Parameter Calibration

To optimize the long-term performance of the $TAKE token economy, key design parameters are subject to iterative testing, calibration, and governance-led adjustment. The objective is to identify configurations that maximize sustainable GMV growth while maintaining balance between supply-side emissions and demand-side absorption.

Three primary parameter classes are subject to calibration:

1. Buyback Allocation Ratio (θ) The proportion of platform fee revenue allocated to token buybacks. Higher values of θ increase market demand for $TAKE but reduce operational reserves. Calibration Goal: Identify the optimal θ that maximizes token price support without impairing platform operations.

2. Reward Pool Allocation Ratios (α, β, γ) The distribution of the total emission pool across Buyers (α), Sellers (β), and Evangelists (γ), where: α + β + γ = 1. These ratios determine the strategic emphasis of reward incentives. For example:

• Higher α may increase buyer retention and conversion

• Higher β may strengthen liquidity and listing velocity

• Higher γ may enhance network expansion and user acquisition

Calibration Goal: Dynamically allocate emissions to the stakeholder groups that generate the highest marginal GMV per reward unit.

3. Activity Types and Weights (Aᵢ structure) Each agent’s activity score (Aᵢ) is a weighted sum of verifiable actions. The platform continuously evaluates which behaviors most strongly correlate with GMV growth.

Examples:

• Buyer: browsing, purchasing, reviewing

• Seller: listing, completing trades, receiving positive feedback

• Ambassador: referring users, generating content, social engagement

Calibration Goal: Prioritize and reweight actions that empirically drive high-value transactions, while demoting low-signal or easily gamed behaviors.

Experimental Feedback Loop

Calibrations are conducted using live platform data, A/B testing, cohort analysis, and simulation environments. Performance metrics include:

• GMV growth

• Buyback coverage ratio

• Retention and referral rates

• Token price stability

Through this feedback-driven approach, the protocol ensures that its incentive structures remain aligned with ecosystem health, economic sustainability, and long-term token value.