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This is the first piece in a series of articles about a new capital allocation system, incubated in Vrbs with continued development funded by Nouns.
Ensuring capital is effectively allocated to people making real impact is a largely unsolved problem in decentralized systems and more broadly in the world. Verifying quality of impact and genuineness of the people involved is no easy task. A main hypotheses for this streaming grants program is work that helps accurately allocate funding and verify impact should be rewarded. A system that gives rise to effective capital allocation should emerge naturally from incentivizes, not from top-down central management or strict structure. People that verify impact and effectively allocate capital should have a natural incentive to do so, and their success should encourage more capital input and upside for them.
By pairing a streaming money protocol with TCRs, we believe we can come closer to an ideal method of capital allocation that maximizes impact in the world.
Assume a stream of $1,000 per month is sent in per-second increments to a list of people that regularly clean up trash on beaches. Nouns DAO pays to make this happen, and anyone in the world can apply to receive funding to do cleanups. Nouns holders help decide how the $1,000 is split to the list of approved recipients.
How do we build a list of funds recipients in a decentralized manner that is resistant to capture, and ensures money flows to the most impactful beach cleaners while also accounting for the preferences of Nouns holders?
Assume we set up a TCR, with a token called $beach. 10% of the $1,000 per month is paid proportionally to $beach holders. In order to submit an application to receive funding, you must buy and post a bond in $beach. Once you’re on the list, you get paid every second. When you are removed from the list, your stream of funding stops.
There are 3 user types, consumers (Nouns holders), candidates (beach cleaners), and token holders (list curators).
Nouns holders desire that the $1,000 is spent maximizing the outcome of clean beaches where it matters most to them. Said another way, holders desire that the highest quality list of beach cleaners are paid. Holders might also want to express their relative preference over the list to help decide how the money is split.
People cleaning beaches around the world would like to be funded for their efforts. They desire to be included in the list of approved recipients, so that they can be paid to to make impact.
Token holders of $beach desire to increase the price of their tokens, and to receive their share of the 10% split of the beach cleaning budget. In order to do this, they must curate a high quality list of beach cleaners. If Nouns holders judge that their $1,000 is being spent effectively, money will continue to be streamed, and the demand for $beach will remain high and holders will continue to receive their rewards.
To paraphrase the original TCR doc in this new paradigm, to keep both demand and rewards for $beach holders high, $beach holders must keep cleaners desirous of having listings in the registry by maintaining DAO interest in funding the registry by keeping the quality of recipients high. Stated in reverse, if the quality of funds recipients are high, then the DAO will be interested in funding the registry such that beach cleaners will desire to be listed in the registry for funding.
Token holders realize a direct financial benefit for curating the list in an expert manner, and the degree of their benefit increases proportionally to the quality of their curation as consumer (Nouns DAO) and candidate (beach cleaner) interest rise in lockstep.
Real world impact in different areas and the work required to make impact can be measured in many ways. So the verification and funding of this work must be open to new strategies and means of authentication, and the incentives must line up to output effective capital allocation.
After running an MVP of this streaming grants program in Vrbs, we’ve identified and highlight some problems below that this v1 aims to solve.
Zero Sum
With a fixed budget of $1,000 to stream, each new beach cleaner dilutes funding for existing recipients.
Need minimum amount of funding to make impact
Many folks might be able to make real impact cleaning a beach for $100 a month, but maybe not for $5 a month.
Complex decision for curators
In a traditional TCR, curators only have to decide whether a candidate should be on the list or not. In streaming grants, there is now another axes to evaluate: what proportion of the stream does this recipient deserve compared to others? Is the proportion they’re receiving allocating funding most effectively? Additionally, this proportion should reflect at least in part the preferences of Nouns holders.
New builders don’t receive funding
In the current implementation, even if your application is approved, you start out with no funding unless you can find someone to vote for you. This requires a lot of up-front effort for the beach cleaner.
There are many of people around the world interested in receiving money to clean beaches. With a fixed budget, the salary for each cleaner will trend to zero as more people are accepted to the list. To solve this, we propose that each budget has a maxRecipients and funding is split evenly between each recipient to start. This creates a cap on the number of possible recipients for the beach cleaning list, and fosters competition between applicants. Additionally, new builders will know that they can receive a minimum amount of funding if they are accepted, and can decide to apply or not based on that information. They also don’t have to campaign heavily for votes to get paid.
maxRecipients also reduces decision complexity for list curators. Instead of having to decide both whether a beach cleaner deserves money and also constantly evaluate whether the relative work they do is worth the money they’re getting, token holders just need to decide whether the current candidate or recipient is worthy of being in the streaming funding list of length maxRecipients.
Meritocracy
We want to reward people who are building above and beyond to be rewarded.
We plan to introduce a secondary “bonus” stream on top of the baseline salary that Nouns holders can vote to change. This will encourage existing stream recipients to compete to earn more. For the scope of the MVP, we will limit Nouns holder voting to the top level budgets for now.
We believe we are zeroing in on a powerful capital allocation primitive driven by new advances in money streaming, low txn fees on L2s, and TCRs. We plan to build in public and share updates on design decisions, problems, and proposed solutions as we build out a production grade system for Nouns DAO over the next 4 months.
Stay tuned for the next article in our streaming grants series. We plan to continue building in public to get feedback as we prepare for the protocol audit in early October.
https://docs.google.com/document/d/1BWWC__-Kmso9b7yCI_R7ysoGFIT9D_sfjH3axQsmB6E/edit
This is the first piece in a series of articles about a new capital allocation system, incubated in Vrbs with continued development funded by Nouns.
Ensuring capital is effectively allocated to people making real impact is a largely unsolved problem in decentralized systems and more broadly in the world. Verifying quality of impact and genuineness of the people involved is no easy task. A main hypotheses for this streaming grants program is work that helps accurately allocate funding and verify impact should be rewarded. A system that gives rise to effective capital allocation should emerge naturally from incentivizes, not from top-down central management or strict structure. People that verify impact and effectively allocate capital should have a natural incentive to do so, and their success should encourage more capital input and upside for them.
By pairing a streaming money protocol with TCRs, we believe we can come closer to an ideal method of capital allocation that maximizes impact in the world.
Assume a stream of $1,000 per month is sent in per-second increments to a list of people that regularly clean up trash on beaches. Nouns DAO pays to make this happen, and anyone in the world can apply to receive funding to do cleanups. Nouns holders help decide how the $1,000 is split to the list of approved recipients.
How do we build a list of funds recipients in a decentralized manner that is resistant to capture, and ensures money flows to the most impactful beach cleaners while also accounting for the preferences of Nouns holders?
Assume we set up a TCR, with a token called $beach. 10% of the $1,000 per month is paid proportionally to $beach holders. In order to submit an application to receive funding, you must buy and post a bond in $beach. Once you’re on the list, you get paid every second. When you are removed from the list, your stream of funding stops.
There are 3 user types, consumers (Nouns holders), candidates (beach cleaners), and token holders (list curators).
Nouns holders desire that the $1,000 is spent maximizing the outcome of clean beaches where it matters most to them. Said another way, holders desire that the highest quality list of beach cleaners are paid. Holders might also want to express their relative preference over the list to help decide how the money is split.
People cleaning beaches around the world would like to be funded for their efforts. They desire to be included in the list of approved recipients, so that they can be paid to to make impact.
Token holders of $beach desire to increase the price of their tokens, and to receive their share of the 10% split of the beach cleaning budget. In order to do this, they must curate a high quality list of beach cleaners. If Nouns holders judge that their $1,000 is being spent effectively, money will continue to be streamed, and the demand for $beach will remain high and holders will continue to receive their rewards.
To paraphrase the original TCR doc in this new paradigm, to keep both demand and rewards for $beach holders high, $beach holders must keep cleaners desirous of having listings in the registry by maintaining DAO interest in funding the registry by keeping the quality of recipients high. Stated in reverse, if the quality of funds recipients are high, then the DAO will be interested in funding the registry such that beach cleaners will desire to be listed in the registry for funding.
Token holders realize a direct financial benefit for curating the list in an expert manner, and the degree of their benefit increases proportionally to the quality of their curation as consumer (Nouns DAO) and candidate (beach cleaner) interest rise in lockstep.
Real world impact in different areas and the work required to make impact can be measured in many ways. So the verification and funding of this work must be open to new strategies and means of authentication, and the incentives must line up to output effective capital allocation.
After running an MVP of this streaming grants program in Vrbs, we’ve identified and highlight some problems below that this v1 aims to solve.
Zero Sum
With a fixed budget of $1,000 to stream, each new beach cleaner dilutes funding for existing recipients.
Need minimum amount of funding to make impact
Many folks might be able to make real impact cleaning a beach for $100 a month, but maybe not for $5 a month.
Complex decision for curators
In a traditional TCR, curators only have to decide whether a candidate should be on the list or not. In streaming grants, there is now another axes to evaluate: what proportion of the stream does this recipient deserve compared to others? Is the proportion they’re receiving allocating funding most effectively? Additionally, this proportion should reflect at least in part the preferences of Nouns holders.
New builders don’t receive funding
In the current implementation, even if your application is approved, you start out with no funding unless you can find someone to vote for you. This requires a lot of up-front effort for the beach cleaner.
There are many of people around the world interested in receiving money to clean beaches. With a fixed budget, the salary for each cleaner will trend to zero as more people are accepted to the list. To solve this, we propose that each budget has a maxRecipients and funding is split evenly between each recipient to start. This creates a cap on the number of possible recipients for the beach cleaning list, and fosters competition between applicants. Additionally, new builders will know that they can receive a minimum amount of funding if they are accepted, and can decide to apply or not based on that information. They also don’t have to campaign heavily for votes to get paid.
maxRecipients also reduces decision complexity for list curators. Instead of having to decide both whether a beach cleaner deserves money and also constantly evaluate whether the relative work they do is worth the money they’re getting, token holders just need to decide whether the current candidate or recipient is worthy of being in the streaming funding list of length maxRecipients.
Meritocracy
We want to reward people who are building above and beyond to be rewarded.
We plan to introduce a secondary “bonus” stream on top of the baseline salary that Nouns holders can vote to change. This will encourage existing stream recipients to compete to earn more. For the scope of the MVP, we will limit Nouns holder voting to the top level budgets for now.
We believe we are zeroing in on a powerful capital allocation primitive driven by new advances in money streaming, low txn fees on L2s, and TCRs. We plan to build in public and share updates on design decisions, problems, and proposed solutions as we build out a production grade system for Nouns DAO over the next 4 months.
Stay tuned for the next article in our streaming grants series. We plan to continue building in public to get feedback as we prepare for the protocol audit in early October.
https://docs.google.com/document/d/1BWWC__-Kmso9b7yCI_R7ysoGFIT9D_sfjH3axQsmB6E/edit
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