Purpose of This Proposal
This proposal presents an Ecosystem Value Flow design for the Allo Protocol, integrating a dynamic tokenomics model ($ALLO) and reputation-based governance to optimize capital allocation, contributor incentives, and strategic decision-making. Additionally, this design incorporates CollabBerry’s Sweat Equity model to enable fair compensation and long-term alignment among contributors.
I. $ALLO Tokenomics: Minting, Staking, and Burning
The $ALLO tokenomics model is designed to dynamically adjust supply based on value flow within the ecosystem, ensuring sustainability and incentivizing long-term participation. Minting occurs when new capital enters the system, either through funders depositing capital, contributors logging Sweat Equity via CollabBerry, or partner DAOs integrating their treasury into Allo Protocol. Each of these minting events follows a formula that considers the source of value, applying different minting ratios to balance token issuance with economic activity. To prevent inflation, vesting mechanisms may apply to certain minted tokens, particularly for contributors earning $ALLO through work-based compensation.
The scope of this doc is limited to a Hight Level (not deep) design of the tokenomics and economical flows of the inner/outer value loops, technicalities around primary market makers (aka bonding curves) for minting and burning policies are over-simplistic and shall be design including a proper computer aided design modeling.
1. Dynamic Minting: How $ALLO Enters Circulation
$ALLO is minted based on value entering the Allo ecosystem through:
- Funders Allocating Capital → Deposits trigger minting proportional to capital locked.
- Contributors Logging Sweat Equity → Work done via CollabBerry is tokenized as $ALLO.
- Projects Staking Capital → Funded projects must stake a portion of $ALLO.
- Partner DAO Treasury Allocations → Strategic integrations increase $ALLO supply.
Since different types of value enter the system, we need separate minting formulas for each case:
(A) Funders Allocating Capital
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When a funder deposits X USDC into an Allo pool, a proportional amount of $ALLO is minted.
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Example Formula:M=X×Rf
M=X×RfM = X \times R_f
Where:
- M = $ALLO minted
- X = USDC deposited
- R_f = Minting ratio for funders (e.g., 0.8x or 1.2x)
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Example: If R_f = 1.0, depositing $100,000 USDC mints 100,000 $ALLO.
(B) Contributors Logging Sweat Equity
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When work is logged in CollabBerry’s Sweat Equity model (see Compensation Algorithm mechanism), an equivalent amount of $ALLO is minted.
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Formula: M=Vs×Rs
M= Vs×RsM = V_s \times R_s
Where:
- M = $ALLO minted
- V_s = Sweat equity value in USD (e.g., $2,000 worth of work)
- R_s = Minting ratio for sweat equity (can be adjusted for inflation control)
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Example: If R_s = 0.9, and a contributor logs $2,000 in work, they receive 1,800 $ALLO.
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Why? This accounts for a slightly lower minting rate to reflect long-term sustainability.
(C) Projects Staking Capital
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Some projects might be required to lock capital in Allo pools before receiving funding.
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We can mint extra $ALLO for projects that stake their capital for a long period.
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Example Formula:M=S×Rp×T
M=S×Rp×TM = S \times R_p \times T
Where:
- M = $ALLO minted
- S = Amount staked in USDC
- R_p = Minting ratio for project staking (e.g., 0.5x)
- T = Time multiplier (longer stakes get more $ALLO)
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Example: A project stakes $10,000 USDC for 12 months → earns 5,000 $ALLO if R_p = 0.5x.
(D) Treasury Allocations from Partner DAOs
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If a DAO partner integrates its treasury into Allo, we mint $ALLO as an incentive.
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Formula:M=At×Rd
M=At×RdM = A_t \times R_d
Where:
- A_t = Allocated treasury in USD
- R_d = Minting ratio for partner DAOs
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Example: A DAO contributes $50,000 to an Allo pool with R_d = 1.1 → $55,000 in $ALLO is minted.
3. Vesting Considerations
- Funders: No vesting needed; they receive minted $ALLO immediately.
- Contributors: Work-based minting could vest over time (e.g., 6-12 months) to prevent sell pressure.
- Projects: If they receive extra minted tokens, they may need to stake or vest them to prevent immediate dumping.
4. Preventing Supply Inflation
To prevent excessive minting, we can introduce:
- Dynamic Minting Ratios: Adjust R_f, R_s, R_p, and R_d based on market conditions.
- Cap on Yearly Minting: Limit total $ALLO minted per year (e.g., max 10% growth).
- Counterbalance with Burning: Ensure burns happen when capital exits.
2. Staking: Locking Value for Governance & Yield
Staking happens when a participant voluntarily locks $ALLO tokens for a specific duration to receive rewards or unlock protocol benefits. We’ll define staking mechanisms for funders, projects, and contributors. Yield and Slashing mechanisms for governance based staking are presented after the final of this chapter, still, these might have further sense in the Governance chapter itself.
- Funders stake $ALLO to earn yield and secure ecosystem alignment.
- Projects stake $ALLO as a commitment to completing milestones.
- Contributors stake $ALLO to boost governance influence and long-term rewards.
- Dynamic staking multipliers encourage longer-term commitment.
(A) Funders Staking for Yield
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Funders who allocate capital can stake $ALLO to earn a yield (paid in additional $ALLO or a share of protocol fees).
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The longer they lock their tokens, the higher the yield.
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Formula:Ry=S×(1+T×Y)
Ry=S×(1+T×Y)R_y = S \times (1 + T \times Y)
Where:
- R_y = Rewarded $ALLO
- S = Staked amount
- T = Lock duration in months
- Y = Yield rate per month
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Example:
- A funder stakes 10,000 $ALLO for 12 months, with Y = 0.02 (2% monthly).
- They earn 2,400 additional $ALLO over the year.
(B) Projects Staking to Unlock Funding
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Projects that receive funding must stake a portion of $ALLO to align incentives.
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If they complete milestones successfully, they get their stake back + bonus.
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If they fail or abandon the project, a portion is burned or redistributed.
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Formula:Sp=0.1×F
Sp=0.1×FS_p = 0.1 \times F
Where:
- S_p = Required stake
- F = Funding amount received
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Example: A project receiving $50,000 USDC in funding must stake 5,000 $ALLO.
(C) Contributors Staking for Governance Influence
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Contributors who earn $ALLO via Sweat Equity can stake their tokens to gain governance influence.
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This creates a meritocratic model, where those who contribute more have a larger say.
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Staking boosts decision-making weight, preventing plutocracy (richest holders dominating).
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Formula:W=Sc×T
W=Sc×TW = S_c \times T
Where:
- W = Governance weight
- S_c = Staked $ALLO
- T = Staking duration (longer stakes increase weight)
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Example: A contributor stakes 2,000 $ALLO for 6 months, earning 12,000 governance points if T = 6x multiplier.
(D) Staking to Unlock Special Privileges
- Long-term stakers might gain perks like:
Priority access to funding rounds- Lower fees on Allo services
- Boosted rewards in capital distribution
Dynamic Staking Multipliers
We can adjust staking rewards based on:
Market conditions (if inflation is high, increase staking incentives).
Token supply constraints (if supply is high, encourage more staking).
Participant type (funders may get different rates than contributors).
Incentives for Staking $ALLO in Governance
To ensure that staking $ALLO for governance is meaningful, secure, and resistant to manipulation, we need a balanced incentive system that combines rewards (yield) and penalties (slashing).
Key Goals:
Encourage long-term participation in governance.
Prevent stake-based governance abuse (whale attacks, vote-buying, sybil attacks).
Align incentives with DAO sustainability.
Incentives for Staking $ALLO in Governance (Yield Mechanisms)
Why Would Someone Stake for Governance?
Stakers commit $ALLO to gain influence in governance, but they should also receive compensation for locking capital.
Yield-Based Incentives
Reputation Boost for Governance Stakers → Long-term stakers earn higher reputation weight in deliberation.
Revenue-Sharing Model → A % of Allo DAO protocol fees are distributed to governance stakers.
Staking Tiers with Higher Benefits → Longer staking = More governance influence + financial rewards.
Governance Bonds (Locked Staking) → Stakers get governance NFTs representing their governance power based on stake duration.
Example:
- Alice stakes 10,000 $ALLO for 6 months → Earns protocol fee rewards + higher reputation weighting.
- Bob stakes 500 $ALLO for 1 month → Earns lower rewards & has less influence in long-term governance.
Penalties for Malicious Governance Behavior (Slashing Mechanisms)
Why Slashing?
Without a penalty system, malicious actors could stake, disrupt governance, and unstake with no consequences.
Slashing Triggers & Governance Penalties
If a participant is flagged for governance abuse (e.g., sybil attack, spam, vote selling) → They lose a portion of their staked $ALLO.
If a participant stakes but remains inactive → Slow reputation decay reduces their governance power over time.
If a participant deliberately disrupts governance (spam proposals, delaying tactics) → A small fee is deducted from staked funds.
Example:
- Charlie stakes $20,000 $ALLO but submits spam proposals → Loses 5% of his stake.
- Dana stakes $5,000 $ALLO but doesn’t participate in governance for 6 months → Her governance influence decays over time.
Making Staking Dynamic: Adaptive Governance Incentives
To balance incentives, we can adjust staking rewards & penalties dynamically based on DAO activity & token supply.
If governance participation is LOW → Increase rewards for stakers.
If governance abuse increases → Raise slashing penalties.
If too many tokens are locked in governance → Lower yield to prevent overconcentration.
Example:
- If only 5% of $ALLO supply is staked, governance rewards increase to attract more participation.
- If 50%+ of $ALLO is locked in governance, staking yields decrease to prevent governance over-centralization.
3. Burning: Ensuring Sustainable Supply
Now that we have minting (value entering) and staking (value locking), we need to design burning (value exiting) to ensure a sustainable token supply. Burning will act as a deflationary force, preventing oversupply while aligning incentives. Burning happens when value is extracted from the Allo ecosystem. This ensures that $ALLO supply adjusts dynamically based on real economic activity.
- Capital Withdrawals → $ALLO is burned proportionally when funds exit.
- Service Fees → % of fees collected is permanently removed.
- Contributor Cash-Outs → When sweat equity is converted to stablecoins, a portion is burned.
- Unallocated Grants & Expired Treasury → Unused tokens are removed from circulation.
(A) Capital Withdrawals (Funders & Projects)
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When funders or projects withdraw capital, a portion of their $ALLO is burned.
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This ensures that tokens are not just farmed and dumped.
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Formula:Bw=W×Rw
Bw=W×RwB_w = W \times R_w
Where:
- B_w = Burned $ALLO
- W = Amount withdrawn (in USDC)
- R_w = Burn rate (e.g., 0.5% to 2%)
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Example: A project withdraws $10,000 USDC → If R_w = 1%, then 100 $ALLO is burned.
(B) Service Fees on Transactions
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Allo already charges a protocol fee (0.5% on capital flows, 5% on services).
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We can burn part of these fees in $ALLO to offset inflation.
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Formula:Bf=F×Rf
Bf=F×RfB_f = F \times R_f
Where:
- B_f = Burned $ALLO
- F = Total fees collected
- R_f = Burn rate on fees
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Example: If Allo collects $50,000 in service fees, and 20% is burned, then 10,000 $ALLO is destroyed.
(C) Contributor Cash-Outs
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When contributors convert their $ALLO into USDC or stablecoins, a portion is burned.
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This prevents contributors from extracting all value without contributing back.
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Formula:Bc=C×Rc
Bc=C×RcB_c = C \times R_c
Where:
- B_c = Burned $ALLO
- C = Cash-out amount
- R_c = Burn rate on cash-outs (e.g., 2% to 5%)
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Example: A contributor converts $5,000 worth of $ALLO into USDC → If R_c = 3%, then 150 $ALLO is burned.
(D) Unused Treasury Funds & Expired Grants
- If a project fails to use its allocated funds, a portion of its $ALLO is burned.
- This discourages hoarding and rewards active participation.
- Example: A project receives $20,000 but never deploys it. After 6 months, 5% of its staked $ALLO is burned.
Dynamic Burn Adjustments
To balance supply and demand, burn rates can be dynamically adjusted based on:
Inflation Rate – If too much $ALLO is minted, increase burns.
Staking Participation – If staking is low, raise burn penalties to encourage locking.
Market Conditions – Adjust based on token liquidity and circulation.
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Supply & Demand Model for $ALLO
Now that we’ve defined minting (supply growth), staking (value locking), and burning (supply reduction), we need to ensure long-term stability for $ALLO.
Our goal is to maintain a healthy balance where $ALLO is neither:
Hyper-inflated (too much minting, making tokens worthless) Over-burned (too much burning, restricting liquidity and usability)
1. Key Supply-Demand Drivers
Supply (Minting) Increases When: Funders deposit capital → $ALLO is minted Contributors earn sweat equity → $ALLO is minted Projects stake capital → $ALLO is minted DAOs integrate treasury → $ALLO is minted
Demand (Burning + Locking) Reduces Supply When: Funders/projects withdraw capital → $ALLO is burned Allo service fees are collected → $ALLO is burned Contributors cash out their earnings → $ALLO is burned Unused treasury/grants expire → $ALLO is burned Staking locks tokens for governance and yield
2. Dynamic Balancing Mechanisms
To ensure stability, we introduce adaptive mechanisms that increase or decrease minting and burning rates dynamically based on market conditions.
(A) Minting Rate Adjustments
If too much $ALLO is entering circulation, we can:
Lower minting ratios (e.g., reduce R_f for funders) Increase vesting requirements for contributors Require projects to stake higher percentages
(B) Burning Rate Adjustments
If too much $ALLO is in circulation, we can:
Increase burn % on withdrawals and service fees Require higher burn penalties for project inactivity Introduce time-based decay (more burning if tokens remain idle)
3. Simulated Supply-Demand Scenarios
Scenario 1: Healthy Equilibrium
Assumptions:
- Funders deposit $1M USDC per year → 1M $ALLO is minted
- Contributors log $200K sweat equity per year → 180K $ALLO is minted (R_s = 0.9)
- Projects stake $500K USDC → 250K $ALLO is minted
Annual Supply Growth = 1.43M $ALLO
Annual Reductions:
- 5% of capital exits ($1.7M in withdrawals) → 85K $ALLO burned
- Service fees ($100K fees collected) → 20K $ALLO burned
- Contributor cash-outs ($400K in cash-outs, R_c = 3%) → 12K $ALLO burned
- Unused grants/stale tokens expire (~50K burned/year)
Annual Supply Reduction = 167K $ALLO + 300K $ALLO staked
Result: Net $ALLO Supply Growth = 1.43M - (167K burned + 300K staked) = ~960K per year (~9.6% growth rate).
Scenario 2: Too Much Minting (Inflation Risk)
Assumptions:
- Large funder inflow ($5M instead of $1M)
- Sweat equity increasing 2x
- No increase in withdrawals or burning mechanisms
Risk: Net growth rate jumps to 30%+ Solution:- Lower R_f and R_s to slow minting
- Increase R_w (burn on withdrawals)
- Require projects to stake larger portions of their funds
Scenario 3: Too Much Burning (Supply Squeeze)
Assumptions:
- Funders withdraw aggressively (40% capital exits per year)
- Contributor cash-outs spike 3x
- High staking exit rate (unstaking leads to large token dumps)
Risk: Net supply declines too fast, causing token scarcity Solution:- Lower R_w (withdrawal burn) to retain more $ALLO
- Reduce service fee burn %
- Incentivize staking retention (longer locks = higher rewards)
II. Governance Design for Allo DAO
This proposal tackles governance in a very diverse and innovative way, willing to place attention as the main weight primitive to individuals to signal their intentions of decisions over the collective while assuring every shareholder providing the required attention parameters to Allo DAO will be equitably considered for decision making in a Deliberative Formal Consensus (and AI assisted) mechanism.
Assuming the Allo Protocol itself solves the issues for allocating capital within Allo DAO, we won’t be scoping Governance over capital allocation in this document, so this document only entails governance decisions that don’t scope capital allocation, still, strategic decisions that are linked to a subsequent capital allocation might be approved or vetoed with this proposed mechanism. Also, it’s important to notice that Formal Consensus might also be useful in de decision of allocate strategic funding, still, in the decision of agreeing on the amount of funds to be allocated, we are sure Allo Protocol is already on a more efficient and effective track.
Our scope includes tho a conviction-voting mechanism for deciding on priorities of governance proposals to be tackled by the DAO in its Formal Consensus deliberated structure governance model, where decisions will be done by reputation-based multi-stakeholder agents in sync and async structures. The following are our design principles
Aligns incentives between funders, contributors, and projects.
Distributes decision-making fairly without plutocracy (big holders dominating).
Integrates AI & social technology for better deliberation (Harmonica.chat & Formal Consensus).
Uses Allo Protocol strategies for on-chain allocation while allowing off-chain strategy setting.
Regarding Formal Consensus: it is a governance system being promoted since the 90’s that deeply integrate Conflict Transformation as a part of the governance process itself. Formal Consensus has a clearly defined structure. It requires a commitment to active cooperation, disciplined speaking and listening, and respect for the contributions of every member.
Some Characteristics (see more On Conflict and Consensus):
- The least violent decision making process.
- The most democratic decisionmaking process.
- Based on the principles of the group.
- Desirable in larger groups.
- Works better when more people participate.
- NOT inherently time-consuming.
- Cannot be secretly disrupted.
Allo DAO Governance will then be subjected to continuous social deliberation of those proposals that have been pre-approved by an $ALLO based Conviction Voting system.
Conviction Voting as a Governance Proposal Curation Mechanism
Conviction Voting (CV) was presented by Commons Stack as a novel mechanism that offers a novel decision making process that funds proposals based on the aggregated preference of community members, expressed continuously in 2019 and since then has been utilized in communities like 1Hive, Commons Stack itself and Token Engineering Commons (TEC).
In the Emergence of the Crypto Commons, a PhD Dissertation by Felix Fritsch, the author explores how decentralized governance mechanisms like Conviction Voting have been applied in real-world communities. He specifically examines TEC, where Conviction Voting was used for funding prioritization and governance, allowing members to allocate their voting power over time instead of in a single event. This model aimed to reduce plutocratic control, encourage sustained participation, and improve the decision-making process.
However, the study also highlights challenges such as low voter engagement, governance inefficiencies, and the risk of whale influence over long-term decisions. Despite these limitations, Conviction Voting has proven effective as a proposal curation tool, allowing communities to surface high-priority issues for further deliberation.
In practice, this means every proposal will be submitted to a CV process on which all proposals will share the same threshold, ideally, an AI agent could predict how much collective attention a proposal might demand and adjust the threshold around how opinionated or conflictive a proposal might be, but we won’t be going into that in this document.
By integrating Conviction Voting as a filtering mechanism—rather than a final decision-making tool—Allo DAO can leverage its strengths while mitigating its weaknesses. Proposals gaining significant community conviction can be structured, refined, and deliberated in a Formal Consensus process, assisted by AI to ensure rational discourse, bias detection, and structured decision-making.
This hybrid approach balances decentralized participation with structured deliberation, ensuring that governance remains both inclusive and effective.
Formal Consensus Assisted with AI & Reputation-Based Multi-Stakeholder Deliberation
To ensure that governance decisions are inclusive, structured, and resistant to plutocratic influence, we integrate Formal Consensus as the final deliberative process—assisted by AI and weighted by a reputation-based multi-stakeholder system.
While Conviction Voting (CV) serves as a filtering mechanism, surfacing governance priorities, Formal Consensus ensures that only well-reasoned, high-impact decisions get enacted. AI assistance further enhances the process by structuring discussions, detecting biases, and facilitating resolution paths.
Why Formal Consensus?
Formal Consensus, originally outlined by C.T. Butler, is a non-hierarchical decision-making process designed to maximize agreement while preventing majoritarian rule. Unlike token-based voting, which forces winners and losers, Formal Consensus seeks collective alignment through structured deliberation.
To ensure broad participation, Allo DAO’s Formal Consensus is reputation-weighted—meaning different stakeholder classes (e.g., funders, contributors, project teams, governance stewards) have a proportional voice based on their contributions and engagement.
This system avoids:
Wealth-based plutocracy (where token holders dominate)
Governance gridlock (where endless debates prevent progress)
Low engagement & poor decision quality (as seen in pure voting systems)
The 3 Phases of AI-Assisted Formal Consensus
Sense-Making Phase: Understanding the Issue
Goal: Gather context, define the problem, and align stakeholders.
Process:
- AI aggregates relevant discussions & Conviction Voting results to highlight priority topics.
- Reputation-weighted stakeholders participate in structured deliberation, focusing on framing the issue objectively.
- AI detects bias, misinformation, and missing perspectives, ensuring a holistic understanding of the proposal.
Benefit: Prevents low-quality, rushed proposals from reaching final decision-making.
Problem Definition Phase: Structuring the Proposal
Goal: Clearly define the governance action, considering risks, trade-offs, and implementation details.
Process:
- Stakeholders submit converns, arguments, counterarguments, and refinements.
- AI clusters similar perspectives and ensures that opposing viewpoints are given space.
- Reputation-based weighting ensures multi-stakeholder representation (investors, contributors, project teams, etc.).
- AI models potential outcomes using historical data and prior governance decisions.
Benefit: Ensures proposals are well-reasoned and multi-perspective before final agreement.
Solution Definition Phase: Reaching Consensus
Goal: Establish collective agreement on the final governance action.
Process:
- AI structures compromise solutions where applicable.
- If disagreements persist, AI suggests modifications or merges proposals.
- Final objections are reviewed, ensuring valid concerns are addressed.
- If no objections remain, the proposal achieves consensus and is enacted.
Benefit: Avoids the flaws of simple voting, ensuring that final decisions are broadly supported and well-refined.
Closing Options in AI-Assisted Formal Consensus
At the end of the Solution Definition Phase, one of the following outcomes is chosen:
Agreement Reached → Enact the Decision
- The proposal is implemented as consensus has been reached.
Modify & Retry → Adjust & Return to Deliberation
- If strong concerns exist, the proposal is revised and reintroduced.
- AI assists in merging similar proposals and suggesting refinements.
Blocking Objections → Final Arbitration
- If fundamental disagreements persist, the issue enters a structured dispute resolution process.
- AI ensures that objections are legitimate (not personal or irrational roadblocks).
Proposal Withdrawn or Fails
- If there is insufficient support or fundamental misalignment, the proposal is archived.
Key Improvement:
- Conviction Voting Results Can Inform Closing Options → If a proposal with high conviction fails in Formal Consensus, it can be modified and fast-tracked for resubmission rather than being completely discarded.
Introducing Reputation Weighting in Formal Consensus?
While Formal Consensus does not rely on voting, the process in Allo DAO might a more require structured participation to ensure that:
1. Governance Cannot Be Captured by Sybil Attacks
- Without any weighting mechanism, bad actors could flood the deliberation process with fake participants to stall decision-making.
- Reputation-based weighting ensures that those who have contributed meaningfully to the DAO (funders, builders, governance stewards) have a structured role in shaping discussions.
2. Ensures a Balanced Multi-Stakeholder Representation
- Egalitarian participation in Formal Consensus remains intact, but different and categorized stakeholder groups contribute differently to decision-making.
- Instead of token-based plutocracy, participation is shaped by real engagement (e.g., contributors’ work, project teams’ execution, funders’ long-term support).
3. Prevents Deliberation From Being Overwhelmed by Passive or Misaligned Participants
- Some governance decisions require deep expertise (e.g., treasury adjustments, protocol security).
- AI-assisted filtering ensures that qualified, active stakeholders contribute meaningfully, preventing governance inefficiencies.
How Reputation-Based Multi-Stakeholder Deliberation Works
Instead of one-token-one-vote, Formal Consensus weighs participation based on stakeholder contributions:
| Stakeholder Class | Decision Influence Factor | Reputation Source |
|---|---|---|
| Funders | Weighted by capital provided over time | $ALLO staked & treasury impact |
| Contributors | Weighted by work contributed & reputation score | Sweat Equity + Peer validation |
| Project Teams | Weighted by milestones achieved & efficiency | On-chain success metrics and/or RPGF |
| Governance Stewards | Weighted by past governance participation | History of deliberation & impact |
| DAO Capital Providers | Weighted by their participation | History of rounds and deliberation on their own reputation |
How This Works in Practice
Equal Deliberation Access → Every participant can join discussions, submit insights, and propose modifications.
Reputation & Stakeholder Role Influence → Helps guide & structure deliberation, ensuring that those with proven contributions play an active role.
AI Ensures Fairness → Detects bias, spam, or manipulation, ensuring that no stakeholder group dominates the process.
Key Difference from Traditional Governance:
Unlike voting-weighted governance, weight in Formal Consensus only applies to structuring deliberation, NOT to final decision power. Final decisions must still meet consensus-based approval.
Key Partners & Tools
CollabBerry → Sweat Equity compensation model.
Harmonica 
→ AI-powered collective sensemaking & deliberation.
Coh3rence → Socialtech & (Regenerative) Governance Deliberation - Formal Consensus Facilitation
Token Engineers, Devs, VCs, and all Gov nerds that want to try new ways to DAO.
