Earning Mechanisms

Scalable Dynamic Reward System for HTE Token

HTE Tokenomic's Reward System

The HTE earning mechanism will evolve through distinct stages, aligning with the ecosystem's maturation and growth. Market stabilization and precise solution adaptation necessitate a phased approach. Currency price stability introduces inherent costs and challenges throughout this process (Yale Univeresity, Federal Reserve Bank of NY, 2011) .

First stage will be to monitor and operate individual earnings and each will have its own efffort/reward rations or earnings by "actions". Then it will move onto Unified Effort to Reward Algorithm (Scalable Standardization) for scalability and automation.

Then more progressive approach towards Dynamic Reward Adjustment Method combined with a Stamina/Daily Cap Mechanism to ensure fair token distribution across different applications within the HTE ecosystem. This system prevents reward farming inefficiencies and creates an adaptable model for future expansion, including AI-driven real-time adjustments.

Reward System Diagram

Key Challenges

• Different apps have varying levels of effort-to-reward ratios, leading users to choose the easiest method to earn HTE.

• The ecosystem needs a scalable reward mechanism that remains fair and adaptable.

• Preventing over-exploitation while encouraging balanced engagement across all platforms.

Unified Effort-to-Reward Algorithm (Scalable Standardization)

This will be the first and foremost approach that HTE will take from initial manually configured reward ratio - which will be the simplest and easiest way for the community and operations - but still has above mentioned Key Challenges to be considered.

Instead of manually adjusting each app’s reward rate, create a formula that calculates HTE rewards dynamically based on:

• Effort/time spent

• Engagement difficulty

• Market demand for each app’s rewards

Example Formula:

$$HTE_{\text{earned}} = \frac{E_{\text{effort}} \times B_{\text{difficulty}}}{R_{\text{supply}}}$$

Where:

• E= effort/time required

• B = difficulty multiplier (higher for harder tasks)

• R = real-time balancing factor (adjusts if too many people farm one app)

Whenever anew app(s) or method(s) is/are added, it automatically fits into this framework instead of requiring manual adjustments.

Dynamic Reward Adjustment Mechanism

Rewards are adjusted based on real-time user engagement and effort metrics. The base unit of reward calculation is set at with base numerals(e.g. 1 or 0.1), which will be manually converted into HTE by the project team based on market conditions.

Core Formula for Dynamic Reward Adjustment

given:

• E_effort = Effort Score based on engagement metrics

• B_difficulty = Base difficulty of the task

• R_supply = Reward supply adjustment factor based on user demand

• P_app = Participation weight (number of users engaged in the app)

HTE_{\text{earned}} = \frac{E_{\text{effort}} \times B_{\text{difficulty}}}{R_{\text{supply}} \times P_{\text{app}}} \

where:

• Higher E_effort increases reward per task.

• Higher P_app decreases rewards per user to balance supply.

• R_supply adjusts dynamically to prevent excessive inflation.

Dynamic Scaling Based on User Engagement

To prevent excessive farming of a single app, R_supply is dynamically adjusted using:

R_{\text{supply}} = 1 + \alpha (U_{\text{max}} - U_{\text{current}}) \

where:

• U_max = Maximum optimal user base per app

• U_current = Active users in the app

• \alpha = Sensitivity constant for supply adjustment

Stamina & Daily Cap Mechanism

The Stamina & Daily Cap system prevents players from excessively earning HTE beyond a reasonable effort threshold.

Stamina Calculation

• Each player starts with a fixed Stamina Pool (S_max) per day.

• Actions consume stamina points (S_consume) based on difficulty.

$$S_{\text{remaining}} = S_{\text{max}} - \sum S_{\text{consume}}$$

• Once S_remaining = 0, the player stops earning HTE until reset.

Daily Cap Implementation

A global daily cap ensures sustainable token distribution:

$$HTE_{\text{daily}} \leq \beta \times (\sum U_{\text{total}})$$

where:

• HTE_daily = Maximum HTE distributed per day

• \beta = Fixed multiplier based on supply control

• U_total = Total active users in the ecosystem

Future Expansion & AI Integration

Future iterations will include an Effort-to-HTE Formula (Weight-Based Approach) for finer reward balancing, followed by AI or Machine Learning models to automate real-time adjustments.

Conclusion

This presents HTE's tokenomics, especially the mechanism dynamics by stage as the time goes by, from various methods including basic to unified, dynamic, scalable solution for HTE token rewards, ensuring fairness and adaptability across multiple applications. The team will manually convert the base reward unit (base numerals like 1 or 0.1) into HTE based on market conditions, allowing precise control over the token economy.