Reward Rate Formula

The foundation of our system is a dynamic reward rate (ρ) that adjusts based on market activity and total budget allocation. While we use the following formula as a guiding principle, please note that specific parameters like β₀, the moving average of trading volume’s duration observed, and the adjustment factor h are initially chosen by the Mangrove’s DAO Council and may vary for different epochs.

Reward Rate Formula:

ρ(n) = ρ₀ / (1 + (V₂₄/V₀)ʰ) × (1 - β(t)/β₀)

Where:

- ρ₀ is the base reward rate (initially set by the Mangrove DAO Council)

- V₂₄ is the 24-hour moving average of trading volume (24-hour is an example)

- V₀ is our reference volume (for eg. $1M) at which the reward rate halves

- h is an adjustment factor that controls how quickly rewards decrease with volume

- β(t) is the total MGV distributed up to time t

- β₀ is the maximum MGV allocation for the epoch (set by Mangrove’s DAO Council)

This formula has two main components:

• Volume Adjustment: ρ₀ / (1 + (V₂₄/V₀)ʰ) :

  • Reduces rewards as trading volume increases

  • Helps distribute rewards more evenly across time

  • Encourages activity during lower-volume periods

Budget Control: (1 - β(t)/β₀) :

• Gradually reduces rewards as we approach the epoch's budget limit

  • Ensures we don't exceed the planned token distribution

  • Provides a smooth transition as the epoch's allocation is consumed

Important Considerations:

• Parameter selection:

  • β₀, V₂₄, and h are selected by Mangrove’s DAO Council for each epoch to align with platform goals and the incentives program’s principles.

  • Consequently, ρ will be communicated as a value during the first epochs.

• Principle that will be applied:

  • When trading volume is high, the number of incentives per dollar traded will decrease.

  • When platform volume is low, the incentives to be earned will be higher to bootstrap activity.

  • This mechanism is designed to favor genuine activity on the platform and reward truly active participants.

This approach ensures that:

• Token distribution remains within planned limits:

  • By controlling β₀, we manage the total incentives distributed during an epoch.

• Rewards decrease gradually:

  • The reward rate reduces smoothly rather than stopping abruptly, providing predictability.

  • Users can anticipate how rewards might change as the epoch progresses.

• Encourages genuine activity:

  • By adjusting rewards based on volume, we incentivize real, meaningful engagement on the platform.

  • Higher rewards during low-volume periods encourage participation when it benefits the network most.