Quantifying Decentralization
Balaji S. Srinivasan
44118

Regarding Bitcoin and Ethereum dev and client decentralization, I think those four graphs miss two points:

  • Many of the so-called “alternative Bitcoin clients” are in fact forks of the same codebase as Bitcoin Core, whereas all Ethereum implementations have fully separate codebases created from scratch. So it’s not clear that Core and BU should count as two fully separate clients.
  • Ethereum doesn’t really have the concept of a “reference client”. If you take the literal meaning of “client that people refer to to improve their understanding of protocol rules”, then in many cases that’s actually pyethereum because python is easier to read. The C++ client is the client that generates the test suites. So counting commits to Geth imo understates the decentralization of the ecosystem.

This is only to illustrate how subtle the notion of a “subsystem” is. During last year’s DoS attacks, there were days during which Geth was unusable, and most people just switched over to Parity. So there are subsystems that are not quite critical but also not quite negligible.

I also think you missed another reason why Gini indices are a bad idea: whereas in the real world, Gini indices are typically used to measure wealth inequality between full-time residents of a country, and so actually measure inequality of outcome, in the crypto space inequality between accounts can come from two sources: (i) disparities between different users’ ability to prosper in the system at a given level of participation, and (ii) disparities in the extent to which they are participating. The Gini index of cello production across the world population is likely above 0.99, but for obvious reasons nobody cares. In the case of mining and wealth, the problem is that there is such a long tail of very slightly interested amateur contributors that the Gini index likely ends up measuring artefacts of the cutoff of where one particular source of data started counting users more than anything else. So looking at the Nakamoto coefficient, or similar measurements like the share of the top100, is definitely superior.

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