DGF comparisons

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DAOstack

(by RonenT (talk) 18:19, 14 June 2023 (CDT))

DAOstack is an early decentralized governance framework (roughly 2017-2019) whose development was led by Matan Field. I worked with Matan at DAOstack in 2022 (this was for a different reincarnation of the company with a different focus than the original one described here).

DAOstack was an ambitious and large-scale effort ( $30MM USD ICO in 2018), and yet ultimately failed.

My intuition was that DAOstack and DGF share many commonalities, so I wanted to dig deeper on DAOstack and see what we can learn from those earlier efforts.

I’ve found an excellent “post-mortem” of DAOstack described below:

The Dissensus Protocol (a paper reviewing DAOstack as a case study)

The Dissensus Protocol: Governing Differences in Online Peer Communities

The authors had actually worked for (and were paid by) DAOstack, but wrote this a few years later.

Below are some thoughts and excerpts following a first reading. One reservation is that I’m still getting up to speed on DGF so my knowledge on it is still shallower in some respects.

I would also strongly suggest for you to read the paper, or at least the part describing the DAOstack case study.

Dissensus

The paper first introduces the idea of dissensus:

“Dissensus describes the emergence of incompatible differences. Among peer-to-peer technologies, blockchain stands out as a set of ideas that explicitly seek to resolve dissensus through consensus protocols. In this article, we propose dissensus as a “protocol” for foregrounding the often sidelined yet productive aspects of incompatible differences. The concept highlights that there might not always be consensus about a consensus algorithm, and that indeed, dissensus is the precondition for new possibilities and perspectives to emerge”

They highlight a trend in blockchain projects where

“Prevalent among communities forming around blockchain technology more specifically is an emphasis on relegating governance to automated enforcement by a protocol, coordinating the otherwise free actions of actors in the network.”

But observe the tension inherent in trying to resolve dissensus through consensus mechanisms:

“Automating certain governance processes can be a welcome relief, minimizing the need for repeated actions. But when it is understood as neutral mechanisms for resolving dissensus and solving governance once and for all, it causes significant problems in theory and practice: in theory, it causes an issue of infinite regression, whereby dissensus about a consensus algorithm, for instance, then can only be solved through another neutral consensus algorithm”

There is a tendency to try and “solve governance” through technical means, but there are limits to that apporach:

“First, because governance entails more than mere coordination and resolution to conflict between predetermined individual behaviors and preferences—it also involves deliberation, negotiation, and transformation, and the formation of norms, cultures, and understandings that are often better navigated “off-chain,” so to speak. And second, because governance processes and technologies themselves might become the site of conflict. Dissensus as a concept therefore serves to place governance tools back into their social and historical contexts, as particular, not universal forms of negotiating difference.”

Design Governance

They describe what they call a Design Governance approach to blockchain protocols, which provides the context in which DAOstack (and DGF?) were developed, as a reaction to failures of Bitcoin and Ethereum on the governance front:

“A “second wave” of DAOs continued to work on governance, now as a problem of incentive design. Incentives would be used as a design option for creating bespoke forms of social and economic organization. Drawing on game theory, incentives would be arranged, assuming people would respond in predictable ways, making individual behaviors align with an overall design objective”

The note that this approach

repeats many of the oft-criticized assumptions [of market economics], not least that people are (or have to perform as) isolated economically rational actors who react to incentives in a predictable manner”

And

“that imposed rules can to a large degree be replaced with incentive designs that will produce predictable behavioral outcomes.”

DAOstack case study

Similarly to DGF:

“DAOstack’s governance mechanism, described in detail in their whitepaper (DAOstack, 2018), is called “Holographic Consensus” and combines voting, staking, and reputation. The ideal DAO is defined as “an entity that not only lives on the Internet and exists autonomously but also heavily relies on hiring individuals to perform certain tasks that the automaton itself cannot do” (Buterin, 2014). Humans are on the outside, with coordination taking place automatically from the inside by an algorithm. The essential concept is to program the required rules and decision-making of an organization into code on a blockchain, with the idea that it will minimize the need for governing roles”

This seems important too:

“The Genesis DAO is a good example of a unique trait common to many DAOs, namely, that they comprise highly motivated groups that have formed around a set of ideas about governance, rather than governance being a means in order to achieve some shared mission. In other words, it was tool-centric and focused on one main action: allocating funds to proposals. It is unusual for people who are strangers to start making financial decisions together immediately without having time to develop coherence and trust. And this was in fact the very promise of projects like the Genesis DAO: that the technology would bypass the need to develop trusted relationships, meaning thousands of people would be able to coalesce around objectives, take actions, and even spend money together as a group.”

This part seems related to this question on Slack

“The fact that decisions are executed automatically, no matter whether conversation takes place or not, generally had the effect of discouraging it. Instead, it was all too easy to disengage or disappear when dissensus arose. Low proportional voter participation led to much speculation about what a “no vote” meant. Were those not voting abstaining because they had no opinion or no time? Had they registered, left, and never come back? Were they silently disagreeing, or already left because of a diverging opinion? Apart from engaging with the group on other communication channels, there was no way of knowing whether silence meant silent agreement or silent dissent. This dynamic led to a stagnant feeling in the group, hindering it from collectively learning and working through issues as they arose.”

They then detail how governance moved off chain and isn’t actually available on the record at all as a result.

Summary of the paper's conclusions

Their conclusion re DAOstack:

the Genesis DAO is significant in that it attempted to realise many of the ideas of what might become possible with DAOs. But the experiment led to disengagement and important information about disagreements being lost rather than feeding into discussions that might lead to organisational development and growth.”

Some relevant takeaways for me more generally:

“Consensus algorithms, token voting, staking, and forking are highly eccentric and particular ways of going about governing a group or an organization. In short, what is important is not only that dissensus is resolved but also how it is resolved. The concept of dissensus allows for governance to be considered less an abstract universal problem to be solved by technical means, and rather a contextually situated means to achieve a particular set of shared aims.”

governance entails more than mere coordination and resolution to conflict between predetermined individual behaviors and preferences—it also involves deliberation, negotiation, and transformation, and the formation of norms, cultures, and understandings that are often better navigated “off-chain,” so to speak