I lead the team that created the list.
I think it’s going to be useful if I shed some light here.
How the algorithm works
We’ve developed an algorithm that works similar to the original Pagerank. Instead of ranking websites – we rank identities, instead of tracking links – we track attention.
It’s a 2nd order metric, therefore, the number of followers doesn’t matter much. It matters who these followers are.
It’s not just about followers. We’re tracking attention, which is scarce.
Following somebody is an indicator of paying attention. One of many.
At the moment we’re tracking Twitter, but soon we will combine it with many other sources of data.
https://cryptoinfluencers.io/algorithm - a brief description of our algorithm; this page will be gradually expanded with more details
http://maciek.blog/what-is-influence - my blog post outlying how we think of influence
To address the cons in @MaxSemenchuk post:
Majority of them may not be interested to vote
This problem applies to any voting system. One could potentially address this by delegating one’s votes and through other approaches.
May be prone to bribes
One cannot guarantee that no bribery will take place. However, we believe that this approach is less susceptible to this risk than alternatives.
The influence scores distribution is subject to Power Laws. The people who have the most voting power, e.g. Vitalik Buterin, Vlad Zamfir, Joseph Lubin or Gavin Wood are unlikely to be corrupted.
May be prone to fake subscribers approach
We believe that this system can be Sybil-resistant. It’s based on quantifying attention. Attention is the source of scarcity.
To address @decanus concerns:
How do you propose to measure technical ability or contributions to the ecosystem otherwise?
My belief is that systems tracking ability should be fundamentally divided into:
a) Accountability metrics – they work for use cases that can be accurately quantified, e.g. an investors performance can be quantified accurately through returns.
b) Reputation metrics – they are necessary when things escape direct quantification, therefore can’t be measured with accountability metrics.
For example, should surgeons be measured by the number of patients that die/survive on their table? If this was the case, they’d have an incentive to only treat “easy” patients and avoid serious cases. Whereas if one quantified which experienced surgeons other surgeons want to observe and learn from – that’s probably a good indication of their expertise.
I’d love to hear your thoughts on how technical ability/contributions can be measured more accurately.
To address mattdf concerns:
Peter Todd is not on the Ethereum list. You can check the list on our website (tab Ethereum).
It sounds to me like what @MaxSemenchuk is proposing is an experiment. I see no reason why it couldn’t be running along with other experiments, e.g. based on the governance system that @decanus is building.
In the end, one can evaluate how these multiple experiments performed and it will help inform future decisions regarding governance.