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A multiplicative process for generating the rank-order distribution of UK election results

Author

Listed:
  • Trevor Fenner

    (Birkbeck, University of London)

  • Mark Levene

    (Birkbeck, University of London)

  • George Loizou

    (Birkbeck, University of London)

Abstract

Human dynamics and sociophysics suggest statistical models that may explain and provide us with a better understanding of social phenomena. Here we propose a generative multiplicative decrease model that gives rise to a rank-order distribution and allows us to analyse the results of the last three UK parliamentary elections. We provide empirical evidence that the additive Weibull distribution, which can be generated from our model, is a close fit to the electoral data, offering a novel interpretation of the recent election results.

Suggested Citation

  • Trevor Fenner & Mark Levene & George Loizou, 2018. "A multiplicative process for generating the rank-order distribution of UK election results," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(3), pages 1069-1079, May.
    • Handle: RePEc:spr:qualqt:v:52:y:2018:i:3:d:10.1007_s11135-017-0504-9
    DOI: 10.1007/s11135-017-0504-9
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    References listed on IDEAS

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    3. John Curtice & David Firth, 2008. "Exit polling in a cold climate: the BBC–ITV experience in Britain in 2005," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 509-539, June.
    4. Serge Galam, 2008. "Sociophysics: A Review Of Galam Models," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 409-440.
    5. Damián H. Zanette, 2008. "Multiplicative Processes and City Sizes," Springer Books, in: Sergio Albeverio & Denise Andrey & Paolo Giordano & Alberto Vancheri (ed.), The Dynamics of Complex Urban Systems, pages 457-472, Springer.
    6. Artur J. Lemonte & Gauss M. Cordeiro & Edwin M. M. Ortega, 2014. "On the Additive Weibull Distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(10-12), pages 2066-2080, May.
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    Cited by:

    1. Mark Levene & Aleksejus Kononovicius, 2018. "Empirical Survival Jensen-Shannon Divergence as a Goodness-of-Fit Measure for Maximum Likelihood Estimation and Curve Fitting," Papers 1809.11052, arXiv.org, revised Jun 2019.

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