🏛️Reputation and Score

How actual score is calculated

Provided data has "Event" and "Score" attached to it, score, event and network are unique to the whole platform.

each user's score contribution to an action is not treated equally. Instead, their reputation influences how heavily their score weighs into the final calculation.

We achieve this by using a Sigmoid function to adjust the user's reputation, creating a weight for their score. The Sigmoid function is particularly useful for this task because it maps any input value into a range between 0 and 1.

For users with low reputations, their weights are closer to 0, while users with high reputations have weights closer to 1. As a result, a user with a higher reputation will have a greater impact on the final score of an action.

However, the Sigmoid function also ensures that the reputation effect is not linear - meaning the difference in weight between users with low and medium reputations will be more significant than the difference between users with high and very high reputations. This is because the Sigmoid function exhibits saturation for very low and very high input values, thereby creating a balanced system where reputation matters, but extremely high reputations don't overly dominate the score weighting.

Basically, the score of each action is calculated using a combination of all scores submitted heavily weighted by the reputation of the data provider; data providers whose data is closer to eventually calculated numbers will be more rewarded.

Also, the reward will also greatly increase the Data provider's reputation, while if a data provider has a record that's not close to the calculated score might slightly lose reputation.