LithiumFinance
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LithiumFinance

Collective Intelligence: NFT Pricing as a Beauty Contest

Lithium Finance’s proprietary algorithm incorporates human-based inputs alongside Machine Learning to provide accurate NFT valuations, and this is achieved by incentivizing price experts with rewards when they add reliable inputs to the system. Despite the sound assumption, we soon ran into two logical issues.

Community users are expected to input honest answers to get a reward, but we cannot determine if users are giving answers based on their own knowledge of NFT collections, or if they are simply deferring to floor prices from other sources.

Being honest does not always equate to the best valuation. If a user honestly believes that an NFT series is worthless when its floor price is 100 ETH or more, should they be rewarded for their honesty?

To create an algorithm that truly works, we spent some time to consider which of the following information the protocol should aim to collect:

  1. Should it collect individual inputs on what he thinks an NFT is truly worth?
  2. Should it collect individual inputs on what he thinks the others think an NFT is truly worth?
  3. Should it collect individuals input that what he thinks the other would anticipate an NFT would be traded for in the market?
Price Experts Thinking Process Comparison

It became clear that our goal is not to understand what people really believe an NFT is worth to themselves, but how people perceive the market would trade it, as inspired by the Keynesian Beauty Contest.

The term is given to the behavioral economic theory proposed by British economist John Meynard Keynes.

Inspired by beauty contests, the analogy requires entrants to guess six prettiest faces out of 100 photographs and those whose picked most popular faces would win a prize. Through the fictional contest, Keynes theorized that those who wanted to win would pick what they think others would find attractive instead of their personal favorites.

Nobel laureate Richard Thaler experimented the theory with a number guessing game published in the Financial Times where readers had to submit numbers from 0 to 100 and the submission closest to ⅔ of the average chosen number would win.

Thaler summarizes the contest as a game to guess what other people are thinking that other people are thinking. He concludes that Keynes’s beauty-contest analogy remains an apt description of how financial markets work. The experiment proved that not all players will submit fully logical answers and the best players are the ones who succeed in evaluating others’ perception of the average player’s thinking process.

Building upon the ideas developed by Keynes and Thaler, our algorithm is focused on determining the community’s input on how they anticipate others would trade rather than the intrinsic or fundamental value of rare NFTs. To help user think of the price levels for the market to buy and sell an NFT, Price Experts are requested to enter two different inputs:

  • Price to Buy: The best price that the 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

Lithium Finance gauges market sentiment by objectively incentivising and aggregating inputs from the community on their assessment of fellow community members. In next chapter, we will look into how do we produce the final valuation to determine the winners.

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.

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