Deep funding combines two ideas:

1. Value as a graph: instead of asking "how much did X contribute to humanity?", ask "how much of the credit for Y belongs to X?"
2. Distilled human judgement: an open market of AIs fills in all the weights, human jury randomly spot-checks them

https://x.com/TheDevanshMehta/status/1867600164502089925

(1) has been a growing paradigm recently, for good reason. Contribution is hard to measure in the abstract: if you ask people how much they would pay to save N birds, they answer $80 for N=2000 and N=200000. "Local" questions like "is A or B more valuable to C?" are much more tractable.

(2) is based on ideas in my info finance post: https://vitalik.eth.limo/general/2024/11/09/infofinance.html#info-finance-for-distilled-human-judgement . Anyone can use any methods (eg. AI) to suggest weights for *all* edges, a human jury does detailed analysis on a random subset. The submissions that are most compatible with the jury answers decide the final output.

Deep funding combines two ideas:

1. Value as a graph: instead of asking "how much did X contribute to humanity?", ask "how much of the credit for Y belongs to X?"
2. Distilled human judgement: an open market of AIs fills in all the weights, human jury randomly spot-checks them

https://x.com/TheDevanshMehta/status/1867600164502089925

(1) has been a growing paradigm recently, for good reason. Contribution is hard to measure in the abstract: if you ask people how much they would pay to save N birds, they answer $80 for N=2000 and N=200000. "Local" questions like "is A or B more valuable to C?" are much more tractable.

(2) is based on ideas in my info finance post: https://vitalik.eth.limo/general/2024/11/09/infofinance.html#info-finance-for-distilled-human-judgement . Anyone can use any methods (eg. AI) to suggest weights for *all* edges, a human jury does detailed analysis on a random subset. The submissions that are most compatible with the jury answers decide the final output.

https://deepfunding.org/

I find it kinda funny that the 2015 study on the efficiency of prediction markets to estimate research reproducibility you referred to in your info finance post, later failed to be reproduced:
https://replicationindex.com/2021/05/10/osc-pm/

Do you think it's because we didn't have good AI or prediction markets are fundamentally not enough for science?