Convex funding is an allocation mechanism concept that can be used to rank impact in grant programs. It uses convex functions to emphasize diminishing returns and prioritize grants that show a significant potential for impact even with smaller funding amounts. This approach can direct more of initial investments towards projects where marginal gains are highest, optimizing overall resource allocation before the grant program even begins.
The interesting part is that convex funding can be used as a prediction mechanism, this means we can experiment safely, consistently rank impact categories, and evaluate team performance without actually distributing funding! This adds an extra layer of credible neutrality and protection to grant programs, potentially serving as a valuable filter for future milestones and programs alike.
Our goal for this pilot is to use the mechanism as a support function for some of the ThankARB programs to which it is applicable to(MAX 3 programs, with 10-15 applications max-randomly sampled) and verify if our funding assumptions are correct. We will learn which projects were ranked accordingly and why, who overperformed, who underperformed, which reviewers were accurate in their assumptions and how CFX performs overall when used as a prediction mechanism. This is valuable because if successful we can use it to accelerate projects, but also scale the process and apply it to even more grant programs to maximize efficiency
We can use Convex voting to rank impact on the following grant programs part of Thank Arb Milestone 1B:
For the pilot we will chose 3 different programs and test CFX individually or collectively on all.
Soft Deadline 25.07.2024 Hard Deadline 10.08.2024
UPDATE 26.07.2024: We are waiting for the grant programs to finalize eligible applications. We have received the REFI data and are waiting for the RWA and Giveth programs to close applications so we can gather that data as well.
UPDATE 06.08.2024: We have decided on our methodology