> For the complete documentation index, see [llms.txt](https://aetherservice.gitbook.io/about/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aetherservice.gitbook.io/about/incentive-design-and-network-economics/community-growth-and-expansion-model.md). # Community Growth & Expansion Model The Aether referral system is an economically sustainable, technically secure, and scalability-oriented growth mechanism based on verifiable network contribution from referred devices. The system is designed to ensure that referral rewards are always proportional to the actual utility generated by referred nodes within the infrastructure, rather than simple user acquisition or registration activity. ### 1. Core Reward Formula Each time a referred node generates a validated **Proof-of-Bandwidth Contribution (PoBC)** report, the referrer receives a portion of the effective contribution generated by that node. The reward is strictly tied to verified network activity and not to onboarding events. Referral rewards are calculated using the formula: ``` R = ER × (B × Q × S) ``` Where B represents the confirmed volume of transmitted bytes, Q represents the channel quality coefficient reflecting RTT stability, jitter, and packet loss characteristics, and S represents the node stability coefficient, including uptime consistency and predictability of bandwidth availability. ER defines the Effective Rate, which determines the proportional share allocated to the referrer. This structure ensures that rewards are generated only from real, validated network contribution, eliminating incentives based on artificial or inactive referrals. ### 2. Effective Rate (ER) The Effective Rate is composed of multiple independent components that determine the final referral percentage: The base referral rate is defined as **ER\_base = 12%**, forming the foundational incentive for network expansion. A stability-based component increases rewards when referred nodes demonstrate consistent long-term activity. After 7 days of stable operation, an additional +2% is applied, and after 30 days, the bonus increases to +5%, forming the **ER\_stability = 2–5%** range. A growth scaling component provides additional incentives based on the number of active referrals maintained by the user. This scaling increases progressively, starting from no bonus for 1–5 referrals, up to +5% for networks exceeding 100 active nodes. Combined, the maximum effective referral rate reaches: ``` ER_total = up to 22% ``` This structure balances early participation incentives with long-term network sustainability. ### 3. Demand Multiplier (DDM) To align referral rewards with real-time network demand, a dynamic multiplier **DDM ∈ \[1.0; 1.5]** is applied. This coefficient adjusts rewards based on regional bandwidth demand, task complexity, and overall infrastructure load. The final referral reward is calculated as: ``` R_final = (ER_total × B × Q × S) × DDM ``` This ensures that referral incentives scale naturally with actual network usage patterns rather than static emission schedules. ### 4. Early Referral Epoch Bonus To accelerate initial network formation, a temporary early-stage incentive system is applied. During the initial growth phase, the first 10 referred nodes per user receive an additional +3% reward increase per node. Additionally, users who participate during the early network bootstrap phase (first 1000 participants) receive a permanent +2% increase to their Effective Rate. These bonuses are fixed at the account level and remain independent of future network conditions. ### 5. Technical Mechanisms to Prevent Manipulation The referral system incorporates multiple layers of anti-abuse protection designed to eliminate artificial inflation of referral rewards. Activation of referral rewards is strictly dependent on successful completion of **PoBC cycles**, ensuring that inactive or fake nodes cannot generate value. Network fingerprint analysis is used to validate device uniqueness, including NAT classification, TTL behavior patterns, routing consistency, and latency entropy profiles. This makes simulated or virtualized node clusters significantly more difficult to spoof. Traffic behavioral signatures are also analyzed, comparing actual load patterns with expected statistical models derived from RBW forecasts, packet distribution curves, and temporal activity correlations. Any deviation beyond acceptable thresholds results in reward suppression for the affected period. Additionally, IP and subnet overlap detection is applied to prevent self-referral abuse or clustered farming behavior. Nodes operating under shared network environments may have reduced weighting or be excluded from referral calculations. ### 6. Economic Sustainability of the Model The referral mechanism is designed to support sustainable network expansion without introducing uncontrolled emission pressure. Incentives are directly tied to verified contribution rather than user acquisition volume, ensuring that growth remains aligned with actual infrastructure usage. The model prioritizes long-term node activity, quality of connections, and geographic distribution over short-term scaling strategies. Reward boosts are only activated when they correspond to measurable network utility, preventing inflationary distortions. ### Summary The Aether referral system functions as a contribution-aligned growth mechanism where participants can earn up to **12–22% of verified referral contributions**, with additional demand-based multipliers reaching up to 1.5× under high-load conditions. The system is designed to reward stable, high-quality network participation while maintaining strict protection against manipulation and synthetic activity. Rather than serving as a marketing layer, the referral structure acts as an extension of the core infrastructure economy, reinforcing the long-term scalability and resilience of the Aether network. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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