Thank you and rest of Gitcoin team for publishing updated results. This, in my eye, is most informative and logically organized matching report that Gitcoin has produced so far. This greatly increases my confidence in both sybil defense and algorithm presented.
Based on @Joel_m reply to my questions in QED program post post, I can understand rationale for decision to swap algorithms without announcing it first.
Naively clustering by donation profile is only really possible if you donât tell people youâre going to do it before hand (or if youâre not too worried about people strategizing).
I will attempt to sanity-check base amounts later, but at the moment I find round data plausible and no longer have any reservations against final report or the process itself.
Thank you @umarkhaneth and squad for sharing the results as well as the rationale on calculations. Cluster matching is definitely a game changer!
I vote to Ratify the round results
As feedback, maybe there can be a zkproof attached to the results so folks can confirm the calculations match the logic provided. That being said I understand that you cannot reveal all the strategies used to identify the red team but I hope we can strike a healthy middle ground.
Iâd like to ask what the time distribution looks for producing these results? are there any specific leverage points which could reduce the time to output significantly?
Great nuanced points here as expected from @ale.k , especially about how hard it can be to tell the actor behind sybil action. Also thank you for sanity-checking the results.
Thank you for being so engaged and asking for more transparency. This doesnât happen without that! Please keep doing it
Interesting, I donât know enough about zkproofs to know if this is possible but would be quite cool if so.
One of the big improvements this round was having regendata which makes it much easier to access clean data sets from grants stack, passport, and onchain. I canât think of other specific leverage points besides having more/better detection methods ready to go in advance of the round ending
I am unsure if I am using the right vocab, but is it fair to assume that clusters are unique to each core round? i.e., the clusters created for allocating the pool for Eth Infra are mutually exclusive from those created for allocations in Climate. Or are clusters agnostic of core rounds and reflective of decisions made on all projects across all core rounds?
Appreciate the detailed explanation. I now understand the reasoning behind the Cluster-Match QF. While that seems like an approach in the right direction, I wanted to highlight few concerns on this, citing Nawonmesh as an example.
In my opinion, urgent climate action needs a bottom-up approach through the formation of many grassroots organizations focused on building local climate resilience by solving local challenges, not just globally appealing projects like medical herbs. In such cases, funding support will have to come primarily from their local communities as these projects might not appeal to global donors, although local climate efforts also contribute to global GHG reduction. Cluster-Match QF seems to be incentivising the opposite approach i.e. a top-down approach and makes Gitcoin unsuitable as a funding source for grassroots climate orgs having only local appeal.
The logic of crowdfunding is to get initial social proof from the existing community of the project owners before the larger community jumps in to donate. That is how any web2 crowdfunding platform also works. Now, if that existing community is supposed to prove their legitimacy by also donating to other projects (who they know nothing about), it just creates lot of unnecessary donor friction.
Two-third of India survives in less than $2/day. Nawonmesh is a grassroots project operational in one of the most backward regions of rural India. You can imagine the financial capabilities of the immediate and extended communities Nawonmesh serves. Convincing these people to (a) resonate with a project having no immediate benefits to them and (b) DONATE $1, that too in CRYPTO, was itself very very difficult. Expecting them to give more to unrelated projects is completely unrealistic. If I had asked them to also donate to other projects, they would not have donated to Nawonmesh too as it would have been an overkill for them.
Also, ChainEye dashboard had 240 passport votes for us. but you have mentioned 187 eligible voters. I am unable to understand this gap.
Thanks so much for sharing this @umarkhaneth . Really appreciate your time and effort for this. Something similar happened to one of the projects I am associated with > Impact Stream.
Despite the number of votes, the matching amount for us is $10?
I echo @priyank , Cluster-Match QF is defeating the purpose of empowering grassroot level organizations to raise funding from Gitcoin as the only support they can get is from their local community. How will that happen? How will we onboard local orgs solving for challenges on local level? Please excuse my ignorance but would like to learn about this.
Hey Rohit, thatâs right on the money. We cluster based on the donations only within a single core round at a time. Itâd be a cool experiment to try clustering based on donations to all core rounds and seeing if this gives better results.
Hey Priyank! If our goal is the greatest global GHG reduction then shouldnât we be searching for projects which most reduce global GHGs and funding those, regardless of if theyâre local or not? Climate knowledge is not my forte and Iâll defer to @M0nkeyFl0wer for his opinion on this.
Cluster-Match QF is very bottoms-up however rather than trying to fund the largest single community it focuses on funding those who serve the most communities. Youâre right that this may make it unsuitable for orgs with only local appeal (like parks on Long Island). There may be better funding sources out there for local orgs imo. Gitcoin has always been digital-first.
I agree! Thatâs not the answer I would seek. Instead, if Iâm participating in a global funding round Iâd ask what the appeal of my project is to people outside my local community and how I can create value for a more diverse supporter base. The behavior we want to reward is cooperation across differences. If a project is supported by people who are very different then that is a strong signal.
I canât speak to how the ChainEye dashboard was built. If itâs just based on passport score then theyâre missing the post-round squelching we do as described here.
Hi Sejal! Thanks for posting on our forum. Did you see the detailed spreadsheet? Impact Streamâs matching increased when going to Cluster Match QF.
This mechanism disempowers uniform, established monoliths and actually empowers grassroots organizations if theyâre made of diverse, different members. For example, in the Web3 Community and Education round greenpill network with itâs global, distributed chapters saw an increase in matching funding of $3,787.93 when going to cluster match QF
Hey Owocki! thanks for sharing â will take a look
Thank you to everyone who puts their effort into to making Gitcoin and Quadratic Funding a meaningful way to fund public goods and projects. Every time the Tor Project participates in these funding rounds, I am impressed by the amount of collective effort goes into making them run, communicating clearly with the community, and improving over time.
Cluster-Match QF takes the projects you vote for as signals of the communities you belong to. It then calculates matching amounts for each supporter and unique community combination. This method provides more significant matching funding to projects that receive support from more diverse communities.
Awesome. Thank you for making it clear the evolution of QF and the reasoning behind the changes.
There might be one possibility with cluster-matching QF that could benefit local communities. The new algorithm increases the total cost for Sybil attackers and tilts the scale for the system to be âcheaper to defend than attack.â It might be a worthwhile exercise (possibly a prospectively funded project in Citizens Round if anyone is interested) to evaluate if we can lower the requirement for Passport Score in the frontend with cluster-matching QF as an additional rear-guard mechanism to nullify Sybil contributions.
To validate this, someone would need to rerun the squelching with one or two lower passport scores and analyze the impact on the final distribution. If the data supports this hypothesis, a lower score will reduce some friction local communities have in onboarding contributors to Gitcoin Grants.
Here is some background in the 2-minute snippet from @owockiâs conversation with Joel Miller:
hey @umarkhaneth, cluster QF def feels like a step in the right direction.
Just trying to wrap my head around whats happening - Cluster QF filters donors having voted for multiple projects and counts them for QF , so does it also exclude a few donors that projects might have had that have just donated to that single project or that figure needs to cross a certain threshold?
Post Cluster sybil analysis the QF formula applied is the same and you are not tweaking the matching multiple depending on multiplicity of votes from a donor yet?
The difference between base and eligible voters represents the no of votes that projects rcvd from donors just voting for that particular project?
First, Thanks for the hard work done for doing the cluster match QF
I just have a question: " the same cluster are added together as if they were the same voting bloc", I do not quite understand it. Say if one cluster/community has 100 voters, it is considered as one voter?
I understand that the votes from the same cluster should be given less weight, but treating them as from one voter is not quite fair especially for some local communities.
Hi Umar, I agree 100% with your statement. But as of today, none of the climate projects write their GHG reduction potential on their gitcoin grant pages. The simple reason for this is that it is something very difficult to quantify accurately at the early stage of the projects. Due to that, the global appeal of a project does not mean that it has the highest GHG reduction potential. It might just be due to an interesting product, eg. medicinal herbs, or the likability of the founder on twitter interactions. Many grassroots orgs like Nawonmesh work on not-so-interesting things like local regeneration. And are run by senior citizens who are non-digital savvy and non-native English speaker, so spending time on Twitter spaces to showcase their charisma is not their forte (The main reason I am representing Nawonmesh in all the online interactions). It is much easier for them to interact with their local community for support.
Unfortunately, the experience of Nawonmeshâs founder says that the fundraising opportunities for regenerative activities are limited in their region.
Fair logic.
I also feel that we need to increase the donor base of the whole climate round. Compared to the other core rounds, the amount donated and unique donors are way less for the climate round. One of the easy ways to achieve that could have been to let climate projects onboard their communities and then few members from a particular projectâs community would have started cross-funding other projects too in subsequent rounds. Imagine if 100 climate projects could bring in just 50 new people, it would have almost doubled the âunique donorsâ count for the climate round. But, due to Cluster-Match QF, the strategy of onboarding new communities has been somewhat disincentivized.
Why are the âEligible Votersâ numbers different for the same project in the âClimateâ and âClimate - Shellâ sheets in the updated results?
itâs not just a mathematical formula but a social consensus on the best way to leverage the wisdom of the crowds. And the post analysis, pre-payout period is when some of the most active discussions take place. it would be tragic to let go of this tradition.
My main concern with this rounds distribution is just how closely it mirrors the âwinner take allâ approach of the real world. Consider this chart i found showing the distribution in the open source round, the inequality is worse than any capitalistic nation.
I wonder if we could develop a gini coefficient or some such metric capturing inequality among projects as a 1st step to possibly reducing it in future rounds. Hereâs some interesting research on progressive taxation in quadratic funding systems from DoraHacks thats worth exploring
I also donât know how much were following the gitcoin beta round squelching, but the difference that my project received from the 1st spreadsheet to the last was over 30%. These window periods are valuable for getting the communityâs assistance in identifying sybil attackers, such as how mini meadows & some others got caught last round in this window period.
I will say that contrary to my expectations, the teams active on gitcoin radio have performed better under cluster QF. Maybe because we each gave to so many different projects that it increased the value of our vote. So while it wonât initially help local convergence, it is certainly helping digital coordination!
I agree with this point, I urge the team to consider making 10 cents the minimum vote for matching. $1 while living in the west is very different from $1 in the global south. Also, 20-35% of my project votes came from those giving less than a dollar, sometimes 10 cents and tragically even a few 95 cents
Finally, I request the team to not publicly list the payout address of projects as many operate in hostile environments where this information could be used against them
Following some email exchanges with ben , he made me realize it would be more beneficial for us to shift our discussions to the government forums to embrace a âbuild in publicâ approach.
We also see some similar points as @priyank 's regarding our project.
After extensive internal discussions, we made the decision to participate in the Climate-Shell round, so we were opted-in during the application. However, we have noticed that our project is not listed in the Excel file.
@umarkhaneth would you also please kindly look for our project again as numbers show some unfairness I canât comphrend ?
Marked our Earthist - Decentralize the Seeds project with a magenta color in the climate round, and just to understand the numbers in comparison:
We have the second-highest number of eligible voters and eligible crowdfunding yet our match rating is lowest on below example sheet. While the average contribution for our project is $1.66.
We are eager to gain a better understanding of the situation for such low matchmaking even with high passport granted supporters. Your guidance and support in this matter would be greatly appreciated.
Fully in support of this statement. This can help reduce the turnaround time in payouts.
I can relate to these challenges faced in the global south. Iâd like to highlight a Climate Solutions project that has been working extremely hard in a country where minimum wage is $5 and a family needs 108 minimum wages to sustain a family of 4 with basic needs. Mi Costa de Oro has spent months onboarding their community members to web3 tools (snapshot voting, paying for basic needs with crypto, sending/ receiving tokens when compensated for beach clean ups, minting mirror articles, etc). Despite all their hard work since April, not one of them is able to obtain a Gitcoin Passport score that enables matching. This means they didnât even attempt to vote in the round. They were able to create impressive results this round by providing constant and transparent proof of their work and work incredibly hard to promote their grant during the Shill spaces. None of their contributors speak English. This means they mustered up the courage to participate in English speaking spaces, request the mic, shill their project in Spanish and hope people understood or someone present could translate for them. Iâm pointing this out because your project can take a page from their playbook. 100% of the images published by the Nawonmesh twitter account are AI generated. almost all AI generated images. This looks pretty compared to the low quality images published by Mi Costa de Oro, but they donât do a great job showing the work and impact being pitched in the grant application.
Iâm really interested in learning how you were able to get the contributors to achieve passport scores above 20 points because it has been a huge barrier for the communities I work with. I havenât been able to get a single contributors in these communities above 8 points.
I can understand the frustration with this, but I also think itâs healthy for the ecosystem. It creates a pluralistic and regenerative environment where people looking to be funded also take the time to become immersed in the ecosystem, learn more about other projects and potentially collaborate or copy pasta some of their work to benefit their local efforts.
Iâm in favor of these conversations happening in between rounds in an attempt to establish a structure that doensât require debate after every round. Itâs important to consider that many of the smaller projects are living day to day. Continuously delaying payouts for the sake of big brain back and forth seems like torture to many of these projects. Letâs come up with a more streamlined process and stick to it until something serious breaks and needs fixing.
Iâd be interested in seeing how this correlates to userbase. For example - Do Lenster, revoke, JediSwap have a much bigger userbase or transactional volume than projects on the lower end of match funding? If yes, I think this pays out fairly. I donât know those figures, but my gut tells me the funding received reflects the size of their userbase as well. It would be really interesting to identify projects that didnât perform well, but house big userbases.
These might be bot donors. It was something that also occurred in the C grants platform, even between rounds. It always confused grantees. I donât think this is a case of donors giving 95 cents, or less than $1 because thatâs all they could afford.
My big question looking ahead is - Wen Cluster Match + trust bonus based on passport score?
Hey @priyank , I really appreciate you sharing your unique perspective. As we have these discussions about what funding allocations should look like, I want to give my interpretation of the problem QF was originally designed to solve, which I think is different from the problem youâre pointing out.
QF was designed to solve the problem of public goods funding under imperfect coordination. The first iteration of QF solves this problem in a world where people are maximally uncoordinated (i.e. everyone is completely selfish and isolated). In contrast, if everyone was perfectly coordinated, we wouldnât need QF at all.
But the real world has a mix of coordination and isolation. We have local communities with internal communication channels (coordination), but people in different communities may still be isolated from each other. So the new algorithms like Cluster Match try to make funding work in this world by giving less money to projects supported by just one community, and instead favoring projects with diverse bases of support. If a project only has local support from one community, Cluster Match assumes that project doesnât need as much extra funding, since the people in that community should be able to figure out how much to fund it on their own.
Correct me if Iâm wrong, but I think youâre pointing out that this isnât the whole picture. It may be the case that a local community knows how much money a project should get, but doesnât have the cash to fund it. IMO this is kind of an orthogonal issue which is important to address, but needs different tools and different analysis. Of course, itâs important to be aware of a case where trying to be more optimal along one axis (accounting for coordination) may have been less optimal along another axis (accounting for differences in ability to pay). But I think being clear about the microeconomic foundations of whatâs going on here can help us move forward in the best way.
For what itâs worth though, I think the picture around how Cluster Match impacts communities with differences in ability to pay isnât so simple. I actually think that with all else held equal, switching to Cluster Match tends to help communities with less ability to pay. But this post is already too long so Iâll leave out that explanation for now.
Thank you for the work you do to get these results and thanks to Gitcoin for the transparency, this is excellent work and the fact that not everything is automated shows the level of dedication the team put in, thanks a lot.
I have a question regarding the Eligible Crowd Funding, Simple QF Match, Matching difference, and the rest, I wanted to know if they sum up to what a project would receive. Iâve had these questions from some of my community members, and Iâm unsure how to respond.
I want to give a huge shoutout to @umarkhaneth for driving this and everyone else who helped detect Sybils and implement cluster mapping QF.
I am personally very excited about (and bullish on) cluster mapping and other varieties of QF that can effectively dampen collusion and Sybil attacks across the board in an objective fashion. The old cGrants platform had been using pairwise QF for years (a similar modification to cluster mapping, with similar impact/results). This was first tried in GR5, through GR15 (so almost 3 years from 2020 - 2023). We only went back to âtraditional QFâ for the Alpha and Beta rounds.
Most users probably werenât even aware of this and there wasnât a push to publish the match differences between pure QF and pairwise, it was just the method found to work best. Similarly, while Iâm glad Umar shared matching calcs for both methods in this case, given itâs the first time itâs being tried, I donât think itâs productive to publicize all alternatives every round. If we calculated results with pure QF, pairwise, cluster mapping, Sybil or no Sybil squelching, and shared them all to compare, almost everyone would be able to find a scenario where they would get more funding and thus would not be happy.
I do trust the team doing deep data analysis on Sybils, passports, voting patterns, etc, to find and use the best method to prevent collusion across the board (objectively without manual subjective judgments). We should absolutely be as transparent as possible though about the methods used and decisions made, and I believe weâre only getting better in that regard compared to prior rounds.
So all that said, I really appreciate the hard work and hours that went into this from many people, and although not everyone is happy with the outcome, I am in favor of moving forward to a snapshot vote to ratify these results.
cc 
