Bayesian inference on proportional elections

PLoS One. 2015 Mar 18;10(3):e0116924. doi: 10.1371/journal.pone.0116924. eCollection 2015.

Abstract

Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brazil
  • Humans
  • Models, Theoretical*
  • Politics*

Grants and funding

The authors are grateful to the National Council for Scientific and Technological Development (CNPq) and to Decanato de Pesquisa e Pós-graduação (DPP) of the University of Brasilia (UNB) for the financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.