Summary
Governance is critical for Scroll’s success. Good governance drives growth and resource allocation, while poor governance leads to ecosystem collapse despite large treasuries. Currently, DAO governance suffers from two critical challenges:
- Misaligned incentives: DAOs suffer from voter apathy and farming of incentives (GPT delegates). Current manual approaches to rewarding delegates to do better work are biased and conflict-prone.
- Collective decision making is hard: DAOs suffer from haphazard and biased decision making, and frequent back-channelling limits collective learning and discussion.
Dr. Philip Brown is a recognised leader in mechanism design, having received extensive funding from NSF, NASA, and the Air Force and multiple accolades in this field. The Network Goods Institute, in collaboration with him and RnDAO, we have identified “Carroll Mechanisms” — an evolution of Futarchy and Prediction Markets — as a potential solution for governance systems where participants are incentivised to:
- share information honestly,
- evaluate proposals on merit rather than politics,
- and collaboratively discover solutions without relying on pre-defined choices.
The Scroll Foundation also recognises this direction as promising.
Scroll has an opportunity to implement this cutting edge governance mechanism to improve its performance as a DAO, reward delegates fairly, and build the L2 chain where DAOs go to thrive.
This proposal takes Scroll to the forefront of governance innovation by fast-tracking R&D in Carroll Mechanisms from theory into implementation. Via a 6-month research and implementation program from August 2025 through July 2026. The outcome will be better decision-making in Scroll and fair rewards for delegates.
The program is divided into three milestones. M2 and M3 are conditional on successful M1:
- Base research on mechanism feasibility ($45k),
- Applied research to identify the ideal mechanism to implement and calibrate the parameters ($45k),
- Integration into the Negation Game product so Scroll can immediately benefit from this mechanism to upgrade its governance and delegate rewards system ($55k).
Rather than a traditional grant, we propose an investment structure with shared upside: a $145,000 USD-equivalent SAFE+Token Warrant investment at $12M cap that aligns Scroll’s interests with the project’s success through potential ROI and ecosystem growth.
Motivation
Problem:
In Web3, we’ve seen our share of governance failures. Multiple ecosystems have collapsed despite starting with massive treasuries. Too many died not because of competition but through grifting and self-sabotage.
Good governance leads to good decisions and growth. Bad governance leads to an ecosystem of misallocated resources.
Current DAO governance models suffer from multiple challenges to sustain participation, align incentives, reward governance work, and otherwise make good decisions. For DAOs to survive, improving upon the current delegates’ models is an existential necessity. This is not a problem limited to DAOs, as governments and corporations also suffer from analogous issues, including:
- Conflicts of interest between management and shareholders
- Executive compensation misalignment with company performance
- Groupthink, confirmation bias, authority bias, HIPPO, etc.
- Limited access to expertise leading to risk management and oversight deficiencies
- etc.
Designing incentives for good governance is hard. Finding a better model could unlock massive value for all humans.
The current practice in DAOs is to appoint delegates who then vote on behalf of token holders. However, DAO delegate models suffer from their own set of issues and multiple problems of traditional board governance:
- delegates themselves are seldom kept accountable by token holders,
- can be apathetic (limited engagement with governance),
- and often show behaviours (e.g. GPT replies to proposals, always voting in favour to avoid relationship strain, etc.) coming from incentive misalignment.
Current approaches:
Manual approaches to incentivise governance work (i.e. reward delegates) are prone to biases and conflicts of interest as those who determine the incentives are beholden to the delegates they evaluate. Manual allocation of rewards is also prone to conflict due to the inherent subjectivity in the approach.
Rethinking the solution space from the ground up
Voting systems are incredibly popular governance models, whether taking the form of one-person-one-vote, token/share voting, or representative democratic models. However, voting systems have well-known shortcomings, both theoretically (cf. Arrow’s impossibility theoremˇ) as well as practically. Among the most salient shortcomings in the context of delegate-based DAO governance are the problems of:
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incentive misaligned factions (including all forms of bribery and corruption),
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whales (large power holders that strangle promising investments),
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and problems of low voter participation, capacity, and knowledge.
There are alternatives to voting-based governance systems, among them is futarchy. Futarchy is a governance approach that uses prediction markets—such as those found on Polymarket and Metaculus—to guide DAO decision-making based on forecasted metric outcomes. This has been implemented in straightforward cases like Uniswap, where token price provides a clear and mutually accepted success metric. Futarchy offers a more robust and credibly neutral solution, as it incentivises those with valuable information to engage in governance, while aligning incentives towards identifying the correct answer and long-term accrual of value. However, futarchy has severe limitations:
- Constrains deliberation to predetermined options, preventing groups from discovering novel solutions through collaborative reasoning. The system’s effectiveness depends entirely on how well the initial questions and choices are framed, making it vulnerable to poor problem formulation.
- Selecting appropriate resolution metrics proves challenging, as chosen measures may inadequately capture true success, become targets for gaming, or suffer from Goodhart’s Law effects where optimization distorts the underlying objective. Many meaningful outcomes are also difficult to quantify or emerge only over extended timeframes.
- Obscures the reasoning behind decisions, revealing only traders’ final positions without the underlying rationale. This opacity limits knowledge transfer, reduces organizational learning, breeds distrust, and prevents the alignment-building that comes from understanding why decisions were made.
What if we could rethink the use of voting mechanisms altogether? Could we move away from simply voting on predefined options and invent a game that allows participants to deliberate and co-create new solutions?
Solution:
A promising solution is a set of mechanisms we call “Carroll Mechanisms”. Carroll Mechanisms improve upon the limitations of futarchy by helping define the resolution criteria for a market — not just determining what the outcome of a question is, but also how we determine that outcome.
For example:
- Instead of simply asking “Who will win the next presidential election?”, a Carroll Mechanism clarifies what counts as a legitimate source for declaring the winner (e.g. certified results by a national election commission).
- Or for a more complex question like “Does vaccine X cause neurological disorders?”, it helps define what we mean by “neurological disorders,” which studies or data are valid, what thresholds of causality apply, and how uncertainty or conflicting evidence should be handled.
Carroll Mechanisms work by building on prediction markets, which already let people bet on whether statements are true or false (like “Who will become president in 2026?”). But Carroll Mechanisms add something new: they let participants challenge whether a piece of information is actually relevant to answering the original question.
The key innovation is that these mechanisms can automatically figure out what information should count as relevant. In simpler terms: instead of just asking “Is this true?” like regular prediction markets, Carroll Mechanisms also ask “Does this even matter for answering our question?” — and they have a smart way to figure out the answer.
Carroll Mechanisms do this through a built-in system that identifies which participants are genuinely neutral and unbiased, as demonstrated by a history of changing their minds, then uses their input to determine what’s actually useful for resolving the question. For more about Carroll Mechanisms, read this post.
This approach could enable governance participants to engage more collaboratively and productively, and be rewarded accordingly. Carroll Mechanism could hold the missing key to solving incentive alignment for DAO governance.
The Scroll Foundation and RnDAO believe this approach is promising to transform DAO governance, scientific funding allocation ($1.2 trillion market), and even has implications for city and nation state governance and the emergence of network states. However, before proceeding with an implementation in Scroll or elsewhere, it’s necessary to gain confidence about the design of the systems and the best parameters for it. The necessary first step is mathematical modelling and computer simulation, then moving to implementation.
We thus propose an initiative to fund the necessary research (over a period of 6 months) and deliver a working product for Scroll to adopt this new governance system.
We have divided the proposal into 3 phases (milestones) so that the full capital is used only if the initial research validates Carroll Mechanisms.
This proposal will enable Scroll to make high-quality decisions and reward delegates fairly, thus positioning Scroll as an industry leader in governance and addressing one of the biggest limitations of DAOs.
Incentive Alignment with Scroll
Governance represents both a fundamental requirement for Scroll’s success and a strategic opportunity to establish credibility by addressing a critical need across the DAO ecosystem. We sweeten this proposition by aligning our incentives more closely: turning the funding for governance also into an ecosystem growth activity.
We achieve this by making the funding not a grant but an investment. This approach thus aligns Scrolls’ needs for better governance and potential ROI from capital deployments.
Execution
Personnel & Resources
The team:
Lead Researcher: Dr Philip N. Brown is an expert in incentive, mechanism, and utility design for network games and will bring this experience to bear on the problem [Singh et al., Collins et al., Brown et al.]. Using concepts from algebraic graph theory and mechanism design, we will specify the relevant incentive constraints (modeling CAP and RE) which govern the feasibility of Carroll mechanisms.
He has been awarded external research funding from t he National Science Foundation, NASA, the Air Force Office of Scientific Research, and the NSA. Philip is interested in the fundamental mathematics of strategic decision-making and applies this to mechanism design for infrastructure systems, decentralized algorithm design for multi-machine systems, and networked societal/autonomous systems. He’s an Associate Professor in the Department of Computer Science at the University of Colorado Colorado Springs. Dr. Brown received the 2018 CCDC Best PhD Thesis Award from UCSB, the Best Paper Award from GameNets 2021, a 2023 AFOSR Young Investigator Program award, and a 2025 NSF CAREER award. He is also a longtime observer of the world of blockchains and DAOs.
Second researcher: Connor McCormick is the founder of the Network Goods Institute. Prior founder of a machine learning computer vision hardware startup, founding team member at Digital Gaia, a climate intelligence company where he contributed to novel economic primitives and Bayesian machine learning systems for climate impacts, and contributor to the development of collective intelligence protocols at Metaculus. You can find Connor’s writing here and here.
Engineering Lead: Kaden Bilyeu is the engineering lead for the Negation Game, the product suite into which a successful design of Carroll Mechanisms will be integrated. Kaden is the creator of EasyTL an AI based translation tool, he has made over 3.2k commits on GitHub in 2025 so far, examples of his work can be found here.
Project advisor: Daniel Ospina is an instigator at RnDAO. Organisation designer and 3x founder (1 exit), ex-Head of Governance at Aragon and ex-supervisory council SingularityNET. Daniel trained BCG consultants on system design and innovation methodology, was a visiting lecturer at Oxford University on innovation and systems design, and a Harvard Business Review author.
Project advisor: Andrea Gallagher is the research lead at RnDAO. Drea has been building and supporting startups since Web 1. Strategic researcher for products such as Google Workspace ($16bn revenue) and Asana ($183mn revenue), and in Web3 at Aragon. Drea honed her skills in the Innovation Catalyst unit at Intuit (TurboTax and Quickbooks, $14bn revenue).
Operational
The work is divided into two milestones.
The timeline of this project is driven by Dr. Brown’s availability from mid-August 2025 through mid-May 2026, with the greatest availability from August through December. Accordingly, we anticipate that the bulk of the Milestone 1 work will occur between mid-August and mid-December of 2025, and we will prepare a Milestone-1-focused publication in January/February 2026. We propose the following milestone dates for Milestone 1 (base period M1):
- August 18, 2025: Official project kickoff.
- September 30, 2025: Complete mathematical model posed.
- December 15, 2025: Feasibility questions for Carroll mechanisms settled.
- Mid-February, 2026: Milestone 1 results submitted for publication to ACM EC.
The Milestone 2 work (option period) will occur over Spring and Summer 2026, with a lower intensity of activity due to Dr. Brown’s existing obligations:
- January 1, 2026: Milestone 2 kickoff.
- February 28, 2026: Key metrics identified.
- May 31, 2026: Pareto-optimal Carroll mechanisms identified.
- Mid-summer 2026: Milestone 2 results submitted for publication.
Research progress will be summarized on a monthly basis in a public Coda doc found here: Login - Coda
Financial
The team is committed to delivering this research and product for $145,000 USD equivalent.
Costs are divided as per our two milestones $45k (M1: Base) to prove feasibility + $45k (M2: Option) to find the best design + $55k (M3: Option) to integrate it into the Negation Game product and deploy it to the DAO.
Andrea and Daniel will not receive a fee for their engagement on the project.
M2 will only happen if M1 is positive. If not, the remaining funds will be returned to the DAO’s budget.
Budget breakdown:
We break the research budget into a $45,000 base period plus a $45,000 option period, with a $55,000 implementation budget for integrating the resulting mechanism set into the existing Negation Game product and deploy and roll out to Scroll DAO. The base period will center on Research Thrust 1, and the option period will center on Research Thrust 2 plus additional prototype development. If the project is funded at only the base level, we anticipate one significant academic publication; the option period would result in at least one additional publication, with potential for more as resources allow.
Milestone 1 (base research): total $45,000
- Dr. Philip Brown Research time: Total $36,500 for research time, averaging 12 hours/week for 17 weeks from August 18, 2025 through December 15, 2025 (this amount is inclusive of publication preparation time in January, 2026). The average hourly compensation for Dr. Brown’s time is $178 per hour, coming in competitive with other effective hourly rates for game theory and mechanism design research which range from $75-120/hour in academia and $500-1,500/hour for senior consulting work in industry (deepresearch source). Dr. Brown will operate as an independent researcher on M1, studying the feasibility of Carroll mechanisms, reviewing relevant literature, formulating formal conjectures, conducting numerical experiments to verify conjectures, developing mathematical proofs of relevant propositions, preparing results for publication, and presenting results at scientific conferences.
- Network Goods Institute: total $8,500. The Network Goods Institute team will collaborate on research progress with Dr. Philip Brown, including conducting bi-weekly program calls for 17 weeks and evaluating the research progress, once every two weeks (8 total) this will be an open call for anyone to attend so that the community and outsiders can have visibility into the innovative research work that is happening at Scroll DAO. In this supporting capacity, the team will offer feedback on methodological approaches, contribute to discussions about research directions, and suggest technical solutions when appropriate. They will identify potential concerns that might affect the research timeline and provide relevant market and user experience insights, drawing from the learnings from bringing the existing product to market, to complement Dr. Brown’s expertise and help ensure the research achieves its intended impact and is useful for practical applications.
Milestone 2 (applied research): total $45,000
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Dr. Philip Brown: Total $28,500 for research time, averaging 6 hours/week for 26 weeks from January 2026 through July 2026. Dr. Brown will conduct research as described in Thrust 2 on determining the set of Pareto-optimal Carroll mechanisms, reviewing relevant literature, formulating formal conjectures, conducting numerical experiments to verify conjectures, developing mathematical proofs of relevant propositions, preparing results for publication, and presenting results at scientific conferences.
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Network Goods Institute: total $16,500. The Network Goods Institute team will collaborate on research progress with Dr. Brown as in the base period, as well as contributing to numerical evaluation of mechanisms with cadCAD, and prototyping the resultant mechanisms for the purposes of testing their real world usability and dynamics to add richness to the resultant research findings.
Milestone 3 (implementation, running in parallel to M2): total $55,000
- The Network Goods Institute team: Total $55,000 for integration and deployment work over 26 weeks running parallel to the M2 period (January 2026 through July 2026). The team will begin implementing the initial Carroll mechanisms identified in M1 immediately upon completion of the M1 period (December 2025), with iterative updates as additional optimal mechanisms are developed during M2. Implementation effort will ramp up significantly in the final months as M2 research conclusions are finalized, ensuring complete integration and deployment by the conclusion of the M2 period.
- Simulation and evaluation: a paid mechanism beta evaluation process will be put into place, getting feedback from DAO governance participants on the familiarity and usability of the user interface, $5,000.
Implementation timeline and effort allocation:
- January-March 2026: 6-8 hours/week - Initial integration of M1 findings, foundational development
- April-May 2026: 8-10 hours/week - Iterative updates as M2 research progresses
- June-July 2026: 12-15 hours/week - Intensive final integration, testing, and deployment as M2 research concludes
Complete Project Budget Breakdown
Complete Project Budget Breakdown
| Period | Duration | Total Cost | Dr. Philip Brown | Network Goods Institute |
|---|---|---|---|---|
| Base research (M1) | Aug 18, 2025 - Dec 15, 2025 | $45,000 | $36,500 | $8,500 |
| Applied research (M2) | Jan 2026 - Jul 2026 | $45,000 | $28,500 | $16,500 |
| Implementation (M3) | Jan 2026 - Jul 2026 | $55,000 | $0 | $55,000 |
| TOTAL | $145,000 | $65,000 | $80,000 |
Dr. Philip Brown - Activity & Cost Breakdown
Base Period (M1) - $36,500
| Activity | Duration | Cost |
|---|---|---|
| Literature Review & Foundation | 4 weeks | $8,588 |
| Property Definition & Research Framework | 4 weeks | $8,588 |
| Mathematical Modeling & Compositional Development | 5 weeks | $10,735 |
| Iterative Numerical & Theo retical Experimentation | 13 weeks (overlapping) | $8,589 |
| M1 Total | 17 weeks | $36,500 |
Literature Review & Foundation (Weeks 1-4)
- Review of mechanism design theory relevant to Carroll mechanisms
- Analysis of proper scoring rules and prediction market literature
- Survey of reentrancy attack prevention mechanisms
- Review of information cascade and bounty system research
- Analysis of Schelling point theory and coordination mechanisms
Property Definition & Research Framework (Weeks 5-8)
- Formal definition of targeted Carroll mechanism properties:
- Proper scoring incentives for market participants
- Reentrancy attack prevention mechanisms
- Confidence cascade promotion structures
- Information bounty allocation systems
- Early identification advantage mechanisms
- Stable Schelling points around high falsifiability
- Non-plutocratic and integrity-based incentive structures
- Establishment of compositional mechanism design framework for iterative development
Mathematical Modeling & Compositional Development (Weeks 9-13)
- Development of simple foundational models using constructive proof methodology
- Iterative model refinement through pose-refute-refine cycles
- Modular mechanism design approach: proving properties of simple components
- Compositional assembly of verified components into more complex mechanisms
- Incremental formalization of Carroll mechanism properties
Iterative Numerical Experimentation (Weeks 5-17)
- Continuous numerical validation throughout model development
- Simulation-driven exploration of mechanism variants
- Computational verification of theoretical properties for simple components
- Empirical testing of composed mechanism behaviors
- Iterative refinement based on experimental feedback
Applied research (M2) - $28,500
| Activity | Duration | Cost |
|---|---|---|
| Advanced Mechanism Design | 8 weeks | $8,769 |
| Mechanism Refinement and Numerical Optimizations | 8 weeks | $8,769 |
| Prototype Development | 6 weeks | $6,577 |
| Research Synthesis & Documentation | 4 weeks | $4,385 |
| M2 Total | 26 weeks | $28,500 |
Advanced Mechanism Design (Weeks 1-8)
- Research on Pareto-optimal Carroll mechanism variants
- Comparative analysis of different mechanism configurations
- Optimization of mechanism parameters
- Trade-off analysis between efficiency and fairness
Theoretical Refinement (Weeks 9-16)
- Development of formal proofs for key propositions
- Mathematical characterization of optimal mechanisms
- Analysis of equilibrium properties
- Robustness and stability analysis
Prototype Development (Weeks 17-22)
- Design of mechanism prototypes for testing
- Integration planning with existing DAO systems
- User experience considerations
- Technical specification development
Research Synthesis & Documentation (Weeks 23-26)
- Consolidation of research findings
- Preparation of technical documentation for DAO
- Publication preparation ($1,500 allocated)
- Knowledge transfer materials for implementation team
Network Goods Institute - Activity & Cost Breakdown
Base research (M1) - $8,500
| Activity | Duration | Cost |
|---|---|---|
| Bi-weekly Research Collaboration Calls | 17 weeks | $5,100 |
| Research Progress Evaluation & Feedback | 17 weeks | $2,550 |
| Market & UX Insights Integration | 17 weeks | $850 |
| M1 Total | 17 weeks | $8,500 |
Applied research (M2) - $16,500
| Activity | Duration | Cost |
|---|---|---|
| Bi-weekly Research Collaboration Calls | 26 weeks | $7,800 |
| Research Progress Evaluation & Feedback | 26 weeks | $3,900 |
| Mechanism Prototyping & Testing | 26 weeks | $4,800 |
| M2 Total | 26 weeks | $16,500 |
Implementation (M3) - $55,000
| Activity | Duration | Cost |
|---|---|---|
| Initial Integration (M1 findings) | 3 months (6-8 hrs/week) | $16,500 |
| Iterative Updates (M2 progress) | 2 months (8-10 hrs/week) | $13,750 |
| Incentives for mechanism beta testers | 2 month (5-7 hours / week) | $5,000 |
| Final Integration & Deployment | 2 months (12-15 hrs/week) | $24,750 |
| M3 Total | 26 weeks | $55,000 |
Key Deliverables:
- Technical integration of Carroll mechanisms into existing codebase
- User interface updates and UX optimization for new mechanisms
- Comprehensive testing suite including governance reward simulations
- Documentation and user guides for DAO members
- Beta testing program with select user groups
- Final production deployment to DAO infrastructure
- Post-launch monitoring and analytics dashboard
Notes
- Publication budget of $1,500 allocated from M2
- All amounts inclusive of independent researcher overhead
A note on valuation: we were offered a $24mn valuation by a VC (BlueYard) with a $1.2mn investment.
BlueYard found the project promising due to the extremely large market opportunity that Carroll Mechanisms present if successful — namely that they address a class of capital allocation whose TAM (total addressable market) includes both venture funding ($500b annual market size) and scientific funding ($1.2t annual market size), coupled with the plausibility of adoption of this mechanism set due to its user story similarity to successful crypto properties like MetaDAO, Polymarket, and pump.fun.
BlueYard also found an investment in the Network Goods Institute attractive due to Index Wallets, a payment mechanism with public goods funding properties, which are beyond the scope of this proposal but also an asset held by the company funded by this proposal.
We’re offering a very attractive $12mn valuation to Scroll because we believe Scroll to be a more aligned funder with our dual goals of delivering a financially viable product while also transforming governance for the better. You can read about our decision to decline (at least for now) the BlueYard funding here. TLDR: we want the alignment to do this right, even if that means taking a slightly longer route.
Evaluation
We request that the Scroll Governance team evaluate the successful completion of the deliverables of this proposal. Below are further details for accountability.
What change do we want to see in the world?
We want to create governance systems where participants are incentivized to contribute valuable information and evaluate proposals based on merit rather than politics, leading to better collective decisions in DAOs, scientific funding, and other institutional contexts.
How will we know that change has occurred?
Milestone 1: Mathematical proofs demonstrate that Carroll Mechanisms achieve incentive compatibility and information efficiency under realistic conditions, with formal analysis showing theoretical advantages (and potential vulnerabilities) over current voting and futarchy systems.
Milestone 2: Complete theoretical framework with optimal design parameters and proven mathematical properties that provide clear specifications for practical implementation.
Milestone 3: Implementation into the Negation Game with user-friendly UI, user test and positive evaluation from users. Desire to use the Negation Game in Scroll for proposal evaluation and rewarding delegates.
Success Metrics
Mathematical Properties:
- Formal proof of incentive compatibility (honest participation is the Nash equilibrium)
- Theoretical demonstration of superior information aggregation compared to existing systems
- Mathematical analysis of robustness against strategic manipulation
Academic Validation Test:
- Research suitable for peer-reviewed publication in mechanism design or governance journals
- Validation by independent experts in game theory and mechanism design
- Clear implementation pathway with mathematically-grounded parameter recommendations
Practical Readiness:
- Theoretical framework complete enough to guide system development
- Formal specifications that translate abstract mechanisms into implementable designs
- Positive assessment from governance practitioners and potential adopters
Marketing and Community Impact:
- Successful usability test with delegates and foundation. Subjective feedback showcases: i) The mechanism is usable within the Negation Game. ii) It creates tangible value (better info, clearer merit evaluation). iii) It positively influences delegate behavior and perception of reward fairness/alignment.
- Education campaign for delegates and for the broader community.
- Usage of the NegationGame in Scroll for proposal evaluation and delegate rewards.
Conclusion
This proposal enables Scroll DAO to pioneer the next generation of governance systems while generating potential returns on investment. The implementation addresses fundamental challenges of DAO governance that also limit Scroll’s DAO — from delegate accountability and incentive misalignment to the limitations of voting-based systems and futarchy.
Why This Matters for Scroll:
The current delegate model suffers from apathy, conflicts of interest, and subjective reward allocation. Manual approaches to governance incentives are inherently biased, while AI-based alternatives face critical limitations in data availability and learning capabilities. Carroll Mechanisms offer a third path - enabling human-AI collaboration in governance, and aligning incentives without replacing human judgment.
Strategic Value:
By funding this research, Scroll gains: better governance, positioning as a governance innovation leader, potential ROI through the investment structure, and transaction fees from resulting governance tooling. The research addresses a greater than $1.2 trillion market opportunity spanning DAO governance, scientific funding allocation, and broader institutional decision-making.
Execution Excellence:
The team combines world-class academic expertise (Dr. Brown’s recognized leadership in mechanism design with NSF CAREER and AFOSR awards) with practical implementation experience (Connor McCormick’s work on collective intelligence protocols, Kaden Bilyeu’s engineering leadership). The phased approach allows for early validation before full adoption, with rigorous academic publication standards ensuring quality.
Expected Impact:
Success will create a governance system where participants are incentivized to contribute valuable information and evaluate proposals based on merit rather than politics. Delegates get fair rewards and Scroll DAO gets thorough decisions.
), so our instinct is always to push toward real-world validation sooner. That said, we also recognize that mechanism design is a special case where some longer upfront modeling and a different methodology can actually save you from a lot of costly missteps later on.