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Parliamentary roll-call voting as a complex dynamical system: The case of Chile

Author

Listed:
  • Diego Morales-Bader
  • Ramón D Castillo
  • Ralf F A Cox
  • Carlos Ascencio-Garrido

Abstract

A method is proposed to study the temporal variability of legislative roll-call votes in a parliament from the perspective of complex dynamical systems. We studied the Chilean Chamber of Deputies’ by analyzing the agreement ratio and the voting outcome of each vote over the last 19 years with a Recurrence Quantification Analysis and an entropy analysis (Sample Entropy). Two significant changes in the temporal variability were found: one in 2014, where the voting outcome became more recurrent and with less entropy, and another in 2018, where the agreement ratio became less recurrent and with higher entropy. These changes may be directly related to major changes in the Chilean electoral system and the composition of the Chamber of Deputies, given that these changes occurred just after the first parliamentary elections with non-compulsory voting (2013 elections) and the first elections with a proportional system in conjunction with an increase in the number of deputies (2017 elections) were held.

Suggested Citation

  • Diego Morales-Bader & Ramón D Castillo & Ralf F A Cox & Carlos Ascencio-Garrido, 2023. "Parliamentary roll-call voting as a complex dynamical system: The case of Chile," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-20, April.
    • Handle: RePEc:plo:pone00:0281837
    DOI: 10.1371/journal.pone.0281837
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    References listed on IDEAS

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    1. Ross, Gordon J., 2015. "Parametric and Nonparametric Sequential Change Detection in R: The cpm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i03).
    2. Puccio, Elena & Pajala, Antti & Piilo, Jyrki & Tumminello, Michele, 2016. "Structure and evolution of a European Parliament via a network and correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 167-185.
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