Temporal Reward Adjustment Mechanism

Strategic Temporal Allocation Formula

To balance early adopter incentives with sustainable growth, Aledger implements a strategic temporal allocation formula that dynamically adjusts rewards based on participation timing, network growth, and total supply constraints.

User Reward = Base Reward × [α × Temporal Coefficient + β × Staking Ratio + γ × Network Saturation Factor]

Where:

Base Reward: Standard reward calculated from asset package APY
α, β, γ: Weight coefficients that sum to 1.0 (initially set to α=0.4, β=0.4, γ=0.2)
Temporal Coefficient: Rewards early adoption (calculated below)
Staking Ratio: Existing staking ratio coefficient from core tokenomics
Network Saturation Factor: Adjusts for network capacity constraints

Temporal Coefficient Calculation

Temporal Coefficient = 1 + 0.5 × [1 - min(User Entry Position / Adoption Threshold, 1)²]

Where:

User Entry Position: Sequential position of user registration (1 = first adopter)
Adoption Threshold: Target number for early adoption phase (initially 1,000 packages)

This creates a non-linear decay curve where:

First adopters receive up to 1.5× multiplier
Middle adopters receive gradually decreasing benefits
Late adopters (beyond threshold) receive the standard 1.0× multiplier

Network Saturation Factor

Network Saturation Factor = max(0, 1 - (Current Daily Emissions / Maximum Daily Emissions)^1.5)

This factor dynamically adjusts rewards as the network approaches maximum emission capacity:

At low emission levels (<50% of capacity): Minimal impact (~1.0×)
At medium emission levels (50-80% of capacity): Moderate reduction
At high emission levels (>80% of capacity): Significant reduction

Parameter Governance

The Temporal Reward Adjustment parameters (α, β, γ, Adoption Threshold) are subject to governance voting by node operators, with the following constraints:

Parameter adjustments limited to ±10% per quarter
14-day advance notice required for any parameter changes
Super-majority (66%) of weighted node votes required for approval

PreviousToken Supply Adjustment Mechanisms NextAeroNyx Network Points System

Last updated 6 days ago

Temporal Reward Adjustment Mechanism

Strategic Temporal Allocation Formula

User Reward = Base Reward × [α × Temporal Coefficient + β × Staking Ratio + γ × Network Saturation Factor]

Where:

Base Reward: Standard reward calculated from asset package APY
α, β, γ: Weight coefficients that sum to 1.0 (initially set to α=0.4, β=0.4, γ=0.2)
Temporal Coefficient: Rewards early adoption (calculated below)
Staking Ratio: Existing staking ratio coefficient from core tokenomics
Network Saturation Factor: Adjusts for network capacity constraints

Temporal Coefficient Calculation

Temporal Coefficient = 1 + 0.5 × [1 - min(User Entry Position / Adoption Threshold, 1)²]

Where:

User Entry Position: Sequential position of user registration (1 = first adopter)
Adoption Threshold: Target number for early adoption phase (initially 1,000 packages)

This creates a non-linear decay curve where:

First adopters receive up to 1.5× multiplier
Middle adopters receive gradually decreasing benefits
Late adopters (beyond threshold) receive the standard 1.0× multiplier

Network Saturation Factor

Network Saturation Factor = max(0, 1 - (Current Daily Emissions / Maximum Daily Emissions)^1.5)

This factor dynamically adjusts rewards as the network approaches maximum emission capacity:

At low emission levels (<50% of capacity): Minimal impact (~1.0×)
At medium emission levels (50-80% of capacity): Moderate reduction
At high emission levels (>80% of capacity): Significant reduction

Parameter Governance

The Temporal Reward Adjustment parameters (α, β, γ, Adoption Threshold) are subject to governance voting by node operators, with the following constraints:

Parameter adjustments limited to ±10% per quarter
14-day advance notice required for any parameter changes
Super-majority (66%) of weighted node votes required for approval

PreviousToken Supply Adjustment Mechanisms NextAeroNyx Network Points System

Last updated 6 days ago