resistance and reputation systems. Instead of relying solely on token-weighted voting for evaluations, integrate tools like BrightID or Gitcoin Passport for unique-human verification of community reviewers. Build a reputation ledger (e.g., an ERC-20 or non-transferable NFT) that grants more voting power in the evaluation phase to members who have previously submitted high-quality assessments or whose past reviews aligned with final DAO voting outcomes. This creates a meritocratic layer within the evaluation process.

Finally, design for transparency and dispute resolution. All evaluation scores, reviewer identities (or pseudonyms), and automated check results should be published on-chain or to immutable storage like IPFS or Arweave. Implement a challenge period where any token holder can stake a bond to dispute an evaluation outcome. The dispute can be routed to a decentralized court like Kleros or a DAO-specific panel. This creates a verifiable audit trail and a safety valve for contested decisions, increasing the overall legitimacy of the governance process.

The framework you've designed—with its clear rubrics, weighted criteria, and transparent process—is now ready for a pilot. Start with a single, well-defined grant round or a small committee. Use this pilot to test your scoring system's objectivity and identify any ambiguous criteria. Tools like Snapshot for voting, GitHub Discussions for proposal feedback, and custom Notion or Airtable databases for evaluator workflows can be instrumental. The goal is to gather initial data on evaluation time, inter-evaluator agreement (using metrics like Cohen's Kappa), and community feedback on the fairness of outcomes.

After the pilot, analyze the results quantitatively and qualitatively. Did the scores clearly differentiate between high and low-quality proposals? Were there disputes about rubric application? Use this analysis to iterate on your framework. You may need to adjust weightings, clarify rubric descriptions, or introduce calibration sessions for evaluators. This iterative loop of deploy, measure, and refine is critical for maintaining the framework's effectiveness as your DAO's scope and the complexity of proposals evolve. Documenting these changes in a public changelog reinforces transparency.

Consider the long-term evolution of your governance. As the DAO matures, you might explore advanced mechanisms like conviction voting for funding, retroactive public goods funding (RPGF) models, or algorithmic reputation scores for evaluators. The framework should be a foundation that enables, not hinders, such innovation. Finally, remember that no system is perfect. Continuous community education about the process, coupled with a commitment to open-source your methodology and findings (like Optimism's RetroPGF rounds), contributes to the broader ecosystem's knowledge and upholds the decentralized governance principles that make DAOs unique.