Reinventing Organizations: Quantitative Approaches to Understand DAO Governance
Xqua
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March 12th, 2022

Abstract

In this three-part article, I present some new insights into the DAO governance space, taking a data-centric approach to analyzing key aspects of the DAO ecosystem, including membership and participation. In Part 1 I give a general introduction to DAOs and organizations and then share the questions that are guiding my current research in the DAO space. In part 2, I dive into trends and patterns that emerge from applying network analysis and data science on the digital traces collected from Snapshot and DAOHaus. Finally, in Part 3, I take the MetaCartel DAO as a case study of governance and look at some of the properties that drive participation. In each section, you will find a collectible NFT. The proceeds from each NFT will go directly towards supporting current and future Diamond Dao community research initiatives. A little spoiler, the DAO network is scale-free, so big hubs have the majority of members, while most DAOs only have a few members! Want to know more, read on!

Article Structure

  • Part 1: An introduction to Decentralized Autonomous Organizations, their connections to organizations, and open research questions
  • Part 2: A Quantitative Study of the DAO Ecosystem
  • Part 3: A DAO Governance Case Study: The MetaCartel DAO

Feel free to skip ahead to part 2 or 3 if you only want to read about the data.

Part 1: An introduction to Decentralized Autonomous Organizations, their connections to organizations, and open research questions

The social embeddings of human behaviors drive organizations

Before we look at the properties of DAOs, let’s wonder about where do organizations come from? Very early on in life, humans invent rules to organize themselves. Let's remember the playground, where kids will play roles, you are the farmer, you are a merchant, you are a mayor, etc. When kids decide to attribute functions and to role-play they integrate themselves into a social structure of norms, accepted forms of relationships. Through this, children learn how to behave, what is acceptable, what is not, and this creates a set of underlying behaviors that will govern their social interactions. Those rules will then transpose themselves later in life, and shape the structures of organizations that are invented by those individuals. The "innocent" playground interactions will then become truism about the necessity of a person "in charge" having power over other people, that wealth is equivalent to power. Those learned behaviors represent the social aspect that underlies the creation of organizational structures. Our capacity to imagine relationship mechanics is embedded in our past and our past is embedded in organizational structures which shaped us. It takes a large amount of openness and creativity to create novel structures and escape our own biases. This is the question of how do you escape a phenomenon called organizational isomorphism. Therefore, are DAOs realizing a rift in organizational structures?

The Decentralized Autonomous Organizations

Homo sapiens are social animals, but unlike (most) other species, we never stopped inventing novel methodologies to organize, regulate, and fluidify our social relations. From a general perspective, we can say that an organization is a tool that allows humans to achieve a set of goals more efficiently than if any individual were to attempt the same on their own. In April 2016, the first Decentralized Autonomous Organization (DAO) appeared on the Ethereum Blockchain, an intriguing entity that utilized the power of the composability of Ethereum to write laws (a set of agreed-upon social mechanics) that would govern the entity. Any single individual could participate in the process, but the process was not governed or enforced by anyone but itself: Code is Law.

In that sense, DAOs are reaching the anarchistic ideal of An-Archos, the absence of a chief; at least in the sense that the computer algorithms governing the relations cannot be manipulated by individuals. This “code is law” perspective allows the potential realization of efficient anarchistic organizations. Despite the overall libertarian ideology of the Crypto space, the invention of DAOs as methods and tools to enable the emergence of governing bodies, organizations, teams, and social structures is more deeply rooted in Anarchy. It is my opinion that this reflects on the weakest point of libertarian ideology, which puts the individual over any form of organization and therefore hinders the emergence of larger-scale governance structures. In what might be the space with the highest concentration of libertarian ideology, organizational structures have emerged and people reinvent the concepts of commons, sharing, decision making, and fairness.

I am hopeful that we will see the organic inception of new and deeper social philosophy, anchored in teachings from Anarchists, Libertarians, and other political philosophies. Therefore, parenthesis aside, I here propose that we work under the assumption that DAOs are trying to reinvent organizations, by relying on trustless and decentralized state machines: the blockchain technologies. Working under this assumption, we can then compare DAOs to other forms of organizations, or at least, study the DAO space under lenses gleaned from observing companies, classrooms, societies, and other forms of human meta-structures.

Here is the most general, (I think) commonly agreed-upon definition of a DAO:

  • Decentralized: It must exist beyond borders, individuals, or existing organizations.
  • Autonomous: It must rely on its own structure to govern itself, exist, and thrive.
  • Organization: A group of humans with a common goal and a set of accepted social laws underlying interactions

In essence, DAOs do not need the blockchain, and individuals exchanging messages through the postal services could create an organization with these same properties. However, what decentralized trustless state machines like Ethereum are offering is a medium that no one can own or control: therefore, it creates a fertile ground for innovative DAOs to emerge and grow.

The Relationship Between DAOs and Organization Structures

Realistically and from an evolutionary perspective, Humans are animals. Mostly but not only, Western cultures have ingrained a deep belief that humans are separated from nature, and are not governed by “instincts”, contrary to other cultural organizations that tried to mirror the natural world and embraced our identity as social animals. Despite this attempted deviation from nature, we know that most of our behaviors have been encoded over millions of years in the very structure of our brain. When we feel the urge to connect to other humans, we are following deeply rooted neuro-behavior cues that evolution selected and favored. Our capacity to self-organize organizations is very likely an evolutionary advantage that allowed humans to transcend our susceptibility to the natural elements. If we are hard-wired to organize, does this mean that the form of organizations that will emerge from those instincts may never take an infinite amount of shapes? Are we only able to see the emergence of a subset of forms of organizations within a much larger space? It seems at least that we can imagine a much larger space than the one we have explored, the very prolific topic of political philosophy is a good example of our capacity to create a, maybe, infinite number of utopias and dystopia. But from this set, how much have we actually explored? From this perspective, are DAOs confined within the realm of imaginable organizations? Do they exhibit the same properties as past organizations due to our inability to create something truly new?

Studying Humans Requires Both Quantitative and Qualitative Methodologies

I subscribe to the quantitative world, my tools are data science and algorithms, and the rest of this piece will try and bring some valuable quantitative insights into what is happening in the DAO space today. But, I must recognize that when studying complex phenomena such as human beings, it is very hard to rely only on quantitative measurements. Indeed, to quantify, one must create or assume a proxy, a measurement. When we decide to talk about a team, how do we create the relationship between individuals? Are they connected because they are in the same room? Because they participate in the same meetings? Have the same skills? The same gender?

In the case of DAOs, there exists a large amount of available data, but to fully make sense of it, people will need to go further than only data-driven methods. Fine-grain understanding will only be achieved when quantitative researchers will partner with qualitative academics that will bring with them other tools, such as careful interviews, coding (not computer coding, qualitative analysis coding), and backgrounds in slow, methodical teasing apart of the complexity. In the remaining two sections, I will present data-driven results and will try to avoid strong conclusions about human behaviors, and instead try to propose potential hypotheses that emerge from the data.

Open Research Questions In the DAO Space

So, what should we ask about DAOs? Because we can consider DAOs a form of human organization, we should study them with the same lens as we are for companies, institutions, open-source projects, or collectives. I’ll conclude this section with a list of interesting and open questions which I think are worth asking.

  • In comparison with the normal world of companies, how is the wealth distributed within DAOs? Are DAOs fairer?
  • Are DAOs more efficient at producing work than normal organizations?
  • Does collective intelligence emerge at a higher rate in the DAO space as it does in normal institutions?
  • What are the quantitative advantages and disadvantages of the different DAO tool stacks?
  • Are DAOs more resilient than normal institutions?
  • Are DAOs nonhierarchical, or do they pretend to be under the disguise of decentralized technologies?

Support the Labs

One of the core missions of Diamond DAO is to study and understand the DAO space as a whole. This happens through the creation of Chainverse and a curation process, and through the funding of more open research projects in the space of DAOs. To help support more research like this one, you can make a donation by acquiring this NFT. The artist is, well … myself (Shameless plug: I also do visual art). By purchasing this NFT 30% will go back to me to support further research in the space of DAOs, and 70% will go to a wallet that will be used to fund other researchers interested in studying this space. I hope you enjoy this nice looping animation!

Part 2: A Quantitative Study of the DAO Ecosystem

Welcome to Part 2 of this article about quantitative analyses of the DAO space. If you arrived here by chance, feel free to head over to Part 1 which is a general introduction. If you are only interested in learning about the MetaCartel DAO case study head over to Part 3 directly. In this section, I take an overview of the DAO ecosystem by looking at the connectivity between the members and the DAOs, as well as the participation rates and the consensus-building dynamics in DAOs.

A Network-Centric Analysis of the DAO Ecosystem

Here, I examine how the ecosystem of DAOs is currently evolving. What are some trends and behaviors that emerge? This article is a bird’s eye view on some properties that can be measured across all DAOs such as participation rate, consensus, and network connectivity. In this section, I am using data from Snapshot and DAOHaus, from their inception up to the end of 2021, with 1727 DAOs, 20415 proposals, and 303802 wallet addresses. This data was embedded in a large network which ontology you can see just below. For this article, I did not use the Discourse data (in red) due to the difficulty that arises from mapping one-to-one usernames and wallet addresses.

The knowledge graph ontology I created to analyze the DAO space resulting from scrapers made in collaboration with Diamond DAO. Colors are the different platform and node types, and edges are labeled with their ontology.
The knowledge graph ontology I created to analyze the DAO space resulting from scrapers made in collaboration with Diamond DAO. Colors are the different platform and node types, and edges are labeled with their ontology.

The DAO Ecosystem: A Network-Centric View of DAOs and Membership.

I first aggregated all wallets and looked at their network similarity by computing the Jaccard index of each node and creating a connection between two nodes if their coefficient was superior to 0.1. Below is the largest connected component, which shows multiple communities of interest, with members being connected through multiple DAOs. This highlights the fact that people are not always staying within a single DAO.

Voter's similarity network. This shows the largest connected component of the Jaccard Index similarity voters network. Two wallets are connected if they share common proposals and DAOs.
Voter's similarity network. This shows the largest connected component of the Jaccard Index similarity voters network. Two wallets are connected if they share common proposals and DAOs.

Let’s now turn to some of the properties of the DAO space: first, let us observe the DAO degree distribution of the Proposals->DAOs and Proposals->Voters’ projections. By looking at the degree distribution we can infer the structure of the graph.

Degree distribution of the DAOs number of proposals and voter's number of votes on log-log plots. This informs about the structure of the network, and we can see a linear shape of the distribution, aka a scale-free distribution.
Degree distribution of the DAOs number of proposals and voter's number of votes on log-log plots. This informs about the structure of the network, and we can see a linear shape of the distribution, aka a scale-free distribution.

Interestingly, both the DAO’s number of proposals and the voter’s number of votes display a scale-free distribution, a property that we see in many naturally occurring networks that follow a preferential attachment model. What this implies is that the more a DAO grows in proposals, the more proposals will get added to it, and similarly, the more a person votes, the more they will get inclined to vote. This is a great sign for DAOs and members that get in the system, but what this also means is that there is a “long-tail” of members and DAOs with very little participation. One of the key action points in the space might be to try and raise the end of the tail to approach a more uniform graph. What this means is to increase members’ participation and overall DAO activity.

The DAO network. There exist two large connected components, and 12 communities seem to emerge from this graph. At the top left is the degree distribution of this graph. The colors represent the communities.
The DAO network. There exist two large connected components, and 12 communities seem to emerge from this graph. At the top left is the degree distribution of this graph. The colors represent the communities.

To finish on the DAO ecosystem, let’s look at the network of DAOs. This network connects two DAOs if they share at least one member in common. Apart from a small number of disconnected nodes, almost all DAOs are connected in two large connected components. Across both components, we observe that 12 communities emerge. If we look at the degree distribution of this network, we also see emerge a scale-free distribution. This is a lot more intuitive, as the more a DAO grows, the more attractive it becomes for other members to want to join it. The fact that there exist two main components is fascinating in itself, especially because those are not DAOHaus and Snapshot as DAOs from both systems are in those. This raises questions about who is participating in those communities, what are their commonalities, and whether we can try to classify them and their members by interest, geographical location, language, or other means.

Understanding Member Participation and Retention Across DAOs & Comparing Snapshot to DAOHaus.

Given the scale-free nature of the networks, it becomes interesting to look at the retention of DAOs. If people only come to vote once, can we say that this form an organization? To look at this issue, I used the Snapshot dataset, and to make sure I removed some of the noise coming from potentially “test” DAOs, I removed all DAOs with a single proposal. I plotted the distribution of the number of active voters in a DAO. Interestingly, this is skewed towards low numbers, meaning that most DAOs have low voter retention, indeed here voters represent people who voted at least once, therefore, a low number reflects that people mostly do not vote on most proposals. I wondered whether this was due to a potential proposal burnout, whereby when the number of proposals is high, the proportion of active voters would decrease. I normalized the number of space proposals by the community size, and then plotted it as a function of the proportion of active voters, we see a highly noisy, but flat trend. This reflects on the fact that it seems the voter retention issue is not due to a voter’s burnout issue. Therefore, I hypothesized that DAOs will need to think about better methods to increase the overall engagement of their community, but that it won’t be by reducing the number of proposals.

Voters distribution and voters retention on Snapshot. On the left, is the distribution of active voters in all DAOs. On the right, is the normalized number of proposals per member as a function of active members. Active voters are corresponds to members who voted more than one time.
Voters distribution and voters retention on Snapshot. On the left, is the distribution of active voters in all DAOs. On the right, is the normalized number of proposals per member as a function of active members. Active voters are corresponds to members who voted more than one time.

Another way to look at this problem is to observe the participation level of each member in the DAOs. This is akin to a “conversion funnel” whereas as time passes a member can become more engaged and gain more rights. But as most DAOs do not have a conversion funnel in place, I decided to observe the participation rate. What we observe is an exponential decay, where the large majority of members do not participate more than a few times in a DAO. Interestingly, this effect is less strong on Snapshot than on DAOHaus, which might be explained by the ease of voting on Snapshot. Indeed a member-only needs to validate the criteria to be able to vote, whereas on DAOHaus they need to first become a member. To me, this is still surprising, as one would expect that the added friction to become a member of a DAO on DAOHaus would motivate people to perform more actions.

Participation funnel across DAOs on DAOHaus and Snapshot. In grey are every single DAO, in orange, mean, and standard error. On the right, is the comparison between DAOHaus and Snapshot.
Participation funnel across DAOs on DAOHaus and Snapshot. In grey are every single DAO, in orange, mean, and standard error. On the right, is the comparison between DAOHaus and Snapshot.

Consensus building in DAOs: too much group think? complete dissent? or a nice balance?

An essential property of an organization is its capacity to be aligned in its decision-making, a process that we can call consensus building. One of the empirical metrics of “good decision making” is that a group should have an agreement rate between 65 and 90%. The reasoning is that too low means the group is completely nonaligned, whereas too high shows too much group think and not enough critical spirit. For this analysis, I only looked at DAOHaus data, as Snapshot does not have a binary yes or no voting system, and extracting consensus is nontrivial and depends on each DAO and each proposal.

Analysis of approval rate across DAOs of DAOHaus. The rescaled time axis is proportional to the number of proposals a DAO has made, normalized by the final amount. In red are DAOs that fall outside the range at the latest time, and green are DAOs that fall within the 65-90% at the latest time.
Analysis of approval rate across DAOs of DAOHaus. The rescaled time axis is proportional to the number of proposals a DAO has made, normalized by the final amount. In red are DAOs that fall outside the range at the latest time, and green are DAOs that fall within the 65-90% at the latest time.

When I looked at the distribution of DAOs according to their approval rate (at the latest time-point, so across all the DAO’s lifetime), we can see that many DAOs are not within the 65% to 90%. To be more exact, a majority of DAOs (>250) fall outside that range. To try and understand the dynamic of consensus building in DAOs, I observed the evolution of the approval rate in every DAO. To be able to compare the DAOs between each other, I re-scaled the time by the number of proposals in a DAO. Indeed, DAOs have existed for a different amount of time, so comparing their “activity” time allows for a clearer picture. What we can see is that there seem to be three main dynamics. Either DAOs start high and stay high, or they start at some value and converge towards a high approval rate, or on the contrary, they descend towards dissent, and the approval rate drops towards zero. What mechanisms underlie those trends will be a very interesting question to pursue further.

Support the Labs

One of the core missions of Diamond DAO is to study and understand the DAO space as a whole. This happens through the creation of Chainverse and a curation process, and through the funding of more open research projects in the space of DAOs. To help support more research like this one, you can make a donation by acquiring this NFT. The artist is, well … myself (Shameless plug: I also do visual art). By purchasing this NFT 30% will go back to me to support further research in the space of DAOs, and 70% will go to a wallet that will be used to fund other researchers interested in studying this space. I hope you enjoy this nice looping animation!

Part 3: A DAO Governance Case Study: The MetaCartel DAO

Welcome to Part 3, if you arrived here on purpose that’s great, but if you want to read the full series you should start at the beginning. If you are interested in learning more about DAOs as a whole, head over to Part 2. This last article in the series is a quantitative case study of the Metacartel DAO governance. It looks at the equality, participation, and incentives that emerge within the network of actors of the DAO. This organization relies on the tools hosted by DAOHaus and is the biggest and most prominent DAO on the DAOHaus platform. Here I explore what we can learn from looking at the digital traces left from its activities.

The different communities of the MetaCartel DAO

The DAOHaus tooling provides organizations with a membership system in a smart contract form that manages governance and investments. Members will create proposals and then vote in favor or against them. To try and understand how people collectively decide on proposals I first created the networks of members and proposals. Using those networks, I extracted communities of interest and measured the amount of consensus happening between them. Finally, given that shares on DAOHaus are equivalent to power, I questioned to what extent participation in governance was tied to shares-holding.

Alright, let's first start by creating the voter's network (people who co-vote on a proposal) and its mirror, the proposal's network (proposals are connected if the same voter votes on both). To create both networks, we project the members <-> proposal bipartite graph both ways.

Analysis of the MetaCartel DAO. At the top is the voter's and proposal's network, both mirrored projections of the BiPartite network. In the
Analysis of the MetaCartel DAO. At the top is the voter's and proposal's network, both mirrored projections of the BiPartite network. In the

On the top-left is the co-voter network, where two members of the DAO are connected if the voted on the same proposal. The colors are automatically detected communities by the Louvain algorithm, which emerge from the topology (the shape) of the network, and we see emerge three communities. The size of the nodes is the number of shares (a proxy for power) in the network. On the top right, this is the proposal’s network where two proposals are connected to each other if a member votes on both of them. It is in essence the mirror network from the voter's one. The colors are again the "communities" detected by the Louvain algorithm. Interestingly, there exist two connected components (group of nodes connected to each other), which means that a set of proposals (in yellow) shared no voters with all the other proposals. Looking a the structure of organizations shows the emerging structures, here of groups of voters and proposals. This probably represents different interests, different activities, or subgroups within the organization.

When looking at each community, I observed that the distribution of shares was overall equal between each other, however, the balance of share allocation in each community was not really similar. Though they all shared a similar structure with few members holding most of the shares (as is visible in the Voter's network as well).

Finally, I decided to look at the consensus allocation between communities. To measure it, I took each proposal from a member of one of the three communities and looked at the Yes/No voting rate, a proxy for consensus. If the rate is close to 1, two communities are in complete agreement, if it's close to 0 they are in complete disagreement. Surprisingly, the diagonal, which corresponds to members of a community voting on their member's proposal, does not contain the highest values of the matrix. But, overall, the communities are aligned, which is not surprising given that they are all part of the same DAO. While this analysis is interesting in itself, to better understand those results, a qualitative follow-up to observe the proposal of each community and try to parse out the different activities of the DAO would be invaluable.

The “Skin in the Game” Effect is a Significant Driver of Participation

Another question that arises from analyzing this DAO is whether the number of shares held by a member increases its propensity to vote in the DAO. When we look below, there is a clear upward trend showing that having more shares increases your propensity to vote more. This is a concept that we could call “Skin in the game”, whereby if you hold a large amount of the DAO treasury, you get more invested in its actions. To get a bit more statistical about it, I divided the DAO members into two groups, by fitting a Gaussian model over the shares distributions, and setting the threshold at 2700 shares, which corresponds to the crossing point between both Gaussians. I then did a Receiver Operator Characteristic analysis, which displayed an AUC of 0.69, which corresponds to a moderate effect. We can conclude that, at least in MetaCartel, having more shares is a significant factor in your participation.

Analysis of the shares as a factor in participation. Left, log-log plot of the participation rate as a function of the number of shares. Middle, distribution of shares cutoff in two groups, high shareholder and low shareholders. A Gaussian Mixture Model was applied to the data to fit both distributions, and the threshold was set at the crossing point between both distributions. Right, ROC curve analysis of to observe the correlation between being a high shareholder and the propensity to vote more, the Area Under the Curve (AUC) is of 0.69.
Analysis of the shares as a factor in participation. Left, log-log plot of the participation rate as a function of the number of shares. Middle, distribution of shares cutoff in two groups, high shareholder and low shareholders. A Gaussian Mixture Model was applied to the data to fit both distributions, and the threshold was set at the crossing point between both distributions. Right, ROC curve analysis of to observe the correlation between being a high shareholder and the propensity to vote more, the Area Under the Curve (AUC) is of 0.69.

Conclusion

What can we tell of DAOs so far? It seems that DAOs have a fairly scale-free distribution of activity, where most members do not partake a lot, and a few do most of the voting. This is reminiscent of most self-organized groups, such as communities on GitHub, which display a Pareto distribution of work. Because we only looked at the governance here, and not actual work we cannot directly compare those properties, but if we consider governance to be work, then we see what we would expect from a normal organization. In that sense, DAOs do not seem to reinvent what humans already invented in the past. Moreover, we have seen that most DAOs have trouble retaining members, with a conversion rate that remains highly skewed towards less than 3 actions in the DAO. However, this does not seem to be due to a voter’s burnout problem. What will help DAOs retain more members remains to be tried and understood.

In terms of the space of DAOs, it seems that the large majority of DAOHaus and Snapshot DAOs are connected through their members. Moreover, this connectivity is scale-free, with a few DAO serving as Hubs, with a large number of members. This is probably underlined by a preferential attachment process whereas a DAO grows, it attracts more people.

Finally, when we analyze in detail the Metacartel DAO, we observed that it was composed of 3 main communities, and it allowed us to infer that a “skin in the game” process was at play, where people with the most shares would participate with a higher rate. Moreover, the emerging 3 communities have overall equally spread shares, and they have a fairly high rate of consensual voting between themselves. A property that is probably very healthy to avoid groupthink and internal debate.

To conclude, a lot more analysis remains to be done on the DAO space, this article barely scratches the surface of what is possible when so many digital traces are left by organizations. I am very eager to see what others will say from observing DAOs and whether DAOs will be true to their essence: Will they reinvent organizations?

Support the Labs

One of the core missions of Diamond DAO is to study and understand the DAO space as a whole. This happens through the creation of Chainverse and a curation process, and through the funding of more open research projects in the space of DAOs. To help support more research like this one, you can make a donation by acquiring this NFT. The artist is, well … myself (Shameless plug: I also do visual art). By purchasing this NFT 30% will go back to me to support further research in the space of DAOs, and 70% will go to a wallet that will be used to fund other researchers interested in studying this space. I hope you enjoy this nice looping animation!

Annex: Methodology

A quick aside to talk about the data and the methods used here. You can find all the code used in this analysis in the shared research repository of Diamond DAO.

Data Scraping

Because what is currently accepted as DAOs exists on transparent systems (the blockchain ecosystem), a very large amount of digital traces are left every-time people perform an action. Working with Diamond DAO, I and others have written data scrapers to collect the actions of different people, interacting with or through different organizations’ toolsets. Here, I will present the results that come from the scrapping of two such systems, DAOHaus, and Snapshot. Both are very different in nature, yet both promise to offer the required tools for any group to equip itself for governance. If you want to play with this data, and other datasets we collected, don't hesitate to contact @dmndDAO on Twitter.

Network Analysis

In order to study how individuals are organizing themselves, I here modeled objects and relationships through nodes and edges, in a network. Networks are a very useful abstraction when studying a complex system. They can assume different shapes, and those, in turn, are informative of the underlying structure. One concept that is important to understand here is network projection. If we have a network with two types of nodes, for example, Wikipedia pages, and Wikipedia members, and we want to know the relationship of "co-editing" a Wikipedia page, that is if two members have edited the same page. We will create a link, an edge, between them if we find that two editors are connected through the same page. The following results heavily rely on projecting networks, mostly of DAO members based on the different relationships they might have: co-voting, co-participating, co-discussing, etc. Just below is an example of such a graph ontology, where different nodes can be connected through relationships, and from that graph, we can then generate new projections to gain insights into the system we are studying, here DAOs.

Time Rescaling

To be able to compare some of the measurable properties of DAOs, I used some time re-scaling (also called time normalization, pseudo-time, etc). In essence, it is the redefinition of time as a state process, instead of the usual continuous variable defined in seconds. For example, if we take the advancement of a project from inception to conclusion, then we could decide to rescale time using the number of achievements. If we look at an embryo developing into an animal, we can define time as stages. Here, I have used some time re-scaling to be able to compare DAOs, given that they have not existed for the same amount of absolute time, it is necessary to define a normalized temporal metric to compare them between each other.

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