This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Bartosz Kusmierz, IOTA Foundation 10405 Berlin, Germany & Department of Theoretical Physics, Wroclaw University of Science and Technology, Poland [email protected];
(2) Roman Overko, IOTA Foundation 10405 Berlin, Germany [email protected].
Table of Links.
IV. SUMMARY
In this article, we analyzed the wealth distribution of the richest addresses in various cryptocurrencies. This included the time evolution of statistical metrics like the approximated Zipf’s law coefficient, Shannon entropy, Gini coefficient, and Nakamoto coefficient, along with their average values. It was shown that coins and ERC20 tokens have quantitatively different distributions of wealth. In particular, the values of approximated Zipf’s law coefficient for coins are 0.4—1.25 and 0.7—2.25 for ERC20 tokens. Differences between the two groups were also apparent during the study of wealth centralization. It was observed that tokens are, in general, much more centralized than coins with higher Gini coefficients and smaller Nakamoto coefficients.
This research might be of particular interest to DAOs and DPoS-based blockchains which rely on some form of a committee of the richest token holders. Presented values of statistical metrics like approximated Zipf coefficient or Nakamoto coefficient might help to model a committee selection process and make it more secure. Future work will evolve in two directions. Firstly, we are working on incorporating more metrics, analyzing more tokens, and considering more different sample size values. Secondly, the main findings of this work will be used to model a committee selection process.
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