How can the attention load on the allocators be reduced?
pairwise voting. This was found to be incompatible with DAO Drops' points approach.
curation
elimination approach to objectively reduce the number of eligible nominations
qualitative approach to evaluate nominations based on criteria, using a scoring rubrik. This approach was found to be problematic because of the amount of voting power it concentrates in the curators, compared to the allocators. It was also difficult to avoid the criteria being subjective. Lastly, it requires potentially a large amount of labor from the curators if there is a high number of nominations.
Our team did find a way to integrate our points approach with Pairwise voting, for our second round of app development:
PairDrop (DAO Drops V2)
To increase fairness in evaluation and further reduce the popularity contest dynamic in many Web3 grants programs, we built an adaptation of DAO Drops that used pairwise comparison voting. It is available as a forkable app here: https://github.com/dOrgTech/PairDrop. The differences between PairDrop and Optimism or General Magic’s pair voting apps are:
Voter points are distributed using a dynamic, adaptable script to scrape on-chain data that demonstrates addresses have various types of expertise and experience in Ethereum. (The algorithmic governance innovation from DAO Drops V1)
Round operators can customize how many pairs are selected. As few as five pairs, even with a low number of voters, successfully determines preference average.
Uses BudgetBox by Colony (https://pairdrop.daodrops.io/assets/misc/BudgetingBoxes.pdf), a trusted machine learning model for generating a “preference graph” to normalize funds distribution based on voter choices. We believe this mechanism is an underutilized way for communities to harness their collective intelligence in an efficient and unbiased manner.
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