Increasingly, Computer Aided Governance is a methodology used in DAOs and decentralized projects to improve the quality of decisions made by incorporating evidence from data, analysis, and various types of modeling including integrative “complex systems” simulation modeling.

In general, the types of evidence used during the course of DAO governance decision making, from most to least “value added” are: Reports / Analyses; Integrative Simulation Models; Supervised AI / ML / Statistical Models; Clustering and Unsupervised Learning; Data Visualization; Raw Data.

The figure below describes a prototypical Evidence-Based decision making process that incorporates Computer Aided Governance.

The [REDACTED] DAO will have a forum with subsections dedicated to a variety of topics including governance-related discussions. Specifically, a subsection where data science and modeling contributors are dedicated to sourcing, sharing, and discussing evidence in support of decision making.

At the outset, imagine a proposal for a decision is before the DAO - such as deciding what collateral to accept to the bonding during the next epoch. To support this decision, data is collected and analyzed with the help of simulation and other models, and the analysis is discussed by decision makers. As a consequence of the discussion, it may be apparent that more evidence will be needed in order to arrive at an informed decision, in which case more data is collected, analyzed and discussed.

If it is apparent from the discussion that there is enough evidence to form a rough consensus around the decision, then the process moves to a binding vote. Once the decision is actuated, the state of the DAO, the controlled system, and the environment changes, which generates new information that can lead to additional questions and decisions in the future.

Here, we leave out routine data science details such as sourcing, cleaning, and transforming raw data into something more convenient for creating models and analysis. Additional extensions that clearly can occur, but are not discussed explicitly here are that multiple decisions (and their processes) can be happening at once, for example, if there are multiple matters for the DAO to decide at the same time.

In the future we anticipate that this loop will become increasingly automated for some types of operational decisions such as real-time parameter adjustments. This process would see removed the human mediated stages, and replacing voting with AI or rules based decision making, becoming a more typical control loop.

Imagine that there’s a proposal before the DAO governance to whether to include a new asset type. Then, the sub-section dedicated to this decision would have a number of posts including high-level reports built on earlier, foundational analyses. Take for example the "Token Economics Report" which combines and discusses evidence including models and analyses previously posted to the forum.

Within the DAO governance community, normative behaviors are assumed to arise and be reinforced, such as voters informing their vote with the best evidence, and also favouring evidence that clearly references source material (analysis, models, or raw data).

The Figure 3 shows that the "Machine Learned Model", "Data Visualization", and "Market Simulation", use previously posted evidence: "Raw Market Data", "Blockchain Data", and "Behavioral Data".

Excuse us as we now have to post the mathematical section of this article as pictures since Mirror does not support special characters yet. A written format of the equations can be accessed through our Discord.

Interestingly, we have demonstrated that this credit assignment is possible without each voting member having to explicitly judge the utility of evidence against the predecessor or source evidence used.

Utilizing Computer-Aided Governance in our decision-making, we can allow for the governance architecture to become not only increasingly automated, but more data-driven. In protocols such as ours, with many moving levers, it is critical that we incorporate the attribution in evidence-based governance of a DAO. More details on this particular topic regarding our DAOs differentiators will be expanded upon in future “Protocol Mechanic” articles as well as our Gitbook, which is coming out ███████.

Join the cartel on Discord

Increasingly, Computer Aided Governance is a methodology used in DAOs and decentralized projects to improve the quality of decisions made by incorporating evidence from data, analysis, and various types of modeling including integrative “complex systems” simulation modeling.

In general, the types of evidence used during the course of DAO governance decision making, from most to least “value added” are: Reports / Analyses; Integrative Simulation Models; Supervised AI / ML / Statistical Models; Clustering and Unsupervised Learning; Data Visualization; Raw Data.

The figure below describes a prototypical Evidence-Based decision making process that incorporates Computer Aided Governance.

The [REDACTED] DAO will have a forum with subsections dedicated to a variety of topics including governance-related discussions. Specifically, a subsection where data science and modeling contributors are dedicated to sourcing, sharing, and discussing evidence in support of decision making.

At the outset, imagine a proposal for a decision is before the DAO - such as deciding what collateral to accept to the bonding during the next epoch. To support this decision, data is collected and analyzed with the help of simulation and other models, and the analysis is discussed by decision makers. As a consequence of the discussion, it may be apparent that more evidence will be needed in order to arrive at an informed decision, in which case more data is collected, analyzed and discussed.

If it is apparent from the discussion that there is enough evidence to form a rough consensus around the decision, then the process moves to a binding vote. Once the decision is actuated, the state of the DAO, the controlled system, and the environment changes, which generates new information that can lead to additional questions and decisions in the future.

Here, we leave out routine data science details such as sourcing, cleaning, and transforming raw data into something more convenient for creating models and analysis. Additional extensions that clearly can occur, but are not discussed explicitly here are that multiple decisions (and their processes) can be happening at once, for example, if there are multiple matters for the DAO to decide at the same time.

In the future we anticipate that this loop will become increasingly automated for some types of operational decisions such as real-time parameter adjustments. This process would see removed the human mediated stages, and replacing voting with AI or rules based decision making, becoming a more typical control loop.

Imagine that there’s a proposal before the DAO governance to whether to include a new asset type. Then, the sub-section dedicated to this decision would have a number of posts including high-level reports built on earlier, foundational analyses. Take for example the "Token Economics Report" which combines and discusses evidence including models and analyses previously posted to the forum.

Within the DAO governance community, normative behaviors are assumed to arise and be reinforced, such as voters informing their vote with the best evidence, and also favouring evidence that clearly references source material (analysis, models, or raw data).

The Figure 3 shows that the "Machine Learned Model", "Data Visualization", and "Market Simulation", use previously posted evidence: "Raw Market Data", "Blockchain Data", and "Behavioral Data".

Excuse us as we now have to post the mathematical section of this article as pictures since Mirror does not support special characters yet. A written format of the equations can be accessed through our Discord.

Interestingly, we have demonstrated that this credit assignment is possible without each voting member having to explicitly judge the utility of evidence against the predecessor or source evidence used.

Utilizing Computer-Aided Governance in our decision-making, we can allow for the governance architecture to become not only increasingly automated, but more data-driven. In protocols such as ours, with many moving levers, it is critical that we incorporate the attribution in evidence-based governance of a DAO. More details on this particular topic regarding our DAOs differentiators will be expanded upon in future “Protocol Mechanic” articles as well as our Gitbook, which is coming out ███████.

Join the cartel on Discord