Suppose you'd like to improve in chess as a total beginner so that within a month training one hour a day your rating in rapids is as high as possible. What do you do? Watch a YouTube video? Read a book? Ask ChatGPT? What about learning a new language in the shortest amount of time, with 20 minutes to spare a day? Or creating a startup full-time for 3 years to maximize net-worth?
The advice is scattered all over the internet, and you don't even have a way of accurately telling which of them have any merit.
In this blog, I propose a platform solution that properly provides incentives and combines beliefs to find the best approaches to problems.
A Problem will be defined by:
- Prompt: The set of valid approaches we are looking for (Getting better at chess one hour a day for one month as a total beginner)
- Metric: How do we evaluate individual approaches (Rating on chess.com in rapids at the end)
Anyone can then submit an Approach (Each day play two rapids, and review the game using the chess.com analysis tool, use the remaining time to do puzzles).
The resulting ratings from following this approach form a standard distribution.
If we could then know what the average expected rating is for any given approach, we could rank them and reward the best ones based on the problem demand to incentivize contributions.
Each problem will have its own demand.
People can boost problems to increase their demand.
Then the total funds for a problem will be slowly over time distributed to the best performing approaches on it.
How do we determine, however, the expected yield of any approach?
Some of you might have already guessed one possible solution.
That's right. It's prediction markets time.
Each approach will have its own market. People can invest either up or down. Then, once an approach has enough liquidity, the creator will be eligible to receive tokens for it.
It's quite hard guessing how much net-worth will an average person have after creating startups for 5 years with a certain approach.
While initially, the estimates might not be very accurate. They will still provide more objective comparison between individual approaches than simple upvotes or downvotes.
Over time if they get tested or put through enough discussion we can expect it to be more and more accurate.
Prediction markets are the best way currently to make such estimates, but they're not a necessary forever component of the platform.
People can bet up their own approaches to earn rewards.
Well, yes, but as I said, something needs to have enough liquidity to be eligible for rewards and if that's the case the person will lose the tokens when other people correct it.
If something doesn't resolve, how will people earn tokens from their investments?
Simple. From price movements. Which will get more and more accurate as they get put through discussion and testing.
In the future, there could also be the concept of testers. So that once an approach gets tested by a large enough sample size, it gets resolved.
For the platform to gain traction, it will require enough people creating relevant problems and contributing & evaluating approaches.
I believe that this is possible since it might provide a large advantage to the ones utilizing the platform.
If you want to be one of the early adopters and help us further develop this idea, you can join here: Approdict.
I'd like to hear your opinions and answer any questions you might have.