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Analyzing a networked social algorithm for collective selection of representative committees


Autoři: Alexis R. Hernández aff001;  Carlos Gracia-Lázaro aff002;  Edgardo Brigatti aff001;  Yamir Moreno aff002
Působiště autorů: Instituto de Física, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil aff001;  Institute for Biocomputation and Physics of Complex Systems (BIFI), Universidad de Zaragoza, Zaragoza, Spain aff002;  Department of Theoretical Physics, Faculty of Sciences, Universidad de Zaragoza, Zaragoza, Spain aff003;  ISI Foundation, Turin, Italy aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222945

Souhrn

A recent work (Hernández, et al., 2018) introduced a networked voting rule supported by a trust-based social network, where indications of possible representatives were based on individuals opinions. Individual contributions went beyond a simple vote-counting and were based on proxy voting. This mechanism selects committees with high levels of representativeness, weakening the possibility of patronage relations. By incorporating the integrity of individuals and its perception, we here address the question of the resulting committee’s trustability. Our results show that this voting rule provides sufficiently small committees with high levels of representativeness and integrity. Furthermore, the voting system displays robustness to strategic and untruthful application of the voting algorithm.

Klíčová slova:

Algorithms – Directed graphs – Interpersonal relationships – Network analysis – Social networks – Sociology of knowledge – Democracy – Tobacco mosaic virus


Zdroje

1. Hernández AR, Gracia-Lázaro C, Brigatti E, Moreno Y. 2018 A networked voting rule for democratic representation. R. Soc. open sci. 5: 172265. http://dx.doi.org/10.1098/rsos.172265 29657817

2. Coleman Stephen, and Blumler Jay G. (2009) The Internet and Democratic Citizenship: Theory, Practice and Policy. New York, NY: Cambridge University Press.

3. Chadwick Andrew. (2006) Internet Politics: States, Citizens, and New Communication Technologies. New York and Oxford: Oxford University Press.

4. Hindman Matthew Scott. (2009) The Myth of Digital Democracy. Princeton, NJ: Princeton University Press.

5. Condorcet MJ. 1785 Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix. Paris, France: de l’Imprimerie royale. https://doi.org/10.3931/e-rara-3791

6. Katz R. S. Democracy and Elections. Oxford University Press, 1997.

7. Kelly J. 1991 Social Choice Bibliography. Soc Choice Welfare. 8, 97–169. http://www.jstor.org/stable/41105976

8. Noveck Beth Simone. (2009) Wiki Government: How Technology Can Make Government Better, Democracy Stronger, and Citizens More Powerful. Washington, DC: Brookings Institution Press.

9. Shane Peter M. (ed.). (2004) Democracy Online: The Prospects for Political Renewal Through the Internet. New York, NY: Routledge.

10. M. A. Rodriguez et al., “Smartocracy: Social Networks for Collective Decision Making,” 2007 40th Annual Hawaii International Conference on System Sciences (HICSS’07), Waikoloa, HI, 2007, pp. 90–90.

11. Yamakawa H., Yoshida M., and Tsuchiya M. Toward delegated democracy: Vote by yourself, or trust your network. International Journal of Human and Social Sciences, 1, 2007.

12. Paolo Boldi, Francesco Bonchi, Carlos Castillo, and Sebastiano Vigna. (2009) Voting in social networks. In Proceedings of the 18th ACM conference on Information and knowledge management (CIKM’09). ACM, New York, NY, USA, 777–786; Paolo Boldi, Francesco Bonchi, Carlos Castillo, Sebastiano Vigna Viscous Democracy For Social Networks, Communications of the ACM, 2011, 54,129–137

13. Erdös P, Rényi A. 1960 On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5:17–60.

14. Barabási AL, Albert R. 1999 Emergence of Scaling in Random Networks. Science, 286, 509–512. doi: 10.1126/science.286.5439.509 10521342

15. Watts DJ, Strogatz SH. 1998 Collective dynamics of “small–world” networks. Nature, 393, 440–442. doi: 10.1038/30918 9623998

16. Holland P. W., Laskey K. B., Leinhardt S. (1983). Stochastic blockmodels: First steps. Social networks, 5(2), 109–137. doi: 10.1016/0378-8733(83)90021-7

17. Jure Leskovec and Andrej Krevl. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data. 2014.

18. B. Rozemberczki, R. Davies, R. Sarkar and C. Sutton. GEMSEC: Graph Embedding with Self Clustering. arXiv:1802.03997. 2018.

19. J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on Ground-truth. ICDM, arXiv:1205.6233. 2012.


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