Abstract
Voting algorithms have been widely used as consensus protocols in the realization of fault-tolerant systems. These algorithms are best suited for distributed systems of nodes with low computational power or heterogeneous networks, where different nodes may have different levels of reputation or weight. Our main contribution is the construction of a fair voting protocol in the sense that the influence of the eventual outcome of a given participant is linear in its weight. Specifically, the fairness property guarantees that any node can actively participate in the consensus finding even with low resources or weight. We investigate effects that may arise from weighted voting, such as centralization, loss of anonymity, scalability, and discuss their relevance to protocol design and implementation.
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References
Neil, B., Shields, L.C., Margolin, N.B.: A survey of solutions to the sybil attack (2005)
Mossel, E., Neeman, J., Tamuz, O.: Majority dynamics and aggregation of information in social networks. Auton. Agent. Multi-Agent Syst. 28, 408–429 (2014)
Gács, P., Kurdyumov, G.L., Levin, L.A.: One-dimensional Uniform Arrays that Wash out Finite Islands. In: Problemy Peredachi Informatsii (1978)
Moreira, A.A., Mathur, A., Diermeier, D., Amaral, L.: Efficient system-wide coordination in noisy environments. Proc. Natl. Acad. Sci. U.S.A. 101, 12085–12090 (2004)
Kar, S., Moura, J.M.F.: Distributed average consensus in sensor networks with random link failures. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP 2007, vol. 2, pp. II–1013–II–1016, April 2007
Cruise, J., Ganesh, A.: Probabilistic consensus via polling and majority rules. Queueing Syst. 78(2), 99–120 (2014)
Gogolev, A., Marchenko, N., Marcenaro, L., Bettstetter, C.: Distributed binary consensus in networks with disturbances. ACM Trans. Auton. Adapt. Syst. 10, 19:1–19:17 (2015)
Popov, S., Buchanan, W.J.: FPC-BI: fast probabilistic consensus within byzantine infrastructures. J. Parallel Distrib. Comput. 147, 77–86 (2021)
Capossele, A., Mueller, S., Penzkofer, A.: Robustness and efficiency of leaderless probabilistic consensus protocols within byzantine infrastructures (2019)
Banisch, S., Araújo, T., Louçã, J.: Opinion dynamics and communication networks. Adv. Complex Syst. 95–111 (2010)
Niu, H.-L., Wang, J.: Entropy and recurrence measures of a financial dynamic system by an interacting voter system. Entropy 2590–2605 (2015)
Przybyła, P., Sznajd-Weron, K., Tabiszewski, M.: Exit probability in a one-dimensional nonlinear \(q\)-voter model. Phys. Rev. E (2011)
Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Modern Phys. 591 (2009)
Rabin, M.: Incorporating fairness into game theory. Department of Economics, UC Berkeley (1991)
Popov, S., et al.: The coordicide (2020)
Vigneri, L., Welz, W., Gal, A., Dimitrov, V.: Achieving fairness in the tangle through an adaptive rate control algorithm. In: 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp. 146–148, May 2019
Vigneri, L., Welz, W.: On the fairness of distributed ledger technologies for the internet of things. In: 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2020)
Gambarelli, G.: Power indices for political and financial decision making: a review. Ann. Oper. Res. 51(4), 163–173 (1994)
Arpan Mukhopadhyay, R.R., Mazumdar, R.R.: Voter and Majority Dynamics with Biased and Stubborn Agents https://arxiv.org/abs/2003.02885 (2020)
Chen, X., Papadimitriou, C., Roughgarden, T.: An axiomatic approach to block rewards. In: Proceedings of the 1st ACM Conference on Advances in Financial Technologies, (New York, NY, USA), pp. 124–131. Association for Computing Machinery (2019)
Popov, S.: A probabilistic analysis of the Nxt forging algorithm. Ledger 1, 69–83 (2016)
Leonardos, S., Reijsbergen, D., Piliouras, G.: Weighted voting on the blockchain: improving consensus in proof of stake protocols (2020)
Leshno, J., Strack, P.: Bitcoin: an impossibility theorem for proof-of-work based protocols. Cowles Foundation Discussion Paper, no. 2204R (2019)
Kondor, D., Pósfai, M., Csabai, I., Vattay, G.: Do the rich get richer? An empirical analysis of the bitcoin transaction network. PloS One 9, e86197 (2014)
Jones, C.I.: Pareto and Piketty: the macroeconomics of top income and wealth inequality. J. Econ. Perspect. 29, 29–46 (2015)
Tao, T.: Benford’s law, Zipf’s law, and the Pareto distribution. https://terrytao.wordpress.com/2009/07/03/benfords-law-zipfs-law-and-the-pareto-distribution/ (2009)
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Müller, S., Penzkofer, A., Camargo, D., Saa, O. (2021). On Fairness in Voting Consensus Protocols. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_65
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DOI: https://doi.org/10.1007/978-3-030-80126-7_65
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