🏆Reward Model

Reward Model (Calculating Rewards)

QUJICOIN employs a unique mining-driven reward system known as prediction mining. Users on the platform can earn QUJI coins by submitting hourly predictions about the performance of leading cryptocurrencies. The accuracy of these predictions directly influences the amount of QUJI coins users receive as a reward. This model incentivizes users to contribute intellectually to the platform by providing accurate forecasts, thus aligning their interests with the platform's success.

The number of QUJI mined is directly proportional to the accuracy of predictions. The more accurate predictions you make, the more QUJI coins you can earn.

Incentive Structure: The QUJI Coin fuels the incentive structure of the "QUJICOIN" platform, rewarding users for active participation and contribution. Users participate in predictive mining to earn QUJI coins, which they can use to purchase crowdsourced wisdom or make other relevant transactions, thus contributing to the growth and sustainability of the QUJICOIN ecosystem while benefiting from the services and insights offered by the platform.

5.6.1. Calculating Rewards Based on Accuracy of Predictions

Let’s understand this with the example of Bitcoin. In this system, users make predictions about the future price of Bitcoin. The predictions are evaluated based on their accuracy within a specified margin of error. The reward users receive depends on the size of this error margin: smaller margins lead to higher rewards.

Key Concepts

• Current Bitcoin Price (P-BTC​): The current price of Bitcoin at the time of prediction.

• Predicted Price (P-predicted​): The price a user predicts for Bitcoin in the future.

• Actual Price (P-actual​): The actual price of Bitcoin at a future time (like after two hours).

• Absolute Distance (ABS Dist): The absolute difference in dollars between predicted and actual prices.

• The Radius of Error %: The percentage error derived from the absolute distance relative to the current Bitcoin price.

• Output Ratio: A coefficient used to calculate rewards based on a given radius of error.

Calculating the Absolute Distance

The absolute distance in dollars is calculated as follows:

ABS Dist = P-predicted​ - P-actual​

Determining the Radius of Error %

To calculate the radius of error percentage, we compare the absolute distance to the current Bitcoin price and express it as a percentage:

Calculating Rewards Using the Radius of Error

Once we have the radius of error (percentage), we determine the output ratio from a predefined table (see below). This ratio defines the multiplier used to calculate the reward based on a given bet.

Reward Calculation

To calculate the reward, we multiply the user's bet by the output ratio:

Ratio Reward = Bet x Output Ratio

Example Scenario:

Suppose the current Bitcoin price is USD 61,500. A user predicts that in two hours, the price will be USD 61,400. After two hours, the actual price is 61,449.2 USD. Here's how you'd calculate the radius of error and the reward:

1. Calculating Absolute Distance

ABS Dist = 61400 - 61449.2 = 49.2 USD

2. Calculating the Radius of Error %

Radius of Error % = (49.2/61500)×100 ≈ 0.08%

3. Determining Output Ratio using the predefined table

The output ratio for a radius of error of 0.08% is 1.751 (as shown in the table below)

4. Calculating Reward

If the user bets 10 QUJI Coins, the reward will be:

10 × 1.751 = 17.51 QUJI Coins

To sum up, this system calculates the radius of error percentage from the absolute distance between the predicted price and the actual price, relative to the current price of a cryptocurrency. Users who predict more accurately receive higher rewards based on the output ratio corresponding to the radius of error. This approach encourages users to make precise predictions while providing flexibility in rewards based on the margin of error.

Radius of Error %	ABS Dist $ (Example FOR BTC)	Output ratio
0.01%	6	1.88
0.02%	12	1.87
0.03%	18	1.85
0.04%	25	1.83
0.05%	31	1.81
0.06%	37	1.79
0.07%	43	1.77
0.08%	49	1.75
0.09%	55	1.73
0.10%	62	1.71
0.11%	68	1.68
0.12%	74	1.66
0.13%	80	1.63
0.14%	86	1.61
0.15%	92	1.58
0.16%	98	1.55
0.17%	105	1.52
0.18%	111	1.49
0.19%	117	1.46
0.20%	123	1.42
0.21%	129	1.39
0.22%	135	1.35
0.23%	141	1.31
0.24%	148	1.27
0.25%	154	1.22
0.26%	160	1.18
0.27%	166	1.13
0.28%	172	1.08
0.29%	178	1.02
0.30%	185	0.96
0.31%	191	0.9
0.32%	197	0.83
0.33%	203	0.76
0.34%	209	0.69
0.35%	215	0.6
0.36%	221	0.52
0.37%	228	0.42
0.38%	234	0.32
0.39%	240	0.2
0.40%	246	0.08
0.41%	252	0

Table: Intelligent Prediction Mining Reward Coefficients based on Radius of Error