Korelasi Bebas-skala dalam Studi Geo-politik Pemilihan

Maulana, Ardian and Situngkir, Hokky (2015): Korelasi Bebas-skala dalam Studi Geo-politik Pemilihan. Published in: BFI Working Paper Series, WP-3-2015 (September 2015)

Abstract

Collective behavior is a phenomena emerged from interacting elements of a complex system. This paper is a preliminary study for the spatial analysis to investigate the spatial characteristics from the correlations within election data. We demonstrate the emerged scale-free spatial correlation by the power law exhibited with the correlation length scaled over the size of the system. The analysis confirmed the collective behavior in voting and election processes in which local elements related to the global view of the system. Furthermore, the implementation of the community derectiom algorithm as the result of the election modeled as weighted correlation network demonstrated some correlated geographical clusters and the patterns they represented. Interestingly, the analysis has opened extended explanation on the geo-political characteristics within a nation, as exemplified by observing the election in Germany in 2013. Further analyses investigating the robustness of this aspects are opened.

• Item Type: MPRA Paper
• English Title: Scale-free correlation within Geopolitics of Election Studies
• Language: Indonesian
• Keywords: election, spatial correlation, scale-free, power law, correlation length, weighted correlation network, community detection.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R0 - General; Z - Other Special Topics > Z1 - Cultural Economics ; Economic Sociology ; Economic Anthropology

References

1. Fortunato, S., Castellano, C. (2012).Physics peeks into the ballot box. Physics Today 65, 74–75 2. Chatterjee, A., Mitrovic, M. & Fortunato, S. (2013). Universality in voting behavior: an empirical analysis. Scientific Reports. Nature Publishing Group. 3. Araripe, L. E., Costa Filho, R. N., Herrmann, H. J. & Andrade, J. S. (2006). Plurality Voting: the Statistical Laws of Democracy in Brazil.Int. J. Mod. Phys. C 17, 1809–1813 4. Maulana,A. & Situngkir,H. (2012). Power Law in Election.Journal of Social Complexity.Bandung Fe Institute. 5. Situngkir,H & Surya,Y. (2004).Democracy : Order out of Chaos, Understanding Power Law in Indonesian Election. WPQ2004. Bandung Fe Institute. 6. Palombi, F.,Toti.,S.(2015). Voting Behavior in Proportional Elections from Agent - Based Models. Physics Procedia, Volume 62, p. 42-47. 7. Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F., & Viale, M. (2010). Scale-free correlations in starling flocks. Proceedings of the National Academy of Sciences of the United States of America, 107(26), 11865–11870. 8. Sarzynska, M.,Leicht, E., Chowell, G., Porter, M.A. (2015). Null Models for Community Detection in Spatially-Embedded, Temporal Networks. arXiv:1407.6297. 9. Gallos, L. K., Sigman, M., & Makse, H. A. (2012). The Conundrum of Functional Brain Networks: Small-World Efficiency or Fractal Modularity. Frontiers in Physiology, 3, 123. 10. Borghesi, C., Raynal, J.-C., & Bouchaud, J.-P. (2012). Election Turnout Statistics in Many Countries: Similarities, Differences, and a Diffusive Field Model for Decision-Making. PLoS ONE, 7(5), e36289. 11. Gallos,L.K., Barttfeld,P., Havlin,S., Sigman,M., Makse,H,A.,(2012). Collective behavior in the spatial spreading of obesity. Scientific Reports, 2012; 2 12. Clauset, A., Shalizi, C. R. & Newman, M. E. J.(2009). Power-law distributions in empirical data. SIAM Review 51, 661–70. 13. Schneidman, E., Berry, M. J., Segev, R. & Bialek, W.(2006). Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440 ,1007–1012 14. Maulana,A. (2008).Dinamika sentimen pemilih dalam Simulasi Voting berbasis Agen. WP-IX-2008.Bandung Fe Institute 15. Blondel, V. D.,Guillaume, J.-L., Lambiotte,R., Lefebvre,E.(2008). Fast unfolding of communities in large networks. J. Statist. Mech., 10:P10008. 16. S. Fortunato.(2010). Community detection in graphs.Physics Reports, 486:75.174 17. Fortunato,S., Barthellemy.(2006). Resolution limit in community detection.Proc. Natl. Acad.Sci. USA, 104:36.41 18. Lancichinetti.,A & Fortunato,S.(2012). Consensus clustering in complex networks.Scientific Reports, 2(336). 19. Maulana,A., Situngkir,H.(2015).OBSERVASI KOMPLEKSITAS PEMILU: Studi Kasus pemilihan umum Indonesia 2014. Working Paper Bandung Fe Institute