This paper is under CC 4.0 license. available on arxiv Authors: (1) Bartosz Kusmierz, IOTA Foundation 10405 Berlin, Germany & Department of Theoretical Physics, Wroclaw University of Science and Technology, Poland bartosz.kusmierz@pwr.edu.pl; (2) Roman Overko, IOTA Foundation 10405 Berlin, Germany roman.overko@iota.org. Table of Links. Abstract and Introduction Related Work and Methodology Results Summary and References 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. REFERENCES [1] M. Castro and B. Liskov. Practical Byzantine Fault Tolerance. 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Simple Verifiable Delay Functions. 2018. https://eprint. iacr.org/2018/627. [15] S. Somin, G. Gordon, and Y. Altshuler. Network Analysis of ERC20 Tokens Trading on Ethereum Blockchain. In A. J. Morales, C. Gershenson, D. Braha, A. A. Minai, and Y. Bar-Yam, editors, Unifying Themes in Complex Systems IX, pages 439–450, Cham, 2018. Springer International Publishing. [16] S. Somin, G. Gordon, A. Pentland, E. Shmueli, and Y. Altshuler. ERC20 Transactions over Ethereum Blockchain: Network Analysis and Predictions, 2020. [17] C. Stathakopoulou, T. David, and M. Vukolic. Mir-BFT: HighThroughput BFT for Blockchains. 06 2019. [18] E. Syta, P. Jovanovic, E. K. Kogias, N. Gailly, L. Gasser, I. Khoffi, M. J. Fischer, and B. Ford. Scalable Bias-Resistant Distributed Randomness. In 2017 IEEE Symposium on Security and Privacy (SP), pages 444–460, 2017. [19] F. Victor and B. K. Luders. Measuring Ethereum-Based ERC20 Token ¨ Networks. In I. Goldberg and T. 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