I was a badgeholder for the OP Collective’s RetroPGF Round 3. The experience was in parts challenging, frustrating, enlightening, and inspiring. It was frustrating because so many people — badgeholders and general community members alike — completely misunderstand the purpose and promise of RetroPGF.

This article is my attempt to explore and explain why RetroPGF — or what I’m tentatively calling Retroactive Ecosystem Revenue Replacement (RERR?) — is such a promising innovation. I also make some associated recommendations for future rounds.

I believe that the program currently known as RetroPGF has the potential to revolutionize the way we fund “public goods.” As I see it, its purpose is to prevent the underproduction of OP Collective ecosystem goods with credibly neutral revenue replacement.

Part 1 of this article is devoted to breaking down and exploring that statement, organized by its key phrases.

The purpose of RetroPGF: to prevent the underproduction of ecosystem goods with credibly neutral revenue replacement

"Prevent underproduction"

Many goods that are underproduced because they can't generate enough income to cover the opportunity cost of producing them. Sometimes this is referred to as "market inefficiency," and sometimes it's called the "public goods problem." But as we’ll see in the next two sections, it's not just pure public goods that are underproduced. “OP Collective” As the OP team has repeatedly stated, RetroPGF is not for charity but a strategic initiative designed to maximize the long term value of the OP Collective. This includes, I think, the value of the $OP token. In other words, this is a selfish, for-profit program. It is focused only on promoting the development of goods that create positive value for the OP Collective. Any value those goods create for other ecosystems is welcome but purely incidental.

Of course, the mechanism itself is not limited to the OP Collective. Any other ecosystem can employ their own instance of it. What’s important to understand is that any single instance of the mechanism is focused on promoting the value of a specific ecosystem. "Ecosystem goods" An ecosystem good is a good that creates value for its ecosystem above and beyond its users' reservation price.

Ecosystem Good: A good that creates value for its ecosystem above and beyond the sum of its users' reservation prices

From classical economics, a person's reservation price for a good is the highest amount they are willing to pay for it. This is typically a function of the value they get from using the good as well as the available alternatives/substitutes. The more the good benefits them, the more they are likely to be willing to pay. And their reservation price for that particular good likely falls if there are other goods that could provide similar benefits.

What are some of the reasons why an ecosystem good might create value that is not reflected in its users’ reservation prices?

1) Non-excludability and non-rivalrousness ("public goods").

If a good is non-excludable, its producers can't prevent people from using it even if they don't pay. Natural resources like timber and fish stocks have this property. We, however, care more about goods that are produced, and a popular example is free public transport (we'll come back to this one).

If a good is non-rivalrous, one person's use doesn't diminish the ability for other people to use it too, and so there's no need to pay for the privilege to use it before others. A good example is broadcast media, including videos, podcasts, newsletters, etc.

Together, non-excludability and non-rivalrousness form the classical definition of a public good. A good is a public good, so the typical line goes, if it is both non-excludable and non-rivalrous. But such a categorical approach devolves into a mistakenly binary understanding, leading to heated disagreements about which projects count as public goods.

The reality, though, is that the differences between types of goods is much less clear-cut, for at least several reasons. First, excludability and rivalrousness are spectrums rather than discrete categories.

Second, excludability depends on the domain of interest. What looks like a club good (non-rivalrous and excludable) from one perspective may look more like a public good (non-rivalrous and non-excludable) from a narrower perspective.

Third, there's a similar phenomenon on the rivalry dimension, where the margins are important. No good is infinitely abundant, but a good can behave very differently when it's relatively abundant compared to when it's relatively scarce. Water, for example, likely feels like a non-rivalrous public good in a rainforest and more like a rivalrous precious resource in a desert.

Finally, excludability and rivalrousness are not always inherent properties of a good that cannot be changed. Consider the free public transport example. There, non-excludability is a choice! The producers of this good could easily exclude non-paying users — certainly you have paid bus or train fare in your lifetime — but they have chosen not to.

2) The user is the product

That last point is so important that one particular flavor of it deserves its own category. I'm talking about advertising.

In one (naive) sense, advertising is the perfect funding model for "public goods." There is a reason that goods like broadcast media, social media, and search engines have so many ads. Each of these is to varying-but-large degrees both non-excludable and non-rivalrous. So instead of charging people to use them, the producers of these goods sell to other people access to those users' attention and (in some cases) data.

This is great in a lot of ways. People get to use really valuable goods for "free," and businesses get a valuable opportunity to convince those people to buy other goods that may be valuable to them. But, as we know, those goods aren't really free. We pay for them with our attention and (in some dystopian cases) our private data.

Worse, the producers of those goods optimize for something other than creating value for their users. Instead, they optimize for creating value for their customers, the advertisers. This distorts the goods themselves, sometimes in ways that cause real harm.

3) Positive externalities and network effects

Many goods create positive externalities. Scientific research often creates lots of benefits in addition to answering the specific research question. Buying a beehive and keeping bees is a fun hobby but also helps pollinate local trees.

Some externalities result from network effects, where every additional user of a good makes that same good more valuable for all the other users. Media platforms, developer infrastructure, and web3 protocols all create a lot of network externalities.

Network effects are especially interesting because producers of goods with network effects have an incentive to charge lower prices to encourage more usage and garner stronger network effects. In fact, often the optimal price for the use of such goods is zero. No wonder social media networks are some of the largest purveyors of the user-is-the-product strategy.

As a mini-summary, this section made two primary points:

A) It's helpful in practice to think more in terms of degrees of “ecosystem good-ness” than in binary terms about public goods vs. non-public goods. It's not just pure public goods that we want more of.

B) The producers of goods can in some cases trivially change the properties of their goods, and are therefore subject to incentives that we might be able to influence with the right mechanisms, such as RERR.

“Credibly neutral"

It’s not enough to produce just any ecosystem goods. The goal is to produce as many ecosystem goods as possible that create net positive value. This requires that nobody in the ecosystem be able to place their thumb on the scales. If an undeserving good is over-rewarded, more deserving goods will be under-rewarded, distorting the resource allocation signals we’re working so hard to facilitate. Worse, producing goods for the ecosystem will become less attractive, resulting in less ecosystem goods.

"Revenue replacement"

Until RetroPGF came along, ecosystem goods were primarily funded with the type of financial resource called capital. Capital is an input into a productive process that seeks to create valuable outcomes or outputs. Examples of capital allocation include all forms of investment, business loans, crowdfunding mechanisms, and most grant programs. Because it is proactive, capital only has limited information to work with; capital allocators must place bets on which goods are going to be most valuable. Some of these bets pay off, but many do not. And many — many! — goods that would have been very valuable don’t even get a chance because no allocator had the necessary vision to fund them.

Retroactive funding is different. Rather than an expectation of future returns, it is a reward for having already produced valuable outputs or outcomes; it is the return that capital expects. And unlike proactive funding, retroactive funding gets to work with the full knowledge of how much value a certain good created.

The most common form of retroactive funding is revenue allocated by paying customers. When you buy a good at the store, you are sending revenue to the business that produced the good. For that business, revenue is the return on the capital invested in producing the good. Without the potential for revenue, there would be no — or at the least, far too little — reason to invest in that business.

As discussed in the last section, revenue from paying customers and resulting market prices are, under the right conditions, the best signals for allocating resources towards the production of goods. But as we've already seen, ecosystem goods don’t meet those conditions and therefore have a hard time earning revenue from paying customers. Since typical markets cannot effectively allocate resources towards them, the production of ecosystem goods is typically funded by non-market means such as proactive grants. And like other methods of proactive funding, grants programs often fail to maximize value created both by errors of commission and of omission.

Retroactive Ecosystem Revenue Replacement (RERR) brings the benefits of revenue-based funding to ecosystem goods. When executed properly, it ensures that all ecosystem goods are economically rewarded according to the value they created for the ecosystem in question, generating the resource allocation signals that lead to the full production of ecosystem goods.

Retroactive revenue replacement recognizes that some ecosystem goods are pure public goods that have no hope of earning revenue of any sort, while others can earn some revenue but not enough to warrant their full production. Retroactive revenue replacement replaces the revenue ecosystem goods cannot earn because of their ecosystem good-ness. The amount of retroactive revenue replacement a particular good should receive is given by the formula:

$RERR (g, t) = V - E$

Where…

• $g$ is the ecosystem good in question

• $t$ is the time period in question

• $V$ is the total value that good $g$ created for the ecosystem within time period $t$

• $E$ is the income already earned by (or other value accrued) good $g$ as a result of $V.$ Note that Income includes both actual revenue and proactive funding that does not expect a financial return, such as grants. It should

One way to think about retroactive revenue replacement is as something like a government subsidy, but with a few key differences:

• Allocated by the ecosystem’s community itself rather than a centralized government

• Amounts are determined retroactively rather than proactively

• Any ecosystem good is eligible, not just those identified a priori

One last point. Just like actual revenue is the return that most proactive capital allocation expects, retroactive ecosystem revenue replacement creates the possibility of return on investment or a business loan. Allocating revenue replacement, therefore, to projects that received investment — including from venture capital — should be encouraged.

RERR creates an incentive for projects to focus on producing ecosystem goods as their primary business activity — as opposed to a side effect of their main business — as well as an incentive for investors to fund such projects.

II) How can future rounds better execute on this idea?

Here are my recommendations for Round 4 and beyond.

Change the name & clarify the purpose

Retroactive Public Goods Funding is a well-meaning label, but in practice it has been woefully misunderstood. “Public goods” is especially misleading, since it causes people to think only of pure public goods and miss the majority of what the program is trying to fund. And many people think the program is designed to fund any and all public goods when the focus is specifically on goods that create value for the OP Collective ecosystem. For all the reasons explored in part I above, my current best idea is something like Retroactive Ecosystem Revenue Replacement, or perhaps Ecosystem Goods Revenue Replacement.

Similarly, the OP team should work on clarifying and explaining the purpose of this program. A high leverage concept to explain further is the “$impact == profit$” framing of the objective. I would start by replacing profit with revenue, since the latter is more strongly correlated with value created.

Establish clear timeframes for each round

To actually serve as revenue replacement, RERR funding needs to be tied to a specific set of value created. The simplest way to do this is to set a clear timeframe for each round. For example, Round 4 could be defined to allocate funding to OP projects according to the impact they generated between January through June 2024. Require more regimented data and disclosures from projects Unlike actual revenue — which is paid by users who have decided the good is more valuable than the cost — the unique properties of ecosystem goods require that RERR is allocated by non-users. This creates a high need for accurate data, related to both the total value created by a given ecosystem good (V) and the amount of income the project already received for that value created (E).

Where should this data come from? Some of this data can be scraped from public sources, much like the excellent OpenSource Observer does. But much of the necessary data is either not publicly available or difficult for outside observers to know where to find. Rather, the people in the best position to supply that data are the project teams themselves. And since they are the most direct beneficiaries of RERR funding, it makes sense that they be required to supply it.

With badgeholder and community input, the OP team should establish a more detailed, specific, and extensive set of data requirements for projects to be eligible to receive RERR. Ideally this also includes information about how badgeholders and other analysts can verify the data they provide.

Create more tools to surface and share information among badgeholders

Even with extensive and accurate data provided with project submissions, sifting through all of the projects is no easy task. Already in Round 3 badgeholders had virtually no hope of reviewing all 600+ projects in the depth necessary to make fully-informed allocation decisions, and the number of projects will only grow from here.

Lists were a good start in the direction of sharing the load, but badgeholders need more and better tools to generate and share information with each other. Here are a few early ideas:

• A way to comment on data provided or claims made by a project such that other badgeholders can see it

• A way to flag or mark data or claims as disputed such that other badgeholders can see it

• More generally, a shared space for badgeholders to discuss projects asynchronously

Experiment with phased evaluation

Badgeholders are tasked with allocating to projects based on the fuzzy concept of “impact,” but are given no official guidance about how to assess impact in practice. While it’s correct for the OP team to not dictate this in a top-down fashion, it leaves each badgeholder with the extremely difficult challenge of simultaneously determining a) what they think counts as “impact” and b) the amount of impact created by each project.

I would like to see Round 4 address the sequential nature of these tasks with a multi-phased process. Here’s a very high level outline of how it could work:

• Phase 1: Establish what counts as “impact”. Badgeholders create a set of metrics and criteria for evaluating project impact. Given the many ways to create impact, the output of this phase should be a diverse set of metrics.

• Phase 2: Evaluate impact. Badgeholders evaluate projects based on the metrics and criteria outlined in Phase 1.

Experiment with goals => impact => metrics

We can take this phased approach even further. Much like measuring project impact is downstream of defining impact, defining impact is downstream of identifying goals and objectives. I hope to see future rounds incorporate more explicit goal-setting of some kind. Of course, the goals shouldn’t be dictated by the OP team; rather, they should be established in a bottoms-up, concave fashion.

Setting goals would have two primary benefits. First, it would provide grounding that would help determine how to measure impact, e.g. in Phase 1 described above. Second, it would help producers of ecosystem goods decide what to build.

Experiment with predictions

Prediction markets might help address the too-many-projects-to-evaluate challenge by pointing badgeholders to the projects most likely to have created high impact. Properly setting up a prediction market with well-tuned parameters is not easy, but I think it’s worth experimenting with.

Deprioritize near-term badgeholder expansion

There is a lot of work to do and a lot of experiments to run. Expanding the badgeholder group much beyond its current size is likely to make those experiments significantly harder to conduct, and unlikely to yield much incremental decentralization or credible neutrality benefits in the near term. I would therefore deprioritize expansion of the badgeholder group until more progress is made in other areas.

I was a badgeholder for the OP Collective’s RetroPGF Round 3. The experience was in parts challenging, frustrating, enlightening, and inspiring. It was frustrating because so many people — badgeholders and general community members alike — completely misunderstand the purpose and promise of RetroPGF.

This article is my attempt to explore and explain why RetroPGF — or what I’m tentatively calling Retroactive Ecosystem Revenue Replacement (RERR?) — is such a promising innovation. I also make some associated recommendations for future rounds.

I believe that the program currently known as RetroPGF has the potential to revolutionize the way we fund “public goods.” As I see it, its purpose is to prevent the underproduction of OP Collective ecosystem goods with credibly neutral revenue replacement.

Part 1 of this article is devoted to breaking down and exploring that statement, organized by its key phrases.

The purpose of RetroPGF: to prevent the underproduction of ecosystem goods with credibly neutral revenue replacement

"Prevent underproduction"

Many goods that are underproduced because they can't generate enough income to cover the opportunity cost of producing them. Sometimes this is referred to as "market inefficiency," and sometimes it's called the "public goods problem." But as we’ll see in the next two sections, it's not just pure public goods that are underproduced. “OP Collective” As the OP team has repeatedly stated, RetroPGF is not for charity but a strategic initiative designed to maximize the long term value of the OP Collective. This includes, I think, the value of the $OP token. In other words, this is a selfish, for-profit program. It is focused only on promoting the development of goods that create positive value for the OP Collective. Any value those goods create for other ecosystems is welcome but purely incidental.

Of course, the mechanism itself is not limited to the OP Collective. Any other ecosystem can employ their own instance of it. What’s important to understand is that any single instance of the mechanism is focused on promoting the value of a specific ecosystem. "Ecosystem goods" An ecosystem good is a good that creates value for its ecosystem above and beyond its users' reservation price.

Ecosystem Good: A good that creates value for its ecosystem above and beyond the sum of its users' reservation prices

From classical economics, a person's reservation price for a good is the highest amount they are willing to pay for it. This is typically a function of the value they get from using the good as well as the available alternatives/substitutes. The more the good benefits them, the more they are likely to be willing to pay. And their reservation price for that particular good likely falls if there are other goods that could provide similar benefits.

What are some of the reasons why an ecosystem good might create value that is not reflected in its users’ reservation prices?

1) Non-excludability and non-rivalrousness ("public goods").

If a good is non-excludable, its producers can't prevent people from using it even if they don't pay. Natural resources like timber and fish stocks have this property. We, however, care more about goods that are produced, and a popular example is free public transport (we'll come back to this one).

If a good is non-rivalrous, one person's use doesn't diminish the ability for other people to use it too, and so there's no need to pay for the privilege to use it before others. A good example is broadcast media, including videos, podcasts, newsletters, etc.

Together, non-excludability and non-rivalrousness form the classical definition of a public good. A good is a public good, so the typical line goes, if it is both non-excludable and non-rivalrous. But such a categorical approach devolves into a mistakenly binary understanding, leading to heated disagreements about which projects count as public goods.

The reality, though, is that the differences between types of goods is much less clear-cut, for at least several reasons. First, excludability and rivalrousness are spectrums rather than discrete categories.

Second, excludability depends on the domain of interest. What looks like a club good (non-rivalrous and excludable) from one perspective may look more like a public good (non-rivalrous and non-excludable) from a narrower perspective.

Third, there's a similar phenomenon on the rivalry dimension, where the margins are important. No good is infinitely abundant, but a good can behave very differently when it's relatively abundant compared to when it's relatively scarce. Water, for example, likely feels like a non-rivalrous public good in a rainforest and more like a rivalrous precious resource in a desert.

Finally, excludability and rivalrousness are not always inherent properties of a good that cannot be changed. Consider the free public transport example. There, non-excludability is a choice! The producers of this good could easily exclude non-paying users — certainly you have paid bus or train fare in your lifetime — but they have chosen not to.

2) The user is the product

That last point is so important that one particular flavor of it deserves its own category. I'm talking about advertising.

In one (naive) sense, advertising is the perfect funding model for "public goods." There is a reason that goods like broadcast media, social media, and search engines have so many ads. Each of these is to varying-but-large degrees both non-excludable and non-rivalrous. So instead of charging people to use them, the producers of these goods sell to other people access to those users' attention and (in some cases) data.

This is great in a lot of ways. People get to use really valuable goods for "free," and businesses get a valuable opportunity to convince those people to buy other goods that may be valuable to them. But, as we know, those goods aren't really free. We pay for them with our attention and (in some dystopian cases) our private data.

Worse, the producers of those goods optimize for something other than creating value for their users. Instead, they optimize for creating value for their customers, the advertisers. This distorts the goods themselves, sometimes in ways that cause real harm.

3) Positive externalities and network effects

Many goods create positive externalities. Scientific research often creates lots of benefits in addition to answering the specific research question. Buying a beehive and keeping bees is a fun hobby but also helps pollinate local trees.

Some externalities result from network effects, where every additional user of a good makes that same good more valuable for all the other users. Media platforms, developer infrastructure, and web3 protocols all create a lot of network externalities.

Network effects are especially interesting because producers of goods with network effects have an incentive to charge lower prices to encourage more usage and garner stronger network effects. In fact, often the optimal price for the use of such goods is zero. No wonder social media networks are some of the largest purveyors of the user-is-the-product strategy.

As a mini-summary, this section made two primary points:

A) It's helpful in practice to think more in terms of degrees of “ecosystem good-ness” than in binary terms about public goods vs. non-public goods. It's not just pure public goods that we want more of.

B) The producers of goods can in some cases trivially change the properties of their goods, and are therefore subject to incentives that we might be able to influence with the right mechanisms, such as RERR.

“Credibly neutral"

It’s not enough to produce just any ecosystem goods. The goal is to produce as many ecosystem goods as possible that create net positive value. This requires that nobody in the ecosystem be able to place their thumb on the scales. If an undeserving good is over-rewarded, more deserving goods will be under-rewarded, distorting the resource allocation signals we’re working so hard to facilitate. Worse, producing goods for the ecosystem will become less attractive, resulting in less ecosystem goods.

"Revenue replacement"

Until RetroPGF came along, ecosystem goods were primarily funded with the type of financial resource called capital. Capital is an input into a productive process that seeks to create valuable outcomes or outputs. Examples of capital allocation include all forms of investment, business loans, crowdfunding mechanisms, and most grant programs. Because it is proactive, capital only has limited information to work with; capital allocators must place bets on which goods are going to be most valuable. Some of these bets pay off, but many do not. And many — many! — goods that would have been very valuable don’t even get a chance because no allocator had the necessary vision to fund them.

Retroactive funding is different. Rather than an expectation of future returns, it is a reward for having already produced valuable outputs or outcomes; it is the return that capital expects. And unlike proactive funding, retroactive funding gets to work with the full knowledge of how much value a certain good created.

The most common form of retroactive funding is revenue allocated by paying customers. When you buy a good at the store, you are sending revenue to the business that produced the good. For that business, revenue is the return on the capital invested in producing the good. Without the potential for revenue, there would be no — or at the least, far too little — reason to invest in that business.

As discussed in the last section, revenue from paying customers and resulting market prices are, under the right conditions, the best signals for allocating resources towards the production of goods. But as we've already seen, ecosystem goods don’t meet those conditions and therefore have a hard time earning revenue from paying customers. Since typical markets cannot effectively allocate resources towards them, the production of ecosystem goods is typically funded by non-market means such as proactive grants. And like other methods of proactive funding, grants programs often fail to maximize value created both by errors of commission and of omission.

Retroactive Ecosystem Revenue Replacement (RERR) brings the benefits of revenue-based funding to ecosystem goods. When executed properly, it ensures that all ecosystem goods are economically rewarded according to the value they created for the ecosystem in question, generating the resource allocation signals that lead to the full production of ecosystem goods.

Retroactive revenue replacement recognizes that some ecosystem goods are pure public goods that have no hope of earning revenue of any sort, while others can earn some revenue but not enough to warrant their full production. Retroactive revenue replacement replaces the revenue ecosystem goods cannot earn because of their ecosystem good-ness. The amount of retroactive revenue replacement a particular good should receive is given by the formula:

$RERR (g, t) = V - E$

Where…

• $g$ is the ecosystem good in question

• $t$ is the time period in question

• $V$ is the total value that good $g$ created for the ecosystem within time period $t$

• $E$ is the income already earned by (or other value accrued) good $g$ as a result of $V.$ Note that Income includes both actual revenue and proactive funding that does not expect a financial return, such as grants. It should

One way to think about retroactive revenue replacement is as something like a government subsidy, but with a few key differences:

• Allocated by the ecosystem’s community itself rather than a centralized government

• Amounts are determined retroactively rather than proactively

• Any ecosystem good is eligible, not just those identified a priori

One last point. Just like actual revenue is the return that most proactive capital allocation expects, retroactive ecosystem revenue replacement creates the possibility of return on investment or a business loan. Allocating revenue replacement, therefore, to projects that received investment — including from venture capital — should be encouraged.

RERR creates an incentive for projects to focus on producing ecosystem goods as their primary business activity — as opposed to a side effect of their main business — as well as an incentive for investors to fund such projects.

II) How can future rounds better execute on this idea?

Here are my recommendations for Round 4 and beyond.

Change the name & clarify the purpose

Retroactive Public Goods Funding is a well-meaning label, but in practice it has been woefully misunderstood. “Public goods” is especially misleading, since it causes people to think only of pure public goods and miss the majority of what the program is trying to fund. And many people think the program is designed to fund any and all public goods when the focus is specifically on goods that create value for the OP Collective ecosystem. For all the reasons explored in part I above, my current best idea is something like Retroactive Ecosystem Revenue Replacement, or perhaps Ecosystem Goods Revenue Replacement.

Similarly, the OP team should work on clarifying and explaining the purpose of this program. A high leverage concept to explain further is the “$impact == profit$” framing of the objective. I would start by replacing profit with revenue, since the latter is more strongly correlated with value created.

Establish clear timeframes for each round

To actually serve as revenue replacement, RERR funding needs to be tied to a specific set of value created. The simplest way to do this is to set a clear timeframe for each round. For example, Round 4 could be defined to allocate funding to OP projects according to the impact they generated between January through June 2024. Require more regimented data and disclosures from projects Unlike actual revenue — which is paid by users who have decided the good is more valuable than the cost — the unique properties of ecosystem goods require that RERR is allocated by non-users. This creates a high need for accurate data, related to both the total value created by a given ecosystem good (V) and the amount of income the project already received for that value created (E).

Where should this data come from? Some of this data can be scraped from public sources, much like the excellent OpenSource Observer does. But much of the necessary data is either not publicly available or difficult for outside observers to know where to find. Rather, the people in the best position to supply that data are the project teams themselves. And since they are the most direct beneficiaries of RERR funding, it makes sense that they be required to supply it.

With badgeholder and community input, the OP team should establish a more detailed, specific, and extensive set of data requirements for projects to be eligible to receive RERR. Ideally this also includes information about how badgeholders and other analysts can verify the data they provide.

Create more tools to surface and share information among badgeholders

Even with extensive and accurate data provided with project submissions, sifting through all of the projects is no easy task. Already in Round 3 badgeholders had virtually no hope of reviewing all 600+ projects in the depth necessary to make fully-informed allocation decisions, and the number of projects will only grow from here.

Lists were a good start in the direction of sharing the load, but badgeholders need more and better tools to generate and share information with each other. Here are a few early ideas:

• A way to comment on data provided or claims made by a project such that other badgeholders can see it

• A way to flag or mark data or claims as disputed such that other badgeholders can see it

• More generally, a shared space for badgeholders to discuss projects asynchronously

Experiment with phased evaluation

Badgeholders are tasked with allocating to projects based on the fuzzy concept of “impact,” but are given no official guidance about how to assess impact in practice. While it’s correct for the OP team to not dictate this in a top-down fashion, it leaves each badgeholder with the extremely difficult challenge of simultaneously determining a) what they think counts as “impact” and b) the amount of impact created by each project.

I would like to see Round 4 address the sequential nature of these tasks with a multi-phased process. Here’s a very high level outline of how it could work:

• Phase 1: Establish what counts as “impact”. Badgeholders create a set of metrics and criteria for evaluating project impact. Given the many ways to create impact, the output of this phase should be a diverse set of metrics.

• Phase 2: Evaluate impact. Badgeholders evaluate projects based on the metrics and criteria outlined in Phase 1.

Experiment with goals => impact => metrics

We can take this phased approach even further. Much like measuring project impact is downstream of defining impact, defining impact is downstream of identifying goals and objectives. I hope to see future rounds incorporate more explicit goal-setting of some kind. Of course, the goals shouldn’t be dictated by the OP team; rather, they should be established in a bottoms-up, concave fashion.

Setting goals would have two primary benefits. First, it would provide grounding that would help determine how to measure impact, e.g. in Phase 1 described above. Second, it would help producers of ecosystem goods decide what to build.

Experiment with predictions

Prediction markets might help address the too-many-projects-to-evaluate challenge by pointing badgeholders to the projects most likely to have created high impact. Properly setting up a prediction market with well-tuned parameters is not easy, but I think it’s worth experimenting with.

Deprioritize near-term badgeholder expansion

There is a lot of work to do and a lot of experiments to run. Expanding the badgeholder group much beyond its current size is likely to make those experiments significantly harder to conduct, and unlikely to yield much incremental decentralization or credible neutrality benefits in the near term. I would therefore deprioritize expansion of the badgeholder group until more progress is made in other areas.