Community Rewards and Staking
Last updated
Last updated
At Social AI, we have developed a robust community reward and staking program that allows users to earn tokens based on their contributions and engagement on the platform. The program operates as follows:
A percentage of the total token supply is allocated to the reward pool. This pool serves as the source of rewards for eligible users. The distribution is determined based on a combination of factors such as the quality of content, user engagement, and stake in the ecosystem.
Users can earn rewards by actively participating in platform activities, including content creation, sharing, and engaging with others. The specific formula for calculating rewards is as follows:
The Quality Score represents the perceived quality of the content or action, the Engagement Score measures the level of user engagement, Stake represents the number of powered-up tokens, and Total Pool refers to the current size of the reward pool.
Users can stake their tokens to increase their "power" within the ecosystem. Powering up tokens demonstrates long-term commitment and enhances the user's influence on platform governance and reward distribution. The formula for calculating the power score is as follows:
To encourage continuous engagement, a decay mechanism is implemented for users who are inactive for a certain period. Users who are inactive for a specific duration experience a gradual decay of their power score.
However, users can recover their power score by actively participating on the platform for consecutive periods. The formula for power recovery is as follows:
The Recovery Rate represents the rate at which power is regained for each active period.
Users with higher power scores can access additional opportunities and benefits within the platform. These may include increased voting power in governance decisions, priority access to certain features, and exclusive events or promotions eligibility.
By implementing this community rewards and staking program, Social AI aims to incentivize active participation, content creation, and engagement while ensuring a fair and transparent distribution of rewards within the ecosystem.
Content Quality (Q): Evaluated using a weighted sum of metrics such as upvotes (U), comments (C), and shares (S), with weights wu, wc, and ws respectively.
User Engagement (E): Modeled as a function of active days (A) and interactions (I), with parameters ɑ and 𝛽 to adjust their influence.
Voting Power (V): Adjusted for decay and regeneration. Initial voting power (V0) is modified based on decay rate Δ and regeneration rate ⍴ over time (t).
Decay Mechanism (D): Implemented as an exponential decay function based on days of inactivity (tinact).
Where λ controls the rate of decay.
Combining the factors into a single reward function, we use a normalization factor (N) to ensure fairness and scalability across different user activities and platform growth stages.
Where (N) is calculated based on the total engagement and quality scores across the platform to normalize the reward distribution.
