Dec 2, 2022
Pricing Quest: What are the winning criterias?
Lithium Finance provides NFT valuation swiftly with a combination of human input and machine learning. Here comes the big question: how do you win and earn rewards on the platform?
Community is at the core of Lithium Finance. Price Experts in the community are consulted on how they think the market would be trading through our platform, and experts share two key pieces of intelligence — the estimated price to buy, and the estimated price to sell.
- Price to Buy: the best price that a buyer could successfully convince the owner to sell today
- Price to Sell: the best price that owner could get from successfully selling the NFT today
From the previous article, we learnt that these estimates are processed and aggregated through our Reputation Clearing system to produce three indicators — Low Value, Consensus Value and High Value.
Winning Criteria and Precision of Estimates
The winning objective of each Pricing Quest is to accurately estimate the price at which the market would trade an NFT, and that market is composed of the Lithium Finance community like you and I! But how do we decide the winners of each Pricing Quest?
We evaluate Price Experts’ performance on how good their estimates are. Price Experts’ submissions are judged by the proximity of their estimates to the final results, being the three indicators collectively produced by the community — Consensus Value, Low Value, and High Value. Price Experts are rewarded with (i) $LITH from Prize pool and (ii) Reputation Points according to their respective performance.
Win from $LITH Prize Pool
Price Experts would be contributing to price multiple NFT in a Pricing Quest. One expert will win in each of these NFT pricing. All winners in a Pricing Quest will share the $LITH Prize Pool, based on the relative amount of $LITH tokens staked.
In each NFT pricing, the Price Expert with the closest and unique submission wins. Meaning the Price Experts with the highest final score according to the following scoring system, PROVIDED his winning submission is unique that such estimate is not duplicated with other Price Expert’s answers.
What about those who did not manage to guess the estimates correctly? They will be able to claim back the original number of tokens that have been staked.
Reputation Points(RP) Adjustments
Asides from $LITH tokens, another reward issued after every Pricing Quest are Reputation Points (RP). Good estimates will be rewarded with a positive point adjustment. Similarly, bad estimates will cause the Price Expert’s reputation to be slashed. But how do we determine the exact number of Reputation Points to add or slash?
Similar to scoring for the $LITH Prize Pool, the Price Experts are scored based on performance. Unlike the $LITH Prize Pool, all Price Experts will receive Reputation Points adjustments in every Pricing Quest.
With their Final Score, which is measured in the number of standard deviations from the respective indicators on a Z-score table, Price Experts are grouped in increments of ±0.1 points. The smaller the distance is from the indicators, the more accurate the prediction is, and the higher the reward in Reputation Points will be. Now that we have Price Experts grouped by performance, each Price Expert will receive Reputation Point adjustment according to the table below.
Experts whose submissions are very good (in absolute Z-score distance ≤ 0.1) will be rewarded 100% of their staked Reputation Points, subject to a cap. Experts whose submissions are reasonable (in absolute Z-score distance of around 1.0) will not receive any adjustment. Any submission in absolute Z-score distance further than 1.0 will result in the expert’s Reputation Points being slashed.
Now you have learned how we derived the community consensus value and the winning criteria, it’s time to Price2Earn! Join our Pricing Quest from 12 December 2022 to earn rewards!
About Lithium
Lithium Finance is the first decentralized NFT valuation protocol powered by collective intelligence and machine learning. Redefining NFT valuation approach through incentivizing honest assessment from community to reveal market sentiments.
