This document outlines the key strategies for optimizing the ZEROBASE economic model. The approach includes visual enhancements, mathematical modeling, and economic model improvements to ensure sustainable growth and efficiency.
The token release curve illustrates the supply increase over time. Using a sigmoid (S-curve) release function instead of a linear release can help reduce early inflation pressure while accelerating release in later stages before stabilizing. This approach balances early incentives with long-term supply management.
Using token release plans and burn mechanisms, we can predict future circulating supply and inflation rates. Inflation rates are expected to decrease over time as token release slows. This visualization helps monitor inflation levels and adjust policies like buybacks and burns accordingly.
A Sankey diagram will effectively illustrate how funds move within the ecosystem, including:
- Foundation operations
- Token buybacks & burns
- Leverage strategies
- Staking rewards (ZBI index)
- Strategic reserves
This visualization highlights major fund flows and identifies potential inefficiencies in the economic model.
To assess decentralization in governance, we can visualize voting power distribution using:
- Lorenz curve for voting power concentration
- Pie charts showing team, investor, and community holdings
- Bar charts representing the percentage of active voters
Using game theory, we analyze optimal incentive structures for staking and governance participation. The goal is to ensure staking is the dominant strategy for rational participants.
- Increasing cooperative incentives: Time-weighted staking rewards to encourage long-term commitment.
- Reducing exit incentives: Implementing withdrawal penalties or burned stakes to discourage premature exits.
- Boosting governance participation: Assigning higher governance weight to long-term stakers.
A Markov chain can model user transitions between different states:
U0: Not holding tokensU1: Holding tokensU2: Staking tokensU3: Participating in governance
By analyzing transition probabilities, we can optimize incentives to increase staking and governance engagement.
Monte Carlo simulations help predict future token price movements and circulating supply under different economic conditions. Using a geometric Brownian motion model, we can:
- Simulate multiple price trajectories
- Assess the probability of extreme price events
- Optimize supply adjustments to maintain stability
- Extending release schedules: Reducing initial emissions while increasing later-stage releases.
- Performance-based vesting: Adjusting token release based on adoption metrics like TVL and transaction volume.
- Adaptive supply mechanisms: Dynamically adjusting releases based on demand and inflation.
- Dynamic burn rates: Adjusting token burns based on inflation and market conditions.
- Transaction fee burns: Implementing an Ethereum-like burn model (EIP-1559) to balance issuance and deflation.
- Automated buybacks: Using a portion of revenue for periodic buybacks and burns.
- Elastic issuance: Adjusting token emissions based on demand-supply dynamics.
- Quarterly buybacks: Utilizing 20% of revenue for buybacks to reduce circulation.
- DAO governance on supply adjustments: Allowing the community to vote on inflation control measures.
- Quadratic voting: Reducing the influence of large stakeholders and enhancing smaller holders' governance power.
- Reputation-based governance: Incorporating reputation scores based on contributions to weigh votes.
- Delegated voting: Allowing users to assign their votes to trusted representatives to increase participation.
By implementing these enhancements in visualization, mathematical modeling, and economic policies, We want ZEROBASE have a more transparent, efficient, and adaptive economic model. These improvements ensure sustainable tokenomics, reduce inflation risks, and foster a robust governance framework for long-term success.