Fundraising and vote distribution: A non-equilibrium statistical approach

The number of votes correlates strongly with the money spent in a campaign, but the relation between the two is not straightforward. Among other factors, the output of a ballot depends on the number of candidates, voters, and available resources. Here, we develop a conceptual framework based on Shannon entropy maximization and Superstatistics to establish a relation between the distributions of money spent by candidates and their votes. By establishing such a relation, we provide a tool to predict the outcome of a ballot and to alert for possible misconduct either in the report of fundraising and spending of campaigns or on vote counting. As an example, we consider real data from two proportional elections with more than 6000 candidates each, where a detailed data verification is virtually impossible, and show that the number of potential misconducting candidates to audit can be reduced to less than ten.

[1]  Bernd Beber,et al.  What the Numbers Say: A Digit-Based Test for Election Fraud , 2012, Political Analysis.

[2]  C. Hafer,et al.  Consumption or Investment? On Motivations for Political Giving , 2007, The Journal of Politics.

[3]  E. Jaynes Information Theory and Statistical Mechanics , 1957 .

[4]  Fabrice Lehoucq,et al.  ELECTORAL FRAUD: Causes, Types, and Consequences , 2003 .

[5]  R. Michael Alvarez,et al.  Election Fraud: Detecting and Deterring Electoral Manipulation , 2009 .

[6]  Maxi San Miguel,et al.  Erratum: Is the Voter Model a Model for Voters? , 2014 .

[7]  Peter C. Ordeshook,et al.  Benford's Law and the Detection of Election Fraud , 2011, Political Analysis.

[8]  Gábor Vattay,et al.  Universal scaling laws in metro area election results , 2017, PloS one.

[9]  J. S. Andrade,et al.  Scaling behavior in a proportional voting process. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  Haroldo V. Ribeiro,et al.  Engagement in the electoral processes: scaling laws and the role of the political positions , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Maxi San Miguel,et al.  Is the Voter Model a model for voters? , 2013, Physical review letters.

[12]  Marija Mitrovic,et al.  Universality in voting behavior: an empirical analysis , 2012, Scientific Reports.

[13]  S. Fortunato,et al.  Scaling and universality in proportional elections. , 2006, Physical review letters.

[14]  S. Picoli,et al.  Scaling laws and universality in the choice of election candidates , 2011, 1109.4360.

[15]  J. S. Andrade,et al.  Brazilian elections: voting for a scaling democracy , 2003 .

[16]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[17]  General , 1970 .

[18]  J. Bouchaud,et al.  Election Turnout Statistics in Many Countries: Similarities, Differences, and a Diffusive Field Model for Decision-Making , 2012, PloS one.

[19]  G. Jacobson,et al.  The Effects of Campaign Spending in Congressional Elections , 1978, American Political Science Review.

[20]  D. Anderson,et al.  Algorithms for minimization without derivatives , 1974 .

[21]  André A Moreira,et al.  Competitive cluster growth in complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  C. Cameron,et al.  Elections And The Theory Of Campaign Contributions: A Survey And Critical Analysis , 1992 .

[23]  H. P. M. Melo,et al.  The price of a vote: Diseconomy in proportional elections , 2017, PloS one.

[24]  José S. Andrade,et al.  Tactical Voting in Plurality Elections , 2010, PloS one.

[25]  Ruben Enikolopov,et al.  Field experiment estimate of electoral fraud in Russian parliamentary elections , 2012, Proceedings of the National Academy of Sciences.

[26]  N. Crokidakis,et al.  What do vote distributions reveal? , 2014, ArXiv.

[27]  Stefan Thurner,et al.  Statistical detection of systematic election irregularities , 2012, Proceedings of the National Academy of Sciences.