One of the powerful governance innovations that DAOs systematized was breaking the dichotomous “you’re either in or you’re out” approach to community membership into a more robust and granular engagement spectrum:

It provides a more gentle path for getting involved in a community and natural stopping points for community members who don’t want to go “all in”. In a world where we’re no longer just members of the single community that we were born into, but instead members of a myriad of globe-spanning communities, this is essential to tapping the broad talent pool that DAOs wish to engage.

Maintaining the principle of proportionality, different levels of community memberships come with their own set of rights and responsibilities:

Key of such benefits is the ability to participate in the DAO’s decision-making process through both deliberation and voting. Key of such responsibilities is the expectation to remain engaged in the DAO and continue to make contributions to the betterment of the DAO.

A very human complication

Most DAOs do a good job defining the trigger points for transitioning from an outer to an inner membership circle - be it holding a certain amount of the governance token or being voted into a contributor role.

The transitions in the other direction are often poorly defined and poorly enforced. This creates an unhealthy dynamic in DAOs where community members with higher/inner status benefit from the rights and privileges associated with that status, but don’t fulfill the responsibilities they are expected to carry.

It is easy to understand how we got here. Numerous socio-neurological theories (SCARF) validate our craving for status, while others (endowment effect, loss aversion) explain why we are reluctant to lose it, or give it away, once we have it. The systems we created are a reflection of our human nature.

A structural path for a solution

What then can we do to combat this very human tendency? We can create structures that make the desired behavior easier to do, and the undesired behavior harder to do.

The first, and perhaps most fundamental structural change is the decoupling of an economic stake in the DAO and a governance (voting) stake in the DAO. While early adopters in both traditional orgs (Steward Ownership) and DAOs (DAOhaus) have put this to practice, there is still a strong and popular default to keep them coupled.

The other is to create a strong mechanism that by default pulls participants towards a lower status tier, working against our natural tendency to hold on to status, and requiring intentional higher effort in order to maintain a higher status.

Here, a perhaps surprising source of inspiration can be found in the design principles of many Frequent Flier programs commonly used by airlines to solve a similar challenge:

• There is a defined period of time in which a member’s contribution is measured - most commonly the number of miles or segments flown in a given year with the airline.

• Meeting a certain threshold of this measure entitles the passenger to a set of privileges in the next time period - such as preferred boarding and free upgrades.

• By default, the status, and benefits are lost in the subsequent year, unless the passenger continues to meet the threshold measured.

• There’s also no exclusivity requirement - a passenger can have a frequent flier status with multiple airlines as long as they’re meeting the contribution requirements.

The transactional relationship between passengers and airlines makes it easier to define a simple contribution measure and anchor the status management program around it. DAO contributions are more nuanced, and will likely require a more complex, balanced scorecard approach, that would still be imperfect - but still a significant step up from the mechanism, or lack thereof, that many of us are using today.

Therefore, as I’ve argued before, understanding the patterns of community ownership and how it evolves over time is critical to maintaining the health of the community in the long run. Which brings us to today’s question - how?

As I initially suggested, a good starting point might be the Gini coefficient, a measure of wealth inequality in a nation or social group. The Gini coefficient is relatively easy to calculate, and relatively easy to understand - its values vary between 0 and 1, where 0 is the most equal distribution - all community members hold exactly the same number of community tokens; 1 is the most unequal distribution - a single community member holds all the tokens. Since it’s a normalized coefficient, it’s also easy to compare across different points in time and across different communities.

However, the Gini coefficient is not without its flaws, and Vitalik being Vitalik wrote a wonderful piece highlighting a key challenge with the Gini coefficient - its actionability.

He argued that it mashes together two separate community challenges:

• Community suffering due to lack of resources - illustrated in Dystopia A where half of the community has no tokens at all and half of the community equally shares all tokens.

• Community suffering due to concentration of power - illustrated in Dystopia B where one person holds half the tokens and the rest of the community equally shares the remaining tokens.

Both Dystopia A and Dystopia B will have the same Gini coefficient of 0.5…

Vitalik went deeper than this, reminding us that since a community token is only a portion of a person’s wealth, the number of tokens they own is not just a measure of the resources that are available to them, but also a measure of their interest in that particular community. Which certainly invites a deeper reflection on the moralistic implications of community token inequality, and perhaps an area for future research exploring how different funding sources (DAO treasuries, DEX, etc.) affect token ownership distribution. Measuring token ownership distribution is a dimension of community health, but looking at community health just through that lens is rather myopic.

Nonetheless, we want to overcome the bundling of dystopia A and dystopia B challenge and Vitalik proposed a few indices that do just that:

• Dystopia A can be independently measured through the Thiel L index.

• Dystopia B can be independently measured through the Herfindahl–Hirschman index, the Theil T index, or the Nakamoto coefficient.

So I went ahead and did just that. I’ve built a Dune Analytics dashboard that calculates the various indices for a given ERC-20 community token. You can find it here.

Here’s how the different indices look like for the Bankless DAO community token (BANK):

And for the Braintrust community token (BTRST):

Along the way, I’ve come across some unique challenges in measuring these indices for community tokens. Some I was able to overcome. The rest are areas for further research:

1. Dust: the exchangeable nature of different community tokens means that even people who’ve opted to leave their community and sell all their community tokens may still be left with some “dust”, a small fractional position that they can’t get rid of. If we count them in our analysis - it’ll likely skew the results. Overcoming this challenge is relatively straightforward by imposing a minimum number of community tokens that a wallet address must own in order to be counted in the analysis. Note, however, that this threshold may vary greatly from one community to the next, while holding 0.1 BANK is negligible, holding 0.1 of BTC isn’t. The dashboard above accounts for that and allows the user to set their own threshold for the token they are exploring.

2. 1 wallet address != 1 person: this feature of Web3 can be broken down into two separate challenges. The first challenge is “one wallet, many people”: a smart contract or a multisig-treasury that is not directly linked to a community member. For community tokens, many of the top wallet addresses actually fall into this category so the generic but crude solution I’ve used is adding the ability to omit the N top wallets from the addresses. The unfiltered “Token current holders” table gives the user a rough sense of how many top addresses to omit. For a specific token, specific known treasury or smart contract addresses can be added to an explicit “black list” that’s ignored in the analysis. The second challenge is “many wallets, one person”. This one requires an explicit decision of the community to use some variant of a proof-of-humanity technology in order to fully overcome it.

3. Community patterns: the community context in which those indices are used creates its own set of challenges. The population of a country tends to change rather slowly, a single-digit % a year if not less. Communities tend to be more volatile than that with double and triple-digit % change annually. In countries, new adult members (both graduating children and immigrants) join its ranks in various places across the social-economic spectrum, whereas in communities the vast majority of new joiners join with zero tokens. The combination of these two patterns means that several of the indices covered here are particularly sensitive to community growth and therefore the community growth rate needs to be taken into account in interpreting the results.

4. Collusion: as Vitalik pointed out, these wallet-based indicators only tell part of the story. Even if we had full mapping of wallets to community members, it would still not capture the relationships between those community members and their ability to collude with each other. For example, a small cohort of whales deciding ahead of time to always vote the same way. The indices only give us a partial view that needs to be supplemented with other means. Probably a topic for a whole new post :)

About talentDAO: talentDAO is a community of organizational scientists, strategists, and researchers with a shared mission to unlock human potential in the decentralized, digital economy. We conduct scientific research that helps DAOs thrive while educating the public on the greater decency and agency offered from this decentralized future of work.

While in Web2 contexts this information is sometimes used to exasperate the information asymmetry between employer and employee, sharing this information freely with the DAO members is part and parcel of the DAO’s transparency ethos.

As DAO membership crosses the 1,000 members threshold, using this data to proactively manage the health of a DAO is no longer a luxury but a necessity.

This post is meant to act as a “stake in the ground” starting point for a conversation around the most important metrics that every DAO should be tracking. I am confident that my thinking here will continue to evolve, and I’m looking to learn from the wisdom of the community around this topic.

What is a healthy DAO?

Identifying good, actionable metrics cannot start with what is easily measurable. Instead, they start with a thesis on what a healthy DAO is and therefore, what metrics we may want to collect as a result. From there, we can start making some pragmatic compromises on what we can feasibly measure.

A healthy DAO is too vague of a starting point, so I’m decomposing it to a definition that we can better work with:

Healthy DAO = healthy community + healthy governance + healthy finance

We can now take it dimension and break it down to a handful of metrics that matter.

Throughout this exercise, we’ll focus on views that show us how the metrics we chose evolve over time. With no clear benchmark yet for “what good looks like?”, looking at trends provides us with greater insights than any specific point in time.

Community health

1. Member conversion funnel

What is it? at its most simplistic form, the member conversion funnel tracks the change in the number of members who have joined and left the DAO over a certain period of time. In DAOs that have defined a more robust membership model with multiple membership tiers, it also tracks promotions/advancements from one tier to the next.

Why does it matter? members are the essence of a DAO. Without members, the DAO cannot accomplish its purpose. The member conversation funnel provides more actionable insights than simply looking at gross members’ growth and can direct the DAO’s energy to a more pointed issue. For example: should we focus on amping up our recruitment efforts? or figure out why so many people join and then immediately leave?

As the DAO matures, more nuanced views can be utilized by layering different member “demographic” attributes over the visual or transitioning to a more complex cohort-based analysis.

2. Small world score

What is it? a normalized score (between 0 and 100) which measures the fit between the overall shape of the DAOs collaboration network and a small world network - a network where the typical distance (the number of steps required) between two randomly chosen members grows proportionally to the logarithm of the number of members in the network

Why does it matter? DAOs want to create communities in which complex ideas can spread but are less susceptible to groupthink. Research (check out the references section here) had found that small-world networks are ideal for that purpose. looking at how the DAO’s small score evolves over time, can give it a sense of whether it’s trending towards or away from that ideal and take the necessary action. As the DAO matures, decomposing the small world score into its components can provide even more pointed insight.

3. Top-5 connectors & outliers

What is it? a list of the top-5 members with the highest betweenness centrality score and a list of the top-5 members with the lowest closeness centrality score.

Why does it matter? as the DAO scales, the “extremes” of the community become a critical source of insight for community health initiatives. Yet figuring out who those members are is becoming harder since no single member of the community has a nuanced sense of the entire community. Betweenness score is an algorithmic method to identify the extremes which enables both taking pointed action with those individuals (preventing burnout, reducing attrition) and identifying broader patterns in the community.

Governance health

4. Proposal approval rate

What is it? the % of governance proposals in a given period of time that get approved.

Why does it matter? perhaps counterintuitively, in the context of governance health, more is not always better. The approval rate for proposals has a “goldilocks” zone (let’s peg it at 65-90%) - too high of an approval rate may suggest: groupthink, lack of psychological safety or moving too slow/cautiously; and too low of an approval rate may suggest: real discord or misalignment, unhealthy conflict, or trying to move too fast/riskily. Whether the approval rate falls inside, above, or below the goldilocks zone will lead the DAO to take different actions to improve governance health and the health of the DAO overall.

5. Voting participation rate

What is it? the % of community members who have participated in a particular governance decision. A more complex version of this metric can overlay additional community information, such as different membership tiers, on the visual.

Why does it matter? a bit part of the DAO ethos is engaging the entire community in governance decision rather than leaving it in the hands of a select few. Tracking the community’s participation in governance decisions can give us a good sense of the progress we’re making towards that ideal.

6. Rolling voting participation rate

What is it? the % of community members who have participated in governance decisions over a certain period of time.

Why does it matter? While ideally, we want a large portion of our community to participate in governance continuously, we may not be there yet. Making progress towards that aspiration requires a more nuanced understanding of how the community is engaging with governance. A good starting point is figuring out whether the same people engage in governance continuously, or different people engage in governance more sporadically. Comparing and contrasting the “single decision” voting participation rate and the “period of time” voting participation rate can help shed light on that.

Financial health

7. Top 10 outflows

What is it? tracking the Top-10 spend categories of the DAO token from the DAO treasury evolve over time.

Why does it matter? Where the DAO spends its tokens implicitly reflects where its priorities lie. Comparing the spending pattern to the DAO’s explicit priorities can give us a good sense of whether these two things are well aligned.

8. Plan vs. actual spend

What is it? A month-by-month (or week-by-week) tracking on the amount of community token the DAO intended to spend, according to its seasonal plan, compared to the amount that was actually spent.

Why does it matter? As DAOs mature, basic fiscal predictability becomes essential to healthy finances and ensuring smooth on-going operations.

9. Community token Gini index

What is it? The Gini Index is a measure of wealth inequality in a social group. It varies between 0 (when everyone is holding the exact same amount of wealth) and 1 (when a single person is holding all the wealth) and therefore offers a quantitative measure of inequality in the group.

Why does it matter? As DAOs are meant to be owned by their communities, understanding the pattern of ownership becomes important. The trajectory here is more important than the point-in-time value: is DAO ownership become more concentrated or more distributed over time? In which direction do we want it to trend? and what can we do about it?

In Sum

In this article, we’ve defined a basic model for DAO health and suggested a starter set of 9 metrics that can enable a DAO to track and manage its health over time.

Is this the perfect set of metrics? probably not. Is this exactly the right set of metrics for your DAO? again, probably not. But is this a good starting point to being the conversation about this important topic? we sure believe so!

About TalentDAO TalentDAO aims to give 1 billion people access to ‘decent work’ through self-sovereign work arrangements offered by thriving, successful DAOs. We offer analytics products and talent strategy consulting services to the decentralized autonomous organization (DAO) ecosystem.

E3Os aim to operate in a highly decentralized way. Rather than viewing themselves as centralized, monolithic organizations providing a set of products and services, they view themselves as ecosystems or platforms that support that emergence, funding, and on-going operation of entrepreneurial initiatives that are aligned with the overarching competitive advantage of the ecosystem. It can be deep expertise in a domain (electronics) or a particular strategy (delivering exceptional customer service).

While they may show in different E3Os under different names, E3Os utilize a consistent set of organizational building blocks:

• Micro-Enterprise (ME) - small unit (<20 people) with unique identity and reputation, providing a self-managed product or service and operated according to a standard set of health indicators defined by the E3O such as a profit & loss statement, a set of input and outcome metrics, or a utilization target.

• Shared-Services Platform (SSP) - a central entity providing services such IT, HR, Legal, and Finance to the MEs in the ecosystem and is funded by a mix of periodic tax-like payment and a-la-carte services fees. The centralization of those functions is often driven by economies of scale or regulatory compliance requirements. Different E3Os give MEs different leeway in using external vendors for services provided by SSP. If this leeway is limited, SSPs are held to explicit SLAs that they must meet.

• Ecosystem Micro-Community (EMC) contract - a multi-party, transient contract between MEs, SSPs, and investors defining what each of them will deliver in support of achieving a collective outcome together. Often, the creation/delivery of a new product or service. The contract also defines how future revenue from the new outcome will be distributed among all the parties that contributed to making it a reality. The initiating ME will define the aim and numeric goal of the EMC and list of “orders” - the technical requirements needed by other MEs and SSPs. MEs and SSPs in the ecosystem then bid on the various orders, specifying the conditions they are ready to meet and their expected return for providing their services (fixed lump sum, % of profits from the outcome, etc.)

• Value Adjustment Mechanism (VAM) - VAM is the mechanism used by E3Os to incubate new MEs. It’s a mix between a term sheet, a contract, and a corporate budgeting statement. They are signed between the members of a new ME and the broader E3O capture the new ME’s aim and leading goals, its plan to accomplish those goals, the needed resources (including capital) required, the inflection point that will define when the ME is self-funding/sustainable, and the upside for ME members.

Cicero et al. also identified a set of processes that support to effective operations of E3Os:

Bridging from E3Os to DAOs

Organizationally, DAOs share a lot in common with E3Os. Perhaps most importantly, the decentralized ethos and the aspiration to act as a container for emergent entrepreneurial initiatives that support the DAOs broader mission.

Operationally, DAOs are still in their early days. The influx of talent into DAOs means that many of them are working hard to catch up to their new scale of operations and the nascent/lightweight practices of the DAO’s early days no longer meet their needs. The E3O pattern/playbook can be one source of inspiration to both avoid making “old mistakes”/reinventing the wheel, and identifying organizational challenges where web3 technology can provide unique leverage in removing friction from large-scale collaboration/coordination challenges and reduce transaction costs even further.

Questions to move us forward:

• How do existing/future guilds and projects of the DAO map to MEs and SSPs?

• What is our DAO-wide measure for assessing the health of MEs?

• Can VAM-like objects be used to spin-up new initiatives?

• Which E3O processes best benefit from web3-based solutions? and how can existing web3 solutions be adapted to better support E3O processes?

To better understand the future of human collaboration, we must first better understand its past: what has led to the emergence of the modern firm? why did people choose to form companies rather than continue to trade bilaterally through contracts in a market? and what can we learn from that about the current dominance of companies in modern economic activity?

In 1937, a young Economics prodigy named Ronald Coase, at the tender age of 27, offered one of the most compelling explanations to these questions in an influential paper titled The Nature of the Firm: Coase argued that markets are not as perfectly efficient as they were believed to be at a time. Every transaction has a transaction cost that goes beyond the cost of the goods exchanged. Those additional/external transaction costs can be things like finding the right exchange opportunity, negotiating a contract, or enforcing its execution. In situations where those transaction costs are particularly high, several individuals banding together as a firm allows them to reduce those costs and coordinate more efficiently. However, the firm introduces costs of its own. For example, in the form of overhead, or human errors that lead to ineffective resource allocation. Therefore, the size of a firm will reflect an optimal balance between the market costs and the firm costs.

This explanation later became known in the Economics canon as “Coase Theorem” and earned him the Nobel Prize in Economics in 1991.

Considering the impact of technological innovation on human collaboration since 1937 (or even since 1991 for that matter), Yochai Benkler offered a thought-provoking observation in a 2016 essay titled Peer production, the commons, and the future of the firm:

When technology dropped the cost of communications, distributed the material capital necessary for knowledge work throughout a large population, and allowed individuals to share designs and incremental improvements with each other, these individuals were able to pool their knowledge and resources, and coordinate action toward shared goals, without the mediation of firm hierarchies or markets.

Benkler then argues that a nimbler, more ad-hoc collaboration structure defined as Peer Production had already started crowding out the firm in several collaboration domains, such as open-source software projects.

This observation opens the door to an even bigger question: in this modern era, is there still a need for the firm?

Benkler identified two domains where the answer was still “yes”.

The first, still under the Coase-ian paradigm, is simply where transaction costs are still high:

(a) when [firms] have the advantage in amortizing high capital costs over many diverse innovation efforts, where optimization of that innovation and its manufacture and distribution are core necessities,or

(b) when exploiting legally-created rent extraction opportunities gives an advantage to a continuous legal entity such as a firm over a more fluid market relationship that comes together purely for the purposes of exploration and innovation.

The second, and perhaps broader domain, recognizes that Coase Theorem only offers a partial explanation to the existence of firms, as it only views people through the homo economicus lens, rather than the complex social creatures that we are. The motivation to create firms was not purely efficiency, firms also helped address a deeper human need:

a model of motivation that assumes that belonging to a socially-meaningful interaction is critical for engaging the full capacities of people suggests a continued role for firms that can integrate social meaning and relations into their organizational ethos and practice… [I]n the presence of substantially lower transactions costs and competition from nimble, flash organizations and non-market innovation, building communities of meaning around economic collaboration is the primary form of strategic advantage firms have over dynamic, fluid networks of collaborators.

Looking at DAOs through both Coase-ian and social lenses helps highlight the incredible potential of this new form of collaboration:

First, they build on everything that Peer Production had already figured out, leveraging technological innovation to minimize transaction costs and maximize the opportunities for self-organization, without having to revert to more authoritarian forms of collaboration.

Second, they meet firms toe-to-toe on the Coase-ian front, in both their ability to raise capital and amortize investments; and when properly wrapped in a thoughtful legal structure, take advantage of the same regulatory opportunities.

Last, and perhaps most important, they offer deep communities of meaning to their members. Far beyond what the typical firm is capable of offering.

These three aspects not only explain the potential of DAOs today, but also where they should continue investing to become the dominant form of collaboration for the years and centuries to come.

It was recently brought back to my attention through Ed Batista’s post: To README or not to README. Ed’s been on a tear of good posts lately and I’d highly recommend checking out his blog.

To quickly recap, the idea of creating a Manager README document seems to trace back to the first of such documents being put together almost 10 years ago, in 2012, by a gentleman named Luc Lavesque. The document’s basic premise is highly benevolent — as a manager, deeply reflect on your own work style and preferences and write them down in a sharable doc. The thesis is that this act of self-disclosure, outlining your communication preferences, how you give feedback, your working hours, etc. will create more explicit expectations for your team and lower the likelihood of misunderstandings.

Several different posts have been written about how publishing these documents can backfire, which Batista summarizes rather succinctly:

• A Manager README is no substitute for a feedback-rich culture — and in the absence of such a culture it may even be counter-productive, signaling to employees that their input on your inevitable blind spots as well as your performance as a manager is unwelcome.

• The use of a README without carefully attending to the power differential between you and your employees can inhibit psychological safety. The mere existence of such a document doesn’t necessarily create a less-safe environment — the impact stems from how it is employed.

• The purpose of any such document is a clearer understanding of work style differences, and that needs to be a two-way street. Expecting employees to conform to your preferences without making an effort to understand and adapt to theirs isn’t management, it’s coercion.

It’s that last critique that I want to focus on a bit more. The first two mostly highlight that the README artifact will only have a positive impact in the right context — as part of the feedback-rich culture, while being mindful of the power dynamics. The second critique starts pointing into a more profound issue which is more fully articulated in the third one: the README artifact only captures one side of the dynamic — “this is who I am, and this is how you can best work with me” — where, in fact, this artifact aims to enable better collaboration. And collaboration requires more than one person…

This suggests, that the structure of the artifact is all wrong. It shouldn’t be about you, or me — it should be about us. What are our different work style preferences? and what collective agreements are we making together about the way we work, so we can collaborate well, despite those differences?

Fortunately, we don’t have to start from scratch. The team at SY Partners created a lightweight construct called “How we roll” (see screenshot above) for capturing these differences and beginning to hash out the mutual agreements for working together. In my opinion, it’s a much better starting point than an individualistic README for making the implicit explicit and starting an honest dialogue that can truly help improve collaboration.