Vitalik Buterin
@vitalik.eth
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/
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Claudio Condor
@oydual3
Interesting proposal on deep funding. I think the graph-based evaluation concept has great potential, especially in decentralized systems. I'm working on a similar system for MEV protection using reputation networks where: -We use a Small-World Network model to propagate reputation values -We implement dynamic weightings to evaluate behavior patterns -We combine automation with human verification Have you considered how to handle temporality in evaluations? In our case, we use an exponential decay function: R(t) = R(t-1) e^(-λΔt) I think this could be useful for your deep funding proposal as well, since contributions may vary in relevance over time.
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