Getting more insights from the Ethereum Carbonvote

Max Semenchuk
Jul 10, 2019 · 5 min read

This is a part of the research on the improvement of the Ethereum governance, specifically by clustering of addresses, which participated in coinvotes and EIP-186 carbonvote specifically. Results are intended to provide some additional insights on different stakeholder stances (holders, users, app developers) for that and future governance proposals. Made with Malkevych Bohdan

Intro

Ethereum governance is a complex multi-stakeholders system governing the changes to the protocol. It’s a mixture of formal proposals management (EIPs) process and less formal signalling & discussion made through calls, forums, tiwtter, reddit, events and more. Actors include foundations (like Ethereum Foundation), developers, miners, users and probably some other groups.

Tennagraph was created to improve the governance by optimizing the work around signalling stakeholder stances, making collection easier and results more insightful. The first version including coin and gas voting, as well as twitter influencer stances, was launched earlier this year. We’ve tested it with a contentious EIP-1057. Now we’re researching the further development of the tech and have identified a number of challenges around the process.

Challenges

The current process shown in the picture has several actors and steps. The problems are formed in the How Might We (HMW)-statements. There’s a lot of interest around increasing the participation in governance, but for our current research, we’ve selected the data analysis, interpretation and presentation opportunities.

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Also, we’ve recognized that a vast data set can be found in the carbon vote used by the network for the EIP-186 (around 3% of all Ethereum participated in that). As we can play with this data, our finding and tools can be reused for any further coinvotes at any time, including tennagraph and carbonvote platforms. Specifically, our challenges (or “how might we”) here are:

Solution hypothesis

So we know some patterns in the type recognition

So we wanted to show the preference graph on the axis and also outline the votes by identified profiles.

Methodology

Disclaimer: This is the first test, probably includes inaccuracies and errors. Also there is a small amount of data for the analysis so the bias can be big. To be improved after receiving more feedback.

For each address that took part in the voting were calculated the consumed gas and balance. So, here’re visualizations on the voting:

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Visualize the participants depending on current balances and consumed gas

Separated plots to find insights for each option separately:

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Cumulative ETH

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Cumulative GAS spent

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To find some insights by clusterization, all participants were split using a median on 3 equal groups.

User type 1 — Users which consume a small amount of gas and have a small amount of eth on their wallets.

User type 2 — The middle by the median.

User type 3 — Users which consume a lot of gas or users who have a big amount of ETH on their wallets.

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Clusterization users by ETH on wallets and consuming gas

Picture 5 demonstrates the frequency of voting by each group of users.

Current findings on EIP-186 include:

We could automate such calculations for other research in the Tennagraph app.

The code and data is published here: https://github.com/TennaGraph/DataAnalysis/blob/master/Test%20Data%20analysis.ipynb

We’ll take them for further research in the future.

Please HEPL

We want to validate a couple of hypotheses around this concept. If you’d like to help — let us know (in a comment or direct messages)

Thanks in advance. It’s an early and small test, we believe there’s a wide room for the improvement. So any criticism, suggestions and guidance is very helpful.

Read More

4IRE labs

Blog on FinTech, blockchain for business and open markets

Max Semenchuk

Written by

Entrepreneur, Product Manager, UX. Research & Play with #Decentralization, #Holacracy, #Lean, #DAO. http://maxsemenchuk.com

4IRE labs

4IRE labs

Blog on FinTech, blockchain for business and open markets

Max Semenchuk

Written by

Entrepreneur, Product Manager, UX. Research & Play with #Decentralization, #Holacracy, #Lean, #DAO. http://maxsemenchuk.com

4IRE labs

4IRE labs

Blog on FinTech, blockchain for business and open markets

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